conjoint analysis research definition

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What Is Conjoint Analysis & How Can You Use It?

• 18 Dec 2020

For a business to run effectively, its leadership needs a firm understanding of the value its products or services bring to consumers. This understanding allows for a more informed strategy across the board—from long-term planning to pricing and sales.

In today’s business environment, most products and services include multiple features and functions by default. So, how do businesses go about learning which ones their customers value most? Is it possible to assign a specific value to each feature a product offers?

This is where conjoint analysis becomes an essential tool.

Here’s an overview of conjoint analysis, why it’s important, and steps you can take to analyze your products or services.

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What Is Conjoint Analysis?

Conjoint analysis is a form of statistical analysis that firms use in market research to understand how customers value different components or features of their products or services. It’s based on the principle that any product can be broken down into a set of attributes that ultimately impact users’ perceived value of an item or service.

Conjoint analysis is typically conducted via a specialized survey that asks consumers to rank the importance of the specific features in question. Analyzing the results allows the firm to then assign a value to each one.

Learn about conjoint analysis in the video below, and subscribe to our YouTube channel for more explainer content!

Types of Conjoint Analysis

Conjoint analysis can take various forms. Some of the most common include:

• Choice-Based Conjoint (CBC) Analysis: This is one of the most common forms of conjoint analysis and is used to identify how a respondent values combinations of features.
• Adaptive Conjoint Analysis (ACA): This form of analysis customizes each respondent's survey experience based on their answers to early questions. It’s often leveraged in studies where several features or attributes are being evaluated to streamline the process and extract the most valuable insights from each respondent.
• Full-Profile Conjoint Analysis: This form of analysis presents the respondent with a series of full product descriptions and asks them to select the one they’d be most inclined to buy.
• MaxDiff Conjoint Analysis: This form of analysis presents multiple options to the respondent, which they’re asked to organize on a scale of “best” to “worst” (or “most likely to buy” to “least likely to buy”).

The type of conjoint analysis a company uses is determined by the goals driving its analysis (i.e., what does it hope to learn?) and, potentially, the type of product or service being evaluated. It’s possible to combine multiple conjoint analysis types into “hybrid models” to take advantage of the benefits of each.

What Is Conjoint Analysis Used For?

The insights a company gleans from conjoint analysis of its product features can be leveraged in several ways. Most often, conjoint analysis impacts pricing strategy, sales and marketing efforts, and research and development plans.

Conjoint Analysis in Pricing

Conjoint analysis works by asking users to directly compare different features to determine how they value each one. When a company understands how its customers value its products or services’ features, it can use the information to develop its pricing strategy.

For example, a software company hoping to take advantage of network effects to scale its business might pursue a “freemium” model wherein its users access its product at no charge. If the company determines through conjoint analysis that its users highly value one feature above the others, it might choose to place that feature behind a paywall.

As such, conjoint analysis is an excellent means of understanding what product attributes determine a customer’s willingness to pay . It’s a method of learning what features a customer is willing to pay for and whether they’d be willing to pay more.

Conjoint Analysis in Sales & Marketing

Conjoint analysis can inform more than just a company’s pricing strategy; it can also inform how it markets and sells its offerings. When a company knows which features its customers value most, it can lean into them in its advertisements, marketing copy, and promotions.

On the other hand, a company may find that its customers aren’t uniform in assigning value to different features. In such a case, conjoint analysis can be a powerful means of segmenting customers based on their interests and how they value features—allowing for more targeted communication.

For example, an online store selling chocolate may find through conjoint analysis that its customers primarily value two features: Quality and the fact that a portion of each sale goes toward funding environmental sustainability efforts. The company can then use that information to send different messaging and appeal to each segment's specific value.

Conjoint Analysis in Research & Development

Conjoint analysis can also inform a company’s research and development pipeline. The insights gleaned can help determine which new features are added to its products or services, along with whether there’s enough market demand for an entirely new product.

For example, consider a smartphone manufacturer that conducts a conjoint analysis and discovers its customers value larger screens over all other features. With this information, the company might logically conclude that the best use of its product development budget and resources would be to develop larger screens. If, however, future analyses reveal that customer value has shifted to a different feature—for example, audio quality—the company may use that information to pivot its product development plans.

Additionally, a company may use conjoint analysis to narrow down its product or service’s features. Returning to the smartphone example: There’s only so much space within a smartphone for components. How a phone manufacturer’s customers value different features can inform which components make it into the end product—and which are cut.

One example is Apple’s 2016 decision to remove the headphone jack from the iPhone to free up space for other components. It’s reasonable to assume this decision was reached after analysis revealed that customers valued other features above a headphone jack.

Leveraging Conjoint Analysis for Your Business

Conjoint analysis is an incredibly useful tool you can leverage at your company. By using it to understand which product or service features your customers value over others, you can make more informed decisions about pricing, product development, and sales and marketing activities.

Are you interested in learning more about how customers perceive and realize value from the products they buy, and how you can use that information to better inform your business? Explore Economics for Managers — one of our online strategy courses —and download our free e-book on how to formulate a successful business strategy.

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What is Conjoint Analysis?

Conjoint analysis is a popular method of product and pricing research that uncovers consumers’ preferences, which is useful when a company wants to:

• Select product features.
• Assess consumers’ sensitivity to price changes.
• Forecast its volumes and market share.
• Predict adoption of new products or services.

Conjoint analysis is frequently used across different industries for all types of products, such as consumer goods, electrical goods, life insurance plans, retirement housing, luxury goods, and air travel. It is applicable in various instances that centre around discovering what type of product consumers are likely to buy and what consumers value the most (and least) about a product. As such, it is a familiar tool for marketers, product managers, and pricing specialists.

Businesses of all sizes can benefit from conjoint analysis, including even local grocery stores and restaurants — and its scope is not just limited to consumer contexts, for example, charities can use conjoint analysis’ techniques to find out donor preferences, while HR departments can use it to build optimal compensation packages .

How does conjoint analysis work?

Conjoint analysis works by breaking a product or service down into its components ( attributes and levels ) and testing different combinations of these components to identify consumer preferences .

For example, consider a conjoint study on smartphones. The smartphone is broken down into four attributes which are each assigned different possible variations to create levels:

Each choice tasks then presents a respondent with different possible smartphones, each created by combining different levels for each attribute:

Going further than simply asking respondents what they like in a product, or what features they find most important, conjoint analysis employs a more realistic approach: asking each respondent to choose between potential product concepts (or alternatives) formed through the combination of attributes and levels. These combinations are carefully assembled into choice sets (or questions). Each respondent is typically presented with 8 to 12 questions . The process of assembling attributes and levels into product concepts and then into choice sets is called experimental design and requires extensive statistical and mathematical analysis (done automatically by Conjointly or manually by researchers).

Using survey results, it is possible to calculate a numerical value that measures how much each attribute and level influenced the respondent’s choices. Each of these values is called a “ preference score ” (AKA “partworth utility” or “utility score”). The below example shows preference scores for attributes and levels of a mobile phone plan.

Preference scores are used to build simulators that forecast market shares for a set of different products offered to the market. By using the simulator to model (i.e. simulate ) respondents’ decisions, we can identify the specific features and pricing that balance value to the customer with cost to the company and forecast potential demand in a competitive market situation. The below example shows how different data amounts in a mobile plan will affect a company’s market share.

Consider you are launching a new product and wish to address several research questions. Through the below example, we demonstrate how various outputs from your Conjointly survey report can be used to gain insights.

• It is also possible to perform clustering based on raw conjoint utilities .

Why do conjoint analysis with Conjointly?

Conjointly automates the often complicated experimental design process using state-of-the-art methodology. This gives you control over specific settings , such as the number of concepts per choice set and the number of choice sets per respondent when you set up a conjoint analysis experiment. Respondents then complete the choice tasks within the conjoint survey – this typically requires a few hundred responses but may vary depending on the complexity of the study.

Once we’ve gathered the recommended sample size of respondents, Conjointly produces a survey report which contains several in-depth outputs. The outputs of Brand Specific Conjoint , Generic Conjoint , and Brand-Price Trade-Off include estimates of respondents’ preferences, overall sample profile, segmentation and interactive simulations. Conjointly estimates and charts preference shares, revenue projections, and price elasticity using simulators.

There are many types/flavours of conjoint analysis , classified by response type, questioning approach, design type, and adaptivity of the design. All flavours of conjoint analysis have the same basics but not all are as effective as others. That’s why Conjointly offers two key conjoint designs, called generic and brand-specific, and uses the most tested, developed, and theoretically sound response type – choice-based conjoint analysis (CBC). CBC’s predictive power far surpasses its alternatives , such as SIMALTO and self-explicated conjoint, making it the ideal choice for your next experiment.

Don’t have a large marketing budget or the scope to conduct conjoint analysis? That’s OK: Conjointly does full conjoint analysis for you, affordably . Unlike desktop software tools, Conjointly does not require you to deep dive into the advanced methodology of conjoint analysis. Your business can rely on the full functionality of the software to deliver high-quality analysis and powerfully accurate results. Conjointly embodies an agile approach that puts you in control of the research process without the need.

Conjointly is made unique by the following characteristics:

We are the home of conjoint analysis. Conjointly offers complete set of outputs and features through an accessible interface.

Quick to set up. Setting up your experiment is fast and hassle-free with a simple wizard, which helps you choose appropriate settings and suggests your minimum sample size. You won’t need to customise or test any survey – the system does that for you. Conjointly can send participants invites on your behalf or generate a shareable link for you.

Easy on respondents. Experiment participants only need a few minutes to complete a survey and can answer questions with ease on their mobile phone, tablet, or computer.

Smart analytics done for you. Behind the scenes, Conjointly uses state-of-the-art analytics to crunch the numbers, and check validity of reporting. Outputs are ready for any application of conjoint analysis (pricing, feature selection, product testing, new market entry, cannibalisation analysis, etc.) in any industry (telecommunications, SaaS, FMCG, automotive, financial services, HR, etc.).

Our market research experts are always ready to support your studies. Schedule a consultation if you need any assistance.

What is the difference between conjoint and discrete choice experiments?

Conjoint analysis is a survey-based quantitative research technique of presenting respondents with several options (each described in terms of feature and price levels) and measuring their response to these options.

When the measured response is their choice between these options (rather than ranking or rating each of these options), it is called choice-based conjoint (which is the most commonly-used type of discrete choice experiments).

Discrete choice analysis is examination of datasets that contain choices made by people from among several alternatives. Commonly, we want to understand what drove people to make these choices. For example, how does weather affect people’s choice of eating out, ordering food delivery, or cooking at home. Discrete choice analysis can be done on historical data (e.g. sales data) or from experiments (including survey-based experiments).

Choice-based conjoint is an example of discrete choice experimentation.

History of conjoint analysis

Conjoint analysis has its roots in academic research from the 1960s and has been used commercially since the 1970s. In 1964, two mathematicians, Duncan Luce and John Tukey published a rather indigestible (by modern standards) article called ‘Simultaneous conjoint measurement: A new type of fundamental measurement’ . In abstract terms, they sketched the idea of “measuring the intrinsic goodness of certain characteristics of objects by measuring the goodness of an object as a whole”.

The article did not mention data collection, products, features, prices, or other elements that we associate with conjoint analysis today, but it spurred academic interest in the topic and perhaps gave rise to the name “conjoint”. It not only kick-started the topic but also set the tone for future developments in the area. Over time, it has become technical to the point of inaccessibility to most people, led by American academics with a strong emphasis on the statistical workings of survey research.

Green and Srinivasin (1978) agree that the theory of conjoint measurement was developed in Luce and Tukey’s paper but that “the first detailed, consumer-orientated" approach was Green and Rao’s (1971) ‘Conjoint Measurement for Quantifying Judgmental Data’ . In 1974, Professor Paul E. Green penned ‘On the Design of Choice Experiments Involving Multifactor Alternatives’ , cementing the impact of conjoint analysis in market research.

Over the next few decades, conjoint analysis became an increasingly popular method across the globe with notable studies in the 1980s and 90s highlighting its growing adoption and development during this time (Wittink & Cattin 1989; Wittink, Vriens, and Burhenne 1994 cited in Green, Kreiger & Wind 2001) .

Conjoint surveys are continuously developing on a range of software platforms, through which many different flavours of conjoint analysis can be enjoyed. Today, conjoint analysis thrives as a widespread tool built on a robust methodology and is used by market researchers daily as an indispensable tool for understanding consumer trade-offs.

Example outputs of Generic Conjoint on ice-cream

This is a simple conjoint analysis report for a Generic Conjoint test on ice-cream. You can also take this survey yourself . We tested three features:

• Flavour (Fudge, Vanilla, Strawberry, and Mango)
• Size (from 120g to 200g)
• Price (from $1.95 to $3.50)

We collected over 1,500 good quality responses in this test (even though this report would be robust enough with a hundred complete answers). It turns out that variation of price was a more important driver of people’s decision-making than differences in both flavour and size of the cone combined:

Unsurprisingly, people preferred larger and cheaper cones. Fudge and vanilla were the two top flavours:

But when we look at confidence intervals, we notice that we are much less certain about average preferences for flavours than for size or price:

It is probably because if we simulate preference shares for four concepts with varied flavours but fixed price and size, we observe that the distribution of people who pick different options is not extremely skewed towards one flavour:

But when we do simulation analysis with different price points, we clearly see that more people prefer to pay a lower price. Even though some still stick with a higher price, probably due to price-quality inference.

Another useful output of the study is marginal willingness to pay , which shows the equivalent amount of money for upgrade from the less preferred to the more preferred features:

If you want to pick the topmost preferred combination of product features, you can take a look at the following ranking as well:

It looks like a large dollop of modestly-priced Frosty Vanilla is the winner today.

A simple conjoint analysis example in Excel

To further your understanding, you can download a conjoint analysis example in Excel , also available on Google Sheets (which you can copy to edit). This example covers:

• Inputs for a conjoint study
• Questions presented to respondents
• Calculations of preference scores (relative preferences and importance scores of attributes)

This example is limited to:

• Ten choice-based responses (in real conjoint tests, we collect ~12 choices from 100 to 2,000 respondents);
• Four attributes with two levels each (in real conjoint tests, we can have up to a dozen attributes and up to several dozen levels);
• A multiple linear regression (in real conjoint tests, we use hierarchical Bayesian multinomial logit );
• A fractional factorial design .

The best way to learn more about conjoint analysis is to set up your own study, which you can do when you sign up . You can also read about:

• Alternatives to conjoint (such as MaxDiff and Claims Test )
• Common mistakes and practical tips for setting up conjoint studies
• Check out our webinar on Conjoint Analysis 101

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Home Market Research

Conjoint Analysis: Definition, Example, Types, and Model

Have you ever bought a house? As one of the most complex purchase decisions you can make, you must consider many preferences. Everything from the location and price to interest rate and quality of local schools can play a factor in your home-buying decision. You can use conjoint analysis to make data-driven decisions that will help you meet customer needs and develop your organization.

LEARN ABOUT: Level of Analysis

Less complicated purchases feature a similar process of choosing a good or service that meets your needs. You just may not be aware you’re making those decisions.

LEARN ABOUT: Behavioral Research

Subconsciously, one person might be more price-sensitive while another is more feature-focused. Understanding which elements consumers consider essential and trivial is the core purpose of conjoint analysis.

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What is conjoint analysis?

Why is it important for researchers, why use conjoint analysis in surveys, when to use it, how to use conjoint analysis, types of conjoint analysis, conjoint analysis: key terms, when is a good time to run a discrete choice-based conjoint study, advantages of conjoint analysis, conjoint analysis example, conjoint algorithm: how it is works, level-up conjoint analysis insights, conjoint analysis marketing example, how to conduct conjoint analysis using questionpro.

Conjoint analysis is defined as a survey-based advanced market research analysis method that attempts to understand how people make complex choices. We make choices that require trade-offs every day — so often that we may not even realize it. Even simple decisions like choosing a laundry detergent or deciding to book a flight are mental conjoint studies that contain multiple elements that lead us to our choice.

Conjoint analysis is one of the most effective models for extracting consumer preferences during the purchasing process . This data is then turned into a quantitative measurement using statistical analysis. It evaluates products or services in a way no other method can.

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Researchers consider conjoint analysis as the best survey method for determining customer values. It consists of creating, distributing, and analyzing surveys among customers to model their purchasing decision based on response analysis.

QuestionPro can automatically compute and analyze numerical values to explain consumer behavior . Our software analyzes responses to see how much value is placed on price, features, geographic location, and other factors. The software then correlates this data to consumer profiles. A software-driven regression analysis of data obtained from real customers makes an accurate report instead of a hypothesis. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders.

LEARN ABOUT: Consumer Surveys

Reliable, accurate data gives your business the best chance to produce a product or service that meets all your customer’s needs and wants.

Currently, choice-based conjoint analysis is the most popular form of conjoint. Participants are shown a series of options and asked to select the one they would most likely buy. Other types of conjoint include asking participants to rate or rank products. Choosing a product to buy usually yields more accurate results than ranking systems.

We recommend you take a look at this free resource: Conjoint analysis survey template

The survey question shows each participant several choices of products or features. The answers they give allow our software to work out the underlying values. For example, the program can work out its preferred size and how much it would pay for its favorite brand. Once we have the choice data, there is a range of analytic options. The critical tools for analysis include What-if modeling, forecasting, segmentation, and applying cost-benefit analysis.

LEARN ABOUT:   Statistical Analysis Methods

Traditional rating surveys can’t place a value on the different attributes that make up a product. On the other hand, conjoint analysis can sift through respondents’ choices to determine the reasoning for those choices. Analyzing the data gives you the ability to peek into your target audience’s minds and see what they value most in goods or services and acts as a market simulator.

Many businesses shy away from the conjoint analysis because of its seemingly sophisticated design and methodology. But the truth is, you can use this method efficiently, thanks to user-friendly survey software like QuestionPro. Here is a breakdown of conjoint in simple terms, along with a conjoint analysis marketing example.

Over the past 50 years, Conjoint analysis has evolved into a method that market researchers and statisticians implement to predict the kinds of decisions consumers will make about products by using questions in a survey.

The central idea is that consumers evaluate different characteristics of a product and decide which are more relevant to them for any purchase decision. An online conjoint survey’s primary aim is to set distinct values to the alternatives that the buyers may consider when making a purchase decision. Equipped with this knowledge, marketers can target the features of products or services that are highly important and design messages more likely to strike a chord with target buyers.

You can also find best alternatives of Conjoint.ly for your business.

The discrete choice conjoint analysis presents a set of possible decisions to consumers via a survey and asks them to decide which one they would pick. Each concept is composed of a set of attributes (e.g., color, size, price) detailed by a set of levels.

GATHER RESEARCH INSIGHTS

Conjoint models predict respondent preference. For instance, we could have a conjoint study on laptops. The laptop can come in three colors (white, silver, and gold), three screen sizes (11”, 13”, and 15”), and three prices ($200, $400, and $600). This would give 3 x 3 x 3 possible product combinations. In this example, there are three attributes (color, size, and price) with three levels per attribute.

A set of concepts or tasks, based on the defined attributes, are presented to respondents. Respondents make choices as to which product they would purchase in real life. It is important to note that there are a lot of variations of conjoint techniques. QuestionPro’s conjoint analysis software uses choice-based analysis, which most accurately simulates the purchase process of consumers.

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There are two main types of conjoint analysis: Choice-based Conjoint (CBC) Analysis and Adaptive Conjoint Analysis (ACA).

Discrete choice-based conjoint (CBC) analysis:

This type of conjoint study is the most popular because it asks consumers to imitate the real market’s purchasing behavior: which products they would choose, given specific criteria on price and features.

For example, each product or service has a specific set of fictional characters. Some of these characters might be similar to each other or will differ. For instance, you can present your respondents with the following choice:

The devices are almost identical, but device 2 has triple cameras with better configuration, and Device 1 has a higher battery power than Device 2. You would know how vital the trade-off between the number of cameras and battery capacity is by analyzing the responses.

Using the discrete choice model, QuestionPro offers three design types to conduct conjoint analysis:

• Random: This design displays random samples of the possible attributes. For each respondent, the survey software uniquely combines the characteristics. You can run a conjoint concept simulator to know what the choices that the tool will present when you deploy your survey.
• D-Optimal: A flawlessly designed experiment helps researchers estimate parameters without minimum variance and bias. A D-optimal design runs a few tests to investigate or optimize the subject under study. The algorithm helps to create a design that is optimal for the sample size and tasks per respondent.
• Import design: You can also import designs in SPSS format. For example, QuestionPro lets you import fractional factorial orthogonal designs to make use of in surveys.

Adaptive conjoint analysis (ACA):

Researchers use this type of conjoint analysis often in scenarios where the number of attributes/features exceeds what can be done in a choice-based scenario. ACA is great for product design and product segmentation research but not for determining the ideal price.

For example, the adaptive conjoint analysis is a graded-pair comparison task wherein the survey respondents are asked to assess their relative preferences between a set of attributes, and each pair is then evaluated on a predefined point scale.

QuestionPro uses CBC, or Discrete Choice Conjoint Analysis, a great option if the price is one of the most critical factors for you or your customers. The method’s key benefit is that it provides a picture of the market’s willingness to make tradeoffs between various features. The result is an answer to what constitutes an “ideal” product or service.

It is a statistical analysis plan used in market research to gain a better understanding of how people make complex decisions. The following are some key terms of it:

• Attributes (Features): The product features are evaluated by the analysis. Examples of characteristics of Laptops: Brand, Size, Color, and Battery Life.
• Levels:  The specifications of each attribute. Examples of standards for Laptops include Brands: Samsung, Dell, Apple, and Asus.
• Task: The number of times the respondent must make a choice. The example shows the first of the five functions as indicated by “Step 1 of 5.” 5.”
• Concept or Profile : The hypothetical product or offering. This is a set of attributes with different levels that are displayed at each task count. There are usually at least two to choose from.
• Relative importance : “attribute importance,” which depicts which of the various attributes of a product/service is more or less important when making a purchasing decision. Example of Laptop Relative Importance: Brand 35%, Price 30%, Size 15%, Battery Life 15%, and Color 5%.
• Part-Worths/Utility values : Part-Worths, or utility values, is how much weight an attribute level carries with a respondent. The individual factors that lead to a product’s overall value to consumers are part-worths. Example part-worths for Laptops Brands: Samsung – 0.11, Dell 0.10, Apple 0.17, and Asus -0.16.
• Profiles : Discover the ultimate product with the highest utility value. At a glance, QuestionPro lets you compare all the possible combinations of product profiles ranked by utility value to build the product or service that the market wants.
• Market share simulation : One of the most unique and fascinating aspects of conjoint analysis is the conjoint simulator. This gives you the ability to “predict” the consumer’s choice for new products and concepts that may not exist. Measure the gain or loss in market share based on changes to existing products in the given market.
• Brand Premium : How much more will help a customer pay for a Samsung versus an LG television? Assigning price as an attribute and tying that to a brand attribute returns a model for a $ per utility distribution. This is leveraged to compute the actual dollar amount relative to any characteristic. When the analysis is done relative to the brand, so you get to put a price on your brand.
• Price elasticity and demand curve : Price elasticity relates to the aggregate demand for a product and the demand curve’s shape. We calculate it by plotting the demand (frequency count/total response) at different price levels. ADD_THIS_TEXT

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We’re asked this question a lot. So much so that we’ve coined the term Conjoint O’ Clock. If you find yourself needing to get into your customers’ minds to understand why they buy, ask yourself what you hope to get from your insights. It’s time for Conjoint O’Clock if you are trying to:

• Launch a new product or service in the market.
• Repackage existing products or services to the market.
• Understand your customers and what they value in your products.
• Gain actionable insight to increase your brand’s competitive edge .
• Place a price on your brand versus competing brands.
• Revamp your pricing structure.

LEARN ABOUT: Pricing Research

There are multiple advantages to using conjoint analysis in your surveys:

• It helps researchers estimate the tradeoffs that consumers make on a psychological level when they evaluate numerous attributes simultaneously.
• Researchers can measure consumer preferences at an individual level.
• It gives researchers insights into real or hidden drivers that may not be too apparent.
• Conjoint analysis can study the consumers and attributes deeper and create a needs-based segmentation.

For example, assume a scenario where a product marketer needs to measure individual product features’ impact on the estimated market share or sales revenue.

In this conjoint study example, we’ll assume the product is a mobile phone. The competitors are Apple, Samsung, and Google. The organization needs to understand how different customers value attributes, such as brand, price, screen size, and screen resolution. Armed with this information, they can create their product range to match consumer preferences.

Conjoint analysis assigns values to these product attributes and levels by creating realistic choices and asking people to evaluate them.

LEARN ABOUT: Average Order Value

It enables businesses to mathematically analyze consumer or client behavior and make decisions based on real insights from customer data. This allows them to develop better business strategies that provide a competitive edge. To fulfill customer wishes profitably requires companies to fully understand which aspects of their product and service are most valued.

We use a logic model coupled with a Nelder-Mead Simplex algorithm. It helps to calculate the utility values or part-worths. This algorithm’s benefit allows QuestionPro to offer a cohesive and comprehensive survey experience all within one platform.

We understand that most businesses don’t need the intricate details of our mathematical analysis. However, we want to provide you with the transparency you need to use conjoint survey results. Have confidence in your results by reviewing the algorithm below.

• Let there be R respondents, with individuals r = 1 … R
• Let each respondent see T tasks, with t = 1 … T
• Let each task t have C concepts, with c = 1 … C
• If we have A attributes, a = 1 to A, with each attribute having La levels, l = 1 to La, then the part-worth for a particular attribute/level is w’(a,l). In this exercise, we will be solving this (jagged array) of part worths.
• We can simplify this to a one-dimensional array w(s), where the elements are {w′(1, 1), w′(1, 2)…w′(1, L1), w′(2, 1)…w′(A, LA)} with w having S elements.
• A specific concept x can be shown as a one-dimensional array x(s), where x(s)=1 if the specific attribute is available, and 0 otherwise.
• Let X rtc  represent the specific concept of the c th  concept in the t th  task for the r th respondent. Thus, the experiment design is represented by the four-dimensional matrix X with size RxTxCxS.
• If respondent r chooses concept c in task t then let Y rtc =1; otherwise 0.
• The value Ux of a definite idea is the total of the part-worths for those elements available in the conception, i.e. the scalar product of x and w.

Multinomial logit model

For a simple choice between two concepts, with utilities U1 and U2, the multinomial logit (MNL) model predicts that concept 1 will be chosen.

Modeled Choice Probability

Let the choice probability (using MNL model) of choosing the cth concept in the tth task for the r th respondent be:

Log-Likelihood Measure

Solving for Part-Worths using Maximum Likelihood

We solve for the part-worth vector by finding the vector w that gives the maximum value for LL. Note that we are solving for S variables.

• This is a multi-dimensional, nonlinear continuous maximization research problem , and it is essential to have a standard solver library. We use the Nelder-Mead Simplex Algorithm.
• The Log-Likelihood function should be implemented as a function LL(w, Y, X) and then optimized to find the vector w that gives us a maximum. The responses Y, and the design.

X is specified and constant for a specific development. The starting values for w can be set to the origin 0. The final part-worth values, w, are re-scaled so that the part-worths for any attribute have a mean of zero. This is done by subtracting the mean of the part-worths for all levels of each quality.

Although conjoint analysis requires more involvement in survey design and analysis, the additional planning effort is often worth it. With a few extra steps, you get an authentic look into your most significant customer preferences when choosing a product.

Price, for example, is vital to most folks shopping for a laptop. But how much more is the majority willing to pay for longer battery life for their laptop if it means a heavier and bulkier design? How much less in value is a smaller screen size compared to a slightly larger one? Using conjoint surveys, you’ll discover these details before making a considerable investment in product development.

Conjoint is just a piece of the insights pie. Capture the full story with a cohesive pricing, consumer preference, branding, or go-to-market strategy using other question types and delivery methodologies to stretch the project to its full potential. With QuestionPro, you can build and deliver comprehensive surveys that combine conjoint analysis results with insights from additional questions or custom profiling information included in the survey.

Gather research insights

Click on the Add New Question link and select the Conjoint (Discrete Choice) Option from under Advanced Question Types. This will open the wizard-based conjoint question template to create tasks by entering attributes (features) and levels for each of the features.

For example, an organization produces televisions and they are a competitor of Samsung, LG, or Vizio. The organization needs to understand how different customers value specific attributes such as the size, brand, and price of a television. Armed with this information, they can create their very own product range and offering that meets a market need and generates revenue.

Step 2: Enter the features and levels.

Enter the features and levels. Set up the task counts and concepts per task and assign feature types: Price, Brand, or Other. Using television brands as an example, consider the following:

• Features for televisions: Price, Size, Brand.
• Price:$800, $1,200, $1,500
• Size: 36”, 45”, 52”
• Brand: Sony, LG, Vizio

Step 3: Select Design Type to either of the three design types: Random, D-Optimal, and Import.

Step 4: Add additional setting options, including fixed tasks and prohibited concepts.

Step 5: Preview, review text data, and distribute the survey.

In this example, the survey would look like this:

Where can I view Reports for the conjoint questions?

Step 1:  Go To  Login »  Surveys »  Analytics »  Choice Modelling »  Conjoint Analysis

Step 2: Here, you can view the online reports.

Step 3: You can download the data in Excel/CSV or HTML format.

The QuestionPro conjoint analysis offering includes the following tools:

• Conjoint Task Creation Wizard: Wizard-based interface to create Conjoint Tasks based on merely entering features(attributes), like price and levels, like $100 or $200, for each feature.
• Conjoint Design Parameters: Tweak your design by choosing the number of tasks, the number of profiles per task, and the “Not-Applicable” option.
• Utility Calculation: Automatically calculates utilities.
• Relative Importance: Automatically calculates the relative importance of attributes (based on utilities).
• Cross/Segmentation and Filtering: Filter the data based on criteria and then run Relative Importance calculations.

LEARN ABOUT: 12 Best Tools for Researchers

Conjoint analysis is an effective market research technique that helps businesses better understand their customer’s preferences and make educated decisions about product creation, pricing, and marketing strategies.

LEARN ABOUT: Market research vs marketing research

The conjoint analysis provides significant insights into how customers assess different aspects when making purchase decisions by breaking down complex purchasing decisions into smaller components and examining them systematically. 

There are several types of conjoint analysis models accessible, each with its own set of advantages and disadvantages. Choosing the best model is determined by the study objectives and the specific characteristics of the market under consideration.

Conjoint analysis is a valuable tool for any company wanting to obtain a better knowledge of its customers and keep ahead of the competition in today’s ever-changing market. If you are thinking about conducting conjoint analysis, QuestionPro is there for you. 

QuestionPro provides a comprehensive set of features and tools to assist businesses in conducting conjoint analysis efficiently and effectively, making it a valuable tool for market research professionals. Contact QuestionPro right away!

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What is a conjoint analysis conjoint types & when to use them.

11 min read Conjoint analysis is a popular market research approach for measuring the value that consumers place on individual and packages of features of a product.

Conjoint analysis explained

Conjoint analysis can be defined as a popular survey-based statistical technique used in market research. It is the optimal approach for measuring the value that consumers place on features of a product or service. This commonly used approach combines real-life scenarios and statistical techniques with the modeling of actual market decisions.

Product testing and employee benefits packages are examples of where conjoint analysis is commonly used. Conjoint surveys will show respondents a series of packages where feature variables are different to better understand which features drive purchase decisions.

Note: For an in-depth guide to conjoint analysis, download our free eBook:   12 Business Decisions you can Optimize with Conjoint Analysis

Menu-based conjoint analysis

Menu-based conjoint analysis is an analysis technique that is fast gaining momentum in the marketing world. One reason is that menu-based conjoint analysis allows each respondent to package their own product or service.

Conjoint studies can help you determine pricing, product features, product configurations, bundling packages, or all of the above. Conjoint is helpful because it simulates real-world buying situations that ask respondents to trade one option for another.

For example, in a survey, the respondent is shown a list of features with associated prices. The respondent then chooses what they want in their ideal product while keeping price as a factor in their decision. For the person conducting the market research , key information can be gained by analyzing what was selected and what was left out. If feature A for $100 was included in the menu question but feature B for $100 was not, it can be assumed that this respondent prefers feature A over feature B.

The outcome of menu-based conjoint analysis is that we can identify the trade-offs consumers are willing to make. We can discover trends indicating must-have features versus luxury features.

Add in the fact that menu-based conjoint analysis is a more engaging and interactive process for the survey taker, and one can see why menu-based conjoint analysis is becoming an increasingly popular way to evaluate the utility of features.

The advanced functionality of Qualtrics allows for the perfect conjoint survey – built with the exact look and feel needed to provide a reliable, easy to understand experience for the respondent. This means better quality data for you.

  There are numerous conjoint methodologies available from Qualtrics.

• Full-Profile Conjoint Analysis
• Choice-Based/Discrete-Choice Conjoint Analysis
• Adaptive Conjoint Analysis
• Max-Diff Conjoint Analysis

To provide a sense of these options, the following discussion provides an overview of conjoint analysis methods.

Two-attribute tradeoff analysis

Perhaps the earliest conjoint data collection method involved presented a series of attribute-by-attribute (two attributes at a time) tradeoff tables where respondents ranked their preferences for the different combinations of the attribute levels. For example, if two attributes each had three levels, the table would have nine cells and the respondents would rank their tradeoff preferences from 1 to 9.

The two-factor-at-a-time approach makes few cognitive demands of the respondent and is simple to follow but it is both time-consuming and tedious. Moreover, respondents often lose their place in the table or develop some stylized pattern just to get the job done. Most importantly, however, the task is unrealistic in that real alternatives do not present themselves for evaluation two attributes at a time.

Full-profile conjoint analysis

Full-profile conjoint analysis takes the approach of displaying a large number of full product descriptions to the respondent. The evaluation of these packages yields large amounts of information for each customer/respondent. Full-profile conjoint analysis has been a popular approach to measure attribute utilities. In the full-profile conjoint task, different product descriptions (or even different actual products) are developed and presented to the respondent for acceptability or preference evaluations.

Each product profile represents a part of a fractional factorial experimental design that evenly matches the occurrence of each attribute with all other attributes. By controlling the attribute pairings, the researcher can correlate attributes with profile preferences and estimate the respondent’s utility for each level of each attribute tested. In the rating task, the respondent gives their preference or likelihood of purchase. While many features and levels may be studied, this type of conjoint is best used where a moderate number of profiles are presented, thereby minimizing respondent fatigue. The advanced functionality of Qualtrics employs experimental designs to reduce the number of evaluation requests within the survey. The output and analysis accumulated from full-profile conjoint surveys is similar to that of other conjoint models.

Adaptive conjoint analysis

Adaptive conjoint analysis varies the choice sets presented to respondents based on their preference. This adaption targets the respondent’s most preferred feature and levels, thereby making the conjoint exercise more efficient, wasting no questions on levels with little or no appeal. Every package shown is more competitive and will yield ‘smarter’ data.

Adaptive conjoint analysis is often more engaging to the survey-taker and thus can produce more relevant data. It reduces the survey length without diminishing the power of the conjoint analysis metrics or simulations. There are multiple ways to adapt the conjoint scenarios to the respondent. Most commonly the design is based on the most important feature levels. As each package is presented for evaluation, the survey accounts for the choice and then makes the next question more efficient. A combination of full profile and feature evaluation methods can be utilized and is referred to as Hybrid Conjoint Analysis.

Choice-based conjoint

The Choice-based conjoint analysis (CBC) (also known as discrete-choice conjoint analysis) is the most common form of conjoint analysis. Choice-based conjoint requires the respondent to choose their most preferred full-profile concept. This choice is made repeatedly from sets of 3–5 full profile concepts.

This choice activity is thought to simulate an actual buying situation, thereby mimicking actual shopping behavior. The importance and preference for the attribute features and levels can be mathematically deduced from the trade-offs made when selecting one (or none) of the available choices. Choice-based conjoint designs are contingent on the number of features and levels. Often, that number is large and an experimental design is implemented to avoid respondent fatigue. Qualtrics provides extreme flexibility in utilizing experimental designs within the conjoint survey.

The output of a Choice-based conjoint analysis provides excellent estimates of the importance of the features, especially in regards to pricing. Results can estimate the value of each level and the combinations that make up optimal products. Simulators report the preference and value of a selected package and the expected choice share (surrogate for market share).

Self-explicated conjoint analysis

Self-explicated conjoint analysis offers a simple but surprisingly robust approach that is easy to implement and does not require the development of full-profile concepts. Self-explicated conjoint analysis is a hybrid approach that focuses on the evaluation of various attributes of a product. This conjoint analysis model asks explicitly about the preference for each feature level rather than the preference for a bundle of features.

Although the approach is different, the outcome is still the same in that it produces high-quality estimates of preference utilities.

• First, like ACA, factors and levels are presented to respondents for elimination if they are not acceptable in products under any condition
• For each feature, the respondent selects the levels they most and least prefer
• Next, the remaining levels of each feature are rated in relation to the most preferred and least preferred levels
• Finally, we measure how important the overall feature is in their preference. The relative importance of the most preferred level of each attribute is measured using a constant sum scale (allocate 100 points between the most desirable levels of each attribute).
• The attribute level desirability scores are then weighted by the attribute importance to provide utility values for each attribute level.

Self-explicated conjoint analysis does not require the statistical analysis or the heuristic logic required in many other conjoint approaches. This approach has been shown to provide results equal or superior to full-profile approaches, and places fewer demands on the respondent. There are some limitations to self-explicated conjoint analysis, including an inability to trade off price with other attribute bundles. In this situation, the respondent always prefers the lowest price, and other conjoint analysis models are more appropriate.

Max-diff conjoint analysis

Max-Diff conjoint analysis presents an assortment of packages to be selected under best/most preferred and worst/least preferred scenarios. Respondents can quickly indicate the best and worst items in a list, but often struggle to decipher their feelings for the ‘middle ground’. Max-Diff is often an easier task to undertake because consumers are well trained at making comparative judgments.

Max-Diff conjoint analysis is an ideal methodology when the decision task is to evaluate product choice. An experimental design is employed to balance and properly represent the sets of items. There are several approaches that can be taken with analyzing Max-Diff studies including: Hierarchical Bayes conjoint modeling to derive utility score estimations, best/worst counting analysis and TURF analysis.

Hierarchical Bayes analysis (HB)

Hierarchical Bayes Analysis (HB) is similarly used to estimate attribute level utilities from choice data. HB is particularly useful in situations where the data collection task is so large that the respondent cannot reasonably provide preference evaluations for all attribute levels. As part of the procedure to estimate attribute level utilities for each individual, hierarchical Bayes focuses individual respondent measurement on highly variable attributes and uses the sample’s attribute level averages when attribute-level variability is smaller. This approach again allows more attributes and levels to be estimated with smaller amounts of data collected from each individual respondent.

Conjoint is a highly effective analysis technique

Conjoint analysis methodology has withstood intense scrutiny from both academics and professional researchers for more than 30 years. It is widely used in consumer products, durable goods, pharmaceutical, transportation, and service industries, and ought to be a staple in your research toolkit.

eBook: 12 Business Decisions You Can Optimize with Conjoint

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Conjoint Analysis: A Research Method to Study Patients’ Preferences and Personalize Care

Basem al-omari.

1 Department of Epidemiology and Population Health, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates; [email protected] (J.F.); [email protected] (M.E.)

2 KU Research and Data Intelligence Support Center (RDISC) AW 8474000331, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates

Joviana Farhat

Mai ershaid, associated data.

Not applicable.

This article aims to describe the conjoint analysis (CA) method and its application in healthcare settings, and to provide researchers with a brief guide to conduct a conjoint study. CA is a method for eliciting patients’ preferences that offers choices similar to those in the real world and allows researchers to quantify these preferences. To identify literature related to conjoint analysis, a comprehensive search of PubMed (MEDLINE), EMBASE, Web of Science, and Google Scholar was conducted without language or date restrictions. To identify the trend of publications and citations in conjoint analysis, an online search of all databases indexed in the Web of Science Core Collection was conducted on the 8th of December 2021 without time restriction. Searching key terms covered a wide range of synonyms related to conjoint analysis. The search field was limited to the title, and no language or date limitations were applied. The number of published documents related to CA was nearly 900 during the year 2021 and the total number of citations for CA documents was approximately 20,000 citations, which certainly shows that the popularity of CA is increasing, especially in the healthcare sciences services discipline, which is in the top five fields publishing CA documents. However, there are some limitations regarding the appropriate sample size, quality assessment tool, and external validity of CA.

1. Introduction

The popularity of conjoint analysis (CA) in health outcomes research has been increasing in recent years [ 1 , 2 ]. Yet, the untraditional concept of this research method is still unclear for many healthcare researchers and clinicians in terms of the design complexity and the absence of confirmed sample size [ 1 , 3 , 4 ]. Throughout clinical practice, healthcare professionals have been driving their efforts towards a patient-oriented profession to improve patient adherence to medications, prognosis, and quality of life [ 5 ]. Over the years, approaches that are referred to as stated and revealed preference methods have been executed to assess patients’ preferences in relation to drug pharmacodynamics, pharmacokinetics, and financial characteristics [ 5 , 6 ]. The stated preferences method relies on what people state while evaluating alternative hypothetical situations (hypothetical decision) [ 7 , 8 ]. Contrarily, the revealed preferences method relies on the observations of the actual choices made by people to measure preferences (actual decision) [ 7 , 8 , 9 ]. Since stated choice does not always reveal the actual preference [ 8 ], behavioral scientists developed alternative techniques that involve studying choice behavior rather than just stated choices [ 10 ]. Therefore, the application of alternative methods in clinical practice is linked to the patients’ own perspective of selecting the best cost-effective treatment, while considering their social and psychological situation instead of relying mostly on disease symptoms [ 6 , 11 , 12 ].

One of the main methods of examining patients’ preferences is the CA, which was developed to scrutinize preferences within the decision-making process [ 4 , 13 ]. CA is a stated preference method that measures how respondents state that they will react in a certain situation [ 14 , 15 ]. Conjoint measurement was first developed in the 1960s by the American mathematical psychologist Duncan Luce and the statistician John Tukey [ 16 ]. In the early 1970s, Green and Rao introduced conjoint measurement to marketing research in order to understand and predict buyer behavior [ 17 ] and thereafter it was most widely used in marketing research [ 18 ]. Although the CA technique was developed in the 1960s, it was not until the 1990s that it was used to elicit patients’ views in the healthcare field [ 19 ]. Since then, its popularity and social impact have been growing gradually through its frequent usage in health services rating based research studies [ 1 , 3 ].

The term CA generally belongs to the description of the variety of quantitative methods used to analyze preferences [ 19 , 20 ]. The denomination “conjoint” refers to the idea that several factors can be “considered jointly” [ 21 ]. Therefore, CA permits people to choose between different hypothetical products or treatments scenarios rather than evaluating their characteristics separately. CA presents people with ideas that closely resemble the decisions made in real life when choosing between alternatives [ 22 ]. For example, if a patient is requested to select the preferred surgical procedure from several alternatives for the treatment of kidney stones, they may consider a specific procedure superior to others. CA elicits patients’ preferences for the selected surgical procedure by evaluating multiple factors associated with each offered procedure. These factors may include adverse events, associated benefits, recovery time, and cost.

When people are making treatment decisions, they base their choice on several characteristics of this treatment. CA assumes that each one of these characteristics has a specific importance to people and they are making trade-offs between these characteristics [ 4 , 23 ]. It is also suggested that people give well-ordered preferences when evaluating options together rather than in isolation [ 17 ]. Therefore, unlike traditional questionnaires, CA poses several hypothetical scenarios and asks patients to rate, rank, or choose their preferred scenario [ 24 ]. Accordingly, the importance of CA is highlighted by being a multivariate technique used specifically to understand how respondents develop preferences for products or services [ 25 ]. In most cases, individuals could make up their minds about a particular treatment characteristic, but they might change their preference when this characteristic is combined to form a treatment scenario. For example, a patient’s decision to choose between different pain-relieving medications could be based on several characteristics of these medications, which may include a pain-relieving effect, frequency of administration, and side effects. This patient may state that she/he would like the medication that provides maximum pain relief, is taken once a day, and has no side effects. Yet, she/he may change their decision when the reality states that the medications with maximum benefits hold risks of side effects and may have to be taken more than once a day. In this situation, the patient must trade off benefits, frequency, and side effects and make some compromises. Hence, CA suggests that presenting patients with several scenarios in a conjoint task could resemble the decision made when selecting medication in real life.

CA methods use three main approaches and tools to elicit well-ordered preferences: ranking, rating, or discrete choices [ 19 ]. When conducting a CA questionnaire, researchers can either utilize a pre-developed questionnaire design or develop their own customized one. For example, Ratcliffe and colleagues developed their conjoint questionnaire using a computer software package to produce a fractional factorial design [ 26 ]. Others built narratives describing different options and asked participants to rate these options [ 27 ] or created hypothetical scenarios and asked participants to choose between them [ 22 ]. In recent years, pre-developed designs such as adaptive conjoint analysis (ACA) or adaptive choice-based conjoint (ACBC) by “Sawtooth software” (a provider of CA software packages) are becoming popular [ 4 , 28 , 29 ]. These designs provide researchers with questionnaire templates and an analysis platform. Consequently, researchers can customize the template to suit their requirements and can build up the questionnaire using the attributes and levels specific to their study. Then, a built-in statistical software such as hierarchical basin (HB) can be used to analyze the data.

This article aims to provide an overview of the CA method and analyze the growth of its application over the past 70 years. It also narratively discusses the literature of the CA method’s process and validity, its use in healthcare settings, and its strengths and limitations.

A comprehensive literature search was conducted. Following Gasparyan and colleagues’ recommendations [ 30 ], PubMed (MEDLINE), EMBASE, Web of Science, and Google Scholar were electronically searched without language or date restrictions. Keywords related to “conjoint analysis”, “discrete choice”, “choice experiment”, “rating conjoint”, and “ranking conjoint” were used to search the literature. Additionally, the lead author has significant experience in the field, and the opinions expressed in this article are also based on personal experience of writing, editing, and commenting on reviewed articles.

The Web of Science Core Collection (WoSCC) was utilized to identify the trend of publications and citations over the past 70 years. WoSCC is a database providing access to billions of cited references dating back to 1900 in the areas of life sciences, social sciences, arts, and humanities [ 31 ], and is an emerging source of citation index [ 32 , 33 , 34 , 35 ]. Bibliometric studies, which are used to systematize and summarize the growing body of publications [ 36 ] and focus on a topic’s popularity at a given point in time [ 37 ], mainly use WoSCC. Therefore, an online search was conducted utilizing all databases indexed in the WoSCC to identify the publications and citations trend in CA. The retrieved database was searched on the 8th of December 2021. The database was accessed through the electronic library portal of Khalifa University, United Arab Emirates. The Boolean search query method was applied. The searching key terms covered a wide range of synonyms which included “conjoint analysis” OR “conjoint measurement” OR “conjoint studies” OR “conjoint choice experiment” OR “discrete choice conjoint experiment” OR “discrete choice experiment” OR “pairwise choices” OR “Best-Worst Scaling” OR “Best Worst Scaling” OR “MaxDiff Scaling” OR “Maximum Difference Scaling” OR “ranking conjoint” OR “rating conjoint” OR “adaptive conjoint analysis” OR “adaptive choice based conjoint” OR “choice based analysis” OR “full profile conjoint” OR “choice based conjoint” OR “choice set” OR “relative preference weight” OR “hypothetical scenario” OR “stated preference”. The search field was limited to the title, and no language or date limitations were applied.

3. Conjoint Analysis Trend over the Past 70 Years

The WoSCC search identified a total of 9614 documents related to CA, which were published between 1950 and the 8th of December 2021. The result of the search demonstrated a significant increase in the production and citation of published papers related to CA over the years to reach nearly 900 documents and 20,000 citations in 2020 and 2021 (see Figure 1 ). The gradual increase in citations and research production indicates the expanded popularity of CA methods. Furthermore, it is an indication of the improvement of the reporting of conjoint experiments that are conducted for commercial purposes. Between 1981 and 1985, it was estimated that approximately 400 commercial conjoint analysis applications were carried out each year [ 38 ]. Yet, only a few documents were published each year during the 1980s. This indicates that the primary purpose of using CA during that period was commercial and academic application and reporting have only become popular during the last 20 years. Furthermore, many advances in CA methods were documented during the 1980s and 1990s [ 39 ] along with observations of greater interest in CA usage throughout the healthcare field during the 1990s [ 19 ].

The trend of CA documents published between 1950 and 2021.

The results of the citations analysis indicated that the business and economics field has the highest number of publications of CA. This is expected, as CA originated from this area of research, more specifically for marketing research. It is not surprising that the healthcare field of research was one of the top five areas publishing papers on CA topics. This indicates the growing interest in the CA method by healthcare researchers (see Table 1 ). In terms of the type of documents, the highest number of published documents were research articles (n = 7047; 73.3%), then meeting abstracts (n = 1624; 16.9%). Furthermore, the highest contributing countries to CA research were the USA (n = 2321; 24.1%), People’s Republic of China (n = 1549; 16.1%), and England (n = 899; 9.4%).

Top 10 research areas publishing CA documents.

Note: The number of published papers in Table 1 adds up to more than the total analyzed documents (n = 9614). The reason for this is that several documents are classified by the databases under several research areas; for example, some documents would be classified under psychology and healthcare sciences services at the same time.

4. The Conjoint Analysis Study Process

During the 1990s, the initial focus of researchers was to assess patient preferences and satisfaction regarding the treatment outcome only [ 25 ]. This was evidenced by the large number of health studies assessing patients’ quality-adjusted life years (QALYs) and healthy-years equivalents [ 24 ]. By the year 2000, the use of preferences methods gradually increased in the healthcare setting. Ryan and Farrar aimed to familiarize and engage patients with their treatment plan in cooperation with their physicians by allowing them to exhibit their preferences. This not only considered patients’ treatment response but also treatment characteristics, surgical options, as well as physicians’ care and attitude [ 16 ]. Ryan and Farrar stratified a multistep plan in order to practice a standardized CA study and achieve a precise assessment of patients’ preferred choices through five main stages stated as follows.

4.1. Identifying the Relevant Attributes

Attributes are known to be the factors, features, or characteristics which are believed to influence people’s preferences for a particular product or treatment [ 40 , 41 ]. Identifying attributes must be supported by evidence that suggests the potential range of preferences and values that people may hold [ 42 ]. Attributes must also be balanced between what is important to the respondent and what is relevant to decision-makers [ 42 ]. Selecting and defining the attributes can be achieved through reviewing the literature, healthcare experts’ group discussions, and interviewing individual subjects from the patients and public involvement (PPI) groups [ 41 ]. In some cases, a predefined policy question may be already available. Defining attributes is the most fundamental and critical aspect of designing a good CA study [ 43 ]. Therefore, attributes must be written in terminology that is easy for patients to understand. This was achieved when the wording and terminology of the attributes were based on the research users’ group (RUG) recommendations and suggestions [ 23 ]. Table 2 shows examples of different attributes for pain-relieving medication.

An example of attributes and levels for pain-relieving medication.

4.2. Assigning Levels

In CA, each attribute is defined by a series of levels [ 28 ]. Therefore, assigning levels to attributes will follow up from identifying attributes and is considered important. Levels represent the different alternatives for each attribute and must be reasonable and capable of being traded off against each other [ 44 ]. Bridges and colleagues suggested that researchers should avoid the use of ranges to define attributes (such as a copayment from USD 5–10) because this requires the respondent to subjectively interpret the levels, which will affect the results, and they should also be cautious of choosing too many levels [ 42 ]. Furthermore, levels of unrealistic and extreme values should be avoided as they will not be acceptable to respondents [ 23 ]. Table 2 shows examples of levels for pain-relieving medication attributes.

4.3. Choosing Scenarios

Once the attributes and levels are identified, the levels of all attributes are combined to form all possible scenarios. CA tasks are the mechanism by which possible profiles are presented to respondents for preference elicitation [ 42 ]. The higher the number of attributes and levels, the higher the number of possible scenarios. Therefore, researchers can very rarely use all produced scenarios. Instead, CA studies utilize the orthogonal fractional factorial experimental designs to construct a set of hypothetical scenarios [ 45 ]. If the scenarios are described with respect to all of the attributes being studied, this is referred to as a full-profile choice experiment [ 46 ]. For example, if the treatment studied has six important attributes, a full-profile scenario would describe the treatment of all six attributes. This means that each scenario would have all six levels; one from each attribute. When a study includes a large number of attributes, it becomes complex for participants to process these scenarios. Instead, the concept of partial-profile choice experiments has been proposed to estimate preferences for a large set of attributes [ 47 ]. In the partial-profile choice experiment, each scenario includes a subset of the total number of attributes being studied [ 18 , 48 ]. All attributes are randomly rotated into the different scenarios, so across all scenarios in the experiment, each respondent typically considers all attributes and levels [ 18 , 48 , 49 ]. For example, if the treatment to be studied has 12 attributes, a partial profile choice experiment describes the treatment on a few attributes in each scenario, i.e., each scenario would possibly have six characteristics of the treatment. The main issue with the partial-profile choice experiment is that the data are spread quite thinly because each task has many attribute omissions [ 50 , 51 ]. This task assumes that respondents can ignore omitted attributes and base their choice solely on the partial information presented in each task [ 51 ].

4.4. Establishing Preference

People’s preferences of the developed scenarios can be established using a rating, ranking, or choice-based approach. The ranking approach in Table 3 asks respondents to list the scenarios in order of preference [ 19 ], whereas the rating approach in Table 4 requests respondents to assign a score to each scenario, e.g., 0–10 or 0–100, usually presented on a visual analog scale [ 52 ]. Accordingly, the rating/ranking CA approach usually requires a low cognitive load and can be easily implemented, but they are direct scaling methods that impose the lack of clear trade-off between preferences [ 53 ]. The choice-based approach in Table 5 asks respondents to choose their preferred scenario out of a couple or few scenarios [ 54 ]. The choice-based approach presents multiple attributes susceptible to simultaneous assessment [ 55 ], then allows patients to pick the best available option from a set of scenarios which in turn permits patients to make clear trade-offs between different levels [ 56 ]. However, with the choice-based approach, there is a lack of ability to justify the reasons behind the choices made [ 57 ]. Rating, ranking, and choice-based CA approaches can be presented to respondents in various ways using different techniques. So, these conjoint methods could be designed through hard copies of pen and paper questionnaires or via computerized software programs.

Examples of the ranking approach.

Examples of the rating approach.

Examples of the choice-based approach.

In Table 5 , each vertical column represents a group of characteristics specific to medication A, B, and C, while each row illustrates the levels of a particular attribute in each of the medication scenarios. The participants are asked to select their preferred scenario and therefore, they have to trade-off attributes and levels against each other.

4.5. Analyzing Data

In CA methods, regression techniques are used to estimate the relative importance of the attributes and the utilities (part-worths) of the levels [ 19 , 23 ]. Moreover, the maximum amount of money that the patients are willing to pay for service or treatment is known as the willingness to pay (WTP) [ 44 , 58 ]. The part-worths are interval data within each attribute that represent the utilities of the levels within that attribute. Generally, part-worths are scaled to an arbitrary additive, while the relative importance is percentages data that are given to each attribute. The higher the percentage the more important is that attribute to the respondents and the relative importance of all attributes add up to 100%.

5. Conjoint Analysis in Healthcare

Several traditional quantitative and qualitative methods have been used to study patients’ preferences. However, these studies were limited in number [ 59 ]. These methods have been used to accentuate patients’ preference in general but did not identify the trade-off that patients make to one of the treatment factors against another [ 60 ]. Unlike traditional questionnaires, the CA method can be used to study preferences [ 13 ] and quantify the trade-off that patients do between the different treatment factors [ 61 ].

Nowadays, there has been a rapid increase in the use of CA to quantify preferences for various healthcare services and treatment options [ 62 ]. For example, in clinics where the majority of cases are non-urgent, CA was found to be a useful tool for allowing patients to discuss their needs and choose medication, health service, and diagnostic tests that suit them the most [ 63 , 64 ]. In turn, CA constituted a supportive tool for clinicians to better understand patients’ preferences and individualize their treatment plans [ 24 , 65 , 66 ]. Hence, the CA use in practice was suggested as a tool to effectively strengthen the communication between patients and healthcare professionals and to engage both parties in the shared decision-making process [ 12 , 62 ]. A scoping review of the studies using CA recommends the use of CA to identify patient preferences for mental health services, which could improve the quality of care and increase the acceptability and uptake of services among patients [ 67 ].

In the hospital setting, implementation of CA constituted a feasible and useful tool in several clinical areas such as investigating hospital stakeholders’ decision-making in the adoption of evidence-based interventions [ 68 ], eliciting patients preferences regarding diagnostics and screening [ 69 , 70 ], improving decision-making regarding patients’ treatment [ 71 , 72 ], understanding patients’ perceived needs and expectations [ 73 ], and determining the clinical factors that physicians prioritize regarding patients’ treatment [ 74 ]. Furthermore, the CA method was very useful during the recent pandemic, as it has been used to understand how people prioritize when deciding whether to present to the emergency department during the coronavirus disease (COVID-19) pandemic for care unrelated to COVID-19 [ 75 ]. This understanding of patients’ priorities helps healthcare professionals to structure an appropriate patient safety assessment which led to remarkably reduced chances of deaths, departments’ crowdedness, and spread of infections [ 75 ]. Moreover, CA was seen to efficiently assess the cost-effectiveness value of alternatives when patients are requested to select their preferred treatment option taking into consideration their financial situation [ 76 ]. Thus, CA is a practical method for generating treatments’ marketing decisions in the pharmaceutical industry, which highly relies on patients’ preferences about a specific drug.

Throughout the discussed CA-based studies, we can discern that CA is a major asset for valuing patients’ important contributions in the decision-making process by assessing patients’ preferences in accordance with treatment selection, patient care, offered health services, and cost comparisons. Research-based literature verified that CA can be a practical method of estimating utility for any combination of attributes, including combinations representing goods or services which may not currently be available [ 77 ]. Thus, when studying patients’ preferences for treatment, CA allows patients to choose their preferred treatment option based on the treatments’ characteristics in isolation of treatments’ names or market brands. Therefore, the precise performance of CA can offer valuable approximation in relation to the relative importance of different aspects of care, the trade-offs between these aspects, as well as the total satisfaction or utility that respondents obtain from healthcare services [ 19 ].

6. Validity of Conjoint Analysis Data

Recently, it has been suggested that the value of CA is not only related to its elevated frequency of implementation but also to the unique ability of this method to generate individuals’ preferences realistically enough to match various decision-making processes faced by individuals in the real world [ 78 ]. Assessing the validity of CA means examining the ability of this data collection tool to accurately measure what it is supposed to measure [ 79 , 80 ]. There are many types of validity and some of these types may overlap, and researchers may argue the names of different types of validity; for example, face validity is often confused with content validity [ 81 ]. Over 20 years ago, very little was known whether CA works in predicting significant real-world actions [ 82 ]. However, within the last couple of decades, researchers have been studying and investigating the validity of CA. Despite the high expansion of CA usage in market and healthcare research, CA validity studies are still limited. A large validity study including over 2000 commercial CA research indicated that there was no validity gain for CA over time [ 83 ]. This could be due to the variation in the CA tools, the expansion of recruitment methods to online and social media, and the differences in the estimation parameters used for each study. In general, the validity for all CA tools can be measured in two ways:

• External validity is the ability of the CA tool to predict what people would choose in real life. This can be achieved by asking the question “did people choose what CA predicted?”. For example, in a conjoint study estimating the market share for an American multinational telecommunications corporation, various trial simulations were implemented hypothesizing that several product features had to be changed in order to attain desired sales (8% of the total market share) [ 56 ]. Four years after launching this product, the actual share was just under 8% [ 56 ], concluding that CA contributes towards the identification of people-desired choices and the estimation of the actual preference behavior. Investigating external validity for CA methods is a challenging task that requires the researcher to follow the participants to examine if they did what the CA tool predicted in terms of buying a product, taking a treatment, attending a particular doctor’s clinic, etc.
• Internal consistency validity is the main validity criterion that has been studied in recent years for strengthening the reliability and applicability of CA. To test the internal validity, the holdouts’ choices are used [ 84 ]. The holdouts are choices that are similar to those selected by the participants in real life but are “held out” of the conjoint approximation by not being part of the final estimation. The internal validity of the conjoint task is examined by comparing how well conjoint utilities predict choices from the holdout tasks. Therefore, the holdout tasks are not used in the estimation of part-worths, but they are presumed to represent respondent choices in the real world [ 85 ]. In a review evaluating CA as a method of estimating consumers’ preferences, Green and Srinivasan reported that several studies have demonstrated the consistency of conjoint models in terms of reproducing current market conditions [ 39 ]. Furthermore, a study offering four topical antibiotics to treat acne confirmed CA consistency and validity when patients’ preferences assessment, the simulated product rankings, and the results of the traditional questionnaire were matched [ 86 ].

Generally, as the use of CA in the healthcare setting increased, some validity studies were performed to approach more patients’ preferences and expectations. These were not focused only on the patients’ benefit–risk trade-offs, but also on evaluating the patients’ WTP for treatments or services [ 55 ].

7. Strengths and Limitations of Conjoint Analysis

Over the years, CA design enabled researchers to elicit and quantify patients’ preferences for treatments and services using a smaller number of scenarios extracted from a larger pool of choices [ 1 , 19 , 72 , 87 , 88 , 89 ]. CA is well known for providing easy experimentation for aspects such as price and features before launching a new product, treatment, or health service [ 90 ]. It is suggested that when people are deciding between multi-attributes alternatives, they apply an unconscious scoring mechanism or system of their preference weight; CA is capable of uncovering this system [ 18 ]. This is achieved by providing respondents with the opportunity to make trade-offs between the specific features of competing items to reach final realistic decisions [ 18 , 91 ]. These trade-offs are based on the value that people place on each attribute.

Some of the limitations of the CA methods are due to the lack of validated quality assessment tools for CA studies and lack of consensus on appropriate sample sizes [ 1 , 3 , 4 ]. Furthermore, one of the inherent limitations of CA is that respondents are evaluating hypothetical scenarios, which might be different from what they do in real life [ 59 ]. It is suggested that the CA questionnaire fatigues respondents as it takes more time to complete than traditional questionnaires and it requires more focus and concentration [ 92 ]. It is also suggested that many patients are not well exposed to research surveys [ 57 , 93 ]. Therefore, reconsidering the number of questions and alternatives presented to participants during data collection is vital to avoid unnecessary respondents’ fatigue [ 1 , 4 ].

8. Strengths and Limitations of This Study

To the best of our knowledge, this is the first article to report the trend of CA publications and citations over the past 70 years and the increase in its popularity based on the amount of published literature. This article takes a well-defined, rich, and clear approach to the discussion of CA. It provides a summary of the very large and wealthy literature describing CA methods. The narrative nature of this article is based on a comprehensive search of the literature and utilized several databases. However, the narrative nature of the discussion could be subjective and open to different interpretations. Therefore, we recommend that the results of this article must be interpreted in line with its limitations. The results in relation to the CA trend over the past 70 years are not based on a comprehensive bibliometric analysis in terms of visualization. Nonetheless, it is based on a comprehensive search of WoSCC databases to identify the growth in CA publications.

9. Conclusions

The popularity of CA in healthcare has been increasing and its use in this setting is gradually competing with its use in business and marketing research. CA is a useful method for eliciting patients’ preferences and WTP. However, there are some limitations in the available CA literature, specifically regarding the appropriate sample size, quality assessment tool, and the validity of CA. This highlights the need for researchers from different fields that use CA methods to come together and develop tools to address these limitations.

Acknowledgments

The authors would like to thank Khalifa University of Science, Technology, and Research for funding and supporting this project.

Author Contributions

Conceptualization, B.A.-O.; methodology, B.A.-O.; software, B.A.-O.; validation, B.A.-O., J.F. and M.E.; formal analysis, B.A.-O.; investigation, B.A.-O., J.F. and M.E.; resources, B.A.-O., J.F. and M.E.; data curation, B.A.-O.; writing—original draft preparation, B.A.-O., J.F. and M.E.; writing—review and editing, B.A.-O., J.F. and M.E.; visualization, B.A.-O.; supervision, B.A.-O.; project administration, B.A.-O., J.F. and M.E. All authors have read and agreed to the published version of the manuscript.

Khalifa University of Science, Technology, and Research, Fund number: 8474000267/FSU-2020-32.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A Guide to Conjoint Analysis

Conjoint analysis definition + example.

Definition: Conjoint analysis is a research technique used to quantify how people value the individual features of a product or service. A conjoint survey question shows respondents a set of concepts, asking them to choose or rank the most appealing ones. When the results are displayed, each feature is scored, giving you actionable data. This data can help determine optimal product features, price sensitivity, and even market share.

Why Is It Important? Conjoint analysis goes beyond a standard rating question. It forces respondents to pick what product concepts they like best, helping identify what your audience truly values.

Interactive Conjoint Example Question

Conjoint analysis is used by any company wanting to do product research; in this example, a restaurant chain. If the chain wanted to release a new ice cream slot on their dessert menu, conjoint analysis would help determine optimal flavor, size, and price, like in the example conjoint survey question below.

This sample question has five total attributes, displayed over three sets, with three attributes shown per set.

If we offered a new menu item for ice cream, which of the following options would be most appealing to you? Please make one choice per set. If no options look appealing, choose "None."

This is an interactive example of choice based conjoint

When to Use Conjoint Analysis

Without conjoint analysis it would be impossible to ask about product prices along with flavor and size; a separate rating question for each flavor and size combination is needed. Conjoint analysis solves these problems with a straightforward survey question. When respondents evaluate this question, concept features are compared against one another, and a researcher can identify preferences.

Conjoint analysis is useful in two specific scenarios, marketing research and pricing analysis.

Marketing Research

Conjoint analysis is used in marketing research to identify what features of a product or service are most appealing to a customer base. This research can be conducted on existing products to improve advertising engagement or identify areas of improvement to increase sales. Conjoint analysis could also be used to conduct preliminary research for product feasibility.

A conjoint study will usually include demographic questions such as gender. A marketing executive can then segment the survey data by gender, revealing hidden insights used to bolster marketing strategy.

Pricing Research

Conjoint analysis is useful in pricing research because it forces customers to decide using trade-offs, helping to identify optimal prices for various levels. The ice cream example we use in this document has a $5 USD price with the highest utility, which is paired with a "medium" size. Without a conjoint study, it would have been logical to assume the "large" size should be sold for $5. Because of the trade-offs, the optimal size and price combination was found.

If the restaurant chain used multiple rating questions instead of conjoint, respondents would likely rate multiple flavors as good, and likely choose the lowest price. Using that method, it would be hard to gather reliable data.

How to Conduct a Conjoint Analysis Study

Often, preliminary data needs to be collected before running your conjoint study. An initial survey would include a MaxDiff or a Van Westendorp question to determine important product features or an acceptable price range. The preliminary survey acts as a baseline to reduce the number of conjoint concepts. A smaller number of concepts reduces survey fatigue and increases the quality of responses.

You also want to organize any custom data that you can be used in the survey. Suppose you want to segment your research by country (USA vs European customers). In that case, you need to make sure that internal data is valid, complete, and accessible by your team before running the conjoint study. If custom data is unavailable, you can add additional questions to the survey before the conjoint question.

With the preliminary survey data in hand and custom data organized, you can now create your conjoint analysis study. You can upload the product attributes and levels, include custom data, and you can add follow-up questions to ensure a successful project.

Conjoint Analysis Terminology

Conjoint analysis is an advanced research technique that uses a variety of unique terminology. To help you get a complete understanding, here is a list of commonly used conjoint terminology:

The high-level product features that respondents will evaluate are called attributes. Attributes are the first column in the above example question. That example has the following features: flavor, size, and price. If you studied a new car offering, you might have features such as color, make, model, MPG, and tire type. There is a limit of 20 attributes on the SurveyKing platform.

The items listed within an attribute are called levels. In the example, the "Flavor" attribute has levels of "Chocolate," "Vanilla," "Cookie Dough," and "Strawberry." When you create the conjoint survey, you define an attribute and the levels that go with each attribute. There is a limit of 15 levels on the SurveyKing platform.

Combining all your attributes and levels, which creates a hypothetical product, is called a concept. In the above example, concepts are the columns that respondents choose. Concepts are sometimes referred to as "cards" in statistical software. There is a limit of 7 concepts on the SurveyKing platform.

Also referred to as a task, a set contains multiple concepts or product offerings. Respondents will choose one concept per set and then be shown a new set of concepts. There is a limit of 20 sets on the SurveyKing platform.

Part-Worths/Utilities

This term is the most crucial in conjoint analysis. It defines how a respondent values each attribute level. When all the utilities for all respondents are analyzed, a researcher can determine an overall product value. Utilities are the output of a regression equation.

Utilities have no scale compared to other conjoint projects you run. They only matter in the context of the current question you are looking at.

Sometimes utilities are called "part-worths" or "part-worth utilities." We use the term "utility."

Types of Conjoint Analysis

Choice-based conjoint.

This is the most common form of conjoint. The example question above is a choice-based conjoint question. Respondents pick the most appealing concept for each set. Each set contains a random set of concepts that are evenly distributed. This type of conjoint best simulates buyer behavior since each set contains hypothetical products (concepts). When respondents choose a complete profile, a researcher can calculate preferences from the tradeoffs made. (e.g even though "Strawberry" isn't a preferred flavor, if the price were low enough, it would still provide consumer utility")

As with most conjoint studies, preliminary research is essential to reduce the number of attributes and levels to choose from. With fewer attributes and levels, the number of concepts is reduced, which lowers survey fatigue. A MaxDiff or ranking survey can be used to find the top four ice cream flavors.

Currently this is the only type of conjoint offered by SurveyKing.

Best/Worst Conjoint

Sometimes referred to as MaxDiff conjoint. Similar to choice-based conjoint, this method shows respondents a set of concepts. In each set, respondents are asked to pick the most/least (or best/worst) concepts. This approach is used when a product or service has features that cause both positive and negative reactions. An example could be studying how parents select daycare. The number of full-time faculty would draw a positive reaction. The percentage of fellow students that are economically disadvantaged could produce a negative reaction.

This is a future addition to the SurveyKing platform.

Adaptive Conjoint

This method is also similar to choice-based conjoint. Respondents pick the most appealing concept for each set, except with this method, the next set of concepts are not random but are tailored based on the previous answers. This method is more engaging to respondents and can help fine-tune the data.

Full-profile conjoint analysis

This method displays many concepts and asks respondents to rate each one based on the likelihood of purchase. This method is outdated and was primarily used prior to the introduction of survey tools that offer choice-based conjoint. Asking to rate lots of concepts at once is error-prone, quickly causes fatigue, and yields low-quality data.

Rating or Ranking Conjoint

Ranking and rating conjoint was the method used for full-profile conjoint analysis. As software has progressed, it is now possible to conduct rating or ranking conjoint similar to a choice-based conjoint. Respondents are shown a set of concepts and asked to rank or rate each concept. They could rank by entering a value for each concept, which sums to 100 for each set, or they could enter a number based on a scale. This method is also sometimes referred to as "Continuous Sum Conjoint".

Ranking conjoint is a future addition to the SurveyKing platform.

Menu-Based Conjoint

Menu-based conjoint is a new conjoint method. This method gives respondents the ability to pick multiple levels from a menu. For example, a car manufacturer could ask respondents to choose a base model and price, just like choice-based conjoint. But then they could also ask to check a box for each additional feature desired such as "Alloy Wheels for $1,500", "Sunroof for $1,000", or "Parking Assist for $1,500".

This method is much more advanced in terms of front-end programming and back-end statistics than choice-based conjoint. Often custom solutions need to be built for a company wishing to create this type of project.

Creating a Conjoint Survey

Any survey that contains a conjoint question is referred to as a conjoint survey. SurveyKing currently only offers choice-based conjoint. Here are the steps needed to create your own conjoint survey:

• Navigate to the "Builder" page of your survey
• Click on the "conjoint" element box, drag it into your questionnaire, or click the "Insert question" dropdown to add a conjoint question at the end of a specific page.
• To add a new attribute, click "Add attribute" within the conjoint builder. The builder will show levels for the attribute to the right of each attribute.
• Choose how many sets and concepts you want to display.
• Select any options to customize the question further.

Conjoint Survey Options

• "None" choice - This option will add one additional card, or column, per set that says "None" This option is marked by default. This setting reflects the real world, where consumers can choose not to buy a product. You should exclude this setting from projects where customers are forced to pick an option, such as a government service.
• Reset choices - With this option, respondents can start back at the beginning. The respondent will clear all answers for the question, and the first set will be displayed when the "reset" button is clicked. We recommend reserving this option for specific circumstances, as it could lead to second-guessing and low-quality data.

How Many Attributes, Levels, Concepts, & Sets are Needed?

An ideal conjoint question will have roughly 5 attributes (rows), 4 concepts per set (columns), and approximately 5 - 10 sets. This will help ensure respondents are not fatigued. A detailed breakdown is below:

• Attributes - Roughly 5 attributes with no more than 10 total levels per attribute. Having fewer levels per attribute ensures the survey will show various concepts more often.
• Concepts - Roughly 4 concepts to show each set. Too many concepts per set, and you risk respondents not making effective choices. The total amount of concepts available is calculated by multiplying the number of levels in each attribute. In the example above, we had four flavors, three sizes, and two prices. Total concepts available would be equal to 4 * 3 * 2 = 24. Ideally, this number should be no larger than 50. The more total concepts, the harder it becomes to draw meaningful conclusions.
• Total Sets - Showing no more than 10 total sets to respondents to avoid survey fatigue. Generally, 3-5 are best.

How Many Responses are Needed?

We recommend collecting at least 100 responses for each segment being researched. For example, if you wanted to research both males and females, you would want to collect 100 responses for both.

Conjoint Analysis Scoring & Results

Conjoint analysis uses regression to calculate how different attributes and levels are valued.

Because conjoint uses categorical data (a name like ice cream flavor) instead of continuous data (a number like a temperature), a particular type of regression is used called logistic regression . Just like any regression equation, the result of this regression calculates coefficients. These coefficients are referred to as "utilities".

Utility is not a standard unit of measure. It can be thought of as "happiness". If a lot of respondents choose concepts containing "Cookie Dough" and only a few choose concepts with "Vanilla.", even without doing the math, you can imagine that the coefficient for "Cookie Dough" would be higher than the coefficient for "Vanilla."

Let's say the coefficient for "Cookie Dough" is 5 and the coefficient for "Vanilla" is 1. We could interpret this as saying "A Cookie dough flavor of ice cream will add 5 units of happiness to a consumer, while vanilla would add only 1 unit of happiness." We would also factor in the utilities for serving size, and price, to come up with the product (or list of products) that would provide customers with the most value or "happiness".

To illustrate this concept, we ran the above ice cream example with 20 respondents. Below is the analysis of those responses. This analysis includes the utilities for each level in addition to the relative importance of each attribute.

Sample Survey Data - Summary Table

Walking through the analysis.

The utilities in the last column are the output of regression analysis. Next to each number is a small bar chart for visual representation.

Remember, utilities are not an actual unit of measurement and could be thought of as happiness. If we look at the above table, the "Cookie Dough" flavor has a utility of 14, and the "Vanilla" flavor has a utility value of 7. We could interpret this as "Cookie Dough has double the happiness of Vanilla."

The importance column is the weighted difference in utilities ranges for the product levels. You can see that flavor has the level with the largest difference of roughly 7. The larger the utility differences for an attribute, the more important they are to consumers. To get a significant difference, as we see with cookie dough, many respondents choose concepts with that flavor. We know the other levels are evenly distributed, meaning that cookie dough was a significant driving factor in decision-making regardless of size or price. Here's how you would calculate the importance:

Take the largest number for each level, and sum: 14.11+4.03+5.06 = 23.02

Divide each of the highest levels by this number. The calculation for flavor importance is 14.11 / 23.02 = 61%

Statistical Details

SurveyKing uses ChoiceModelR , a package in the R statistical program to compute conjoint utilities. ChoiceModelR calculates a coefficient using logistic regression with the maximum likelihood for each attribute level by each respondent. When the analysis is complete, utilities for each level are averaged. The output of our example can be found in this Excel file .

We color-coded the Excel file for each attribute level. Row 22 has an average subtotal, which the average utility for a specific level. The regression equations use effects coding to ensure each attribute in total sums to 0. Because of this, you will notice the excel file contains negative utilities. We shift each number by a constant to eliminate negatives and put the baseline to 0. The dark blue flavor columns were adjusted by 5.43 before the results being loaded into our dashboard. Having a 0 baseline makes the data easier to interpret.

Data used to populate ChoiceModelR:

• Data Matrix - See this Excel file , which is the input for the ice cream example. The first row of each card set contains the card number chosen (column G). The first card selected was 4. This is because the "none" option was selected. When the "none" is the chosen option, the highest index + 1 is the card selection. This is the input required for ChoiceModelR. Other programs use an output similar to this file . You'll see it's the same setup, except column G has a "1" if the card is selected or "0" if not selected. An additional row is added for the none column.
• R - The total number of iterations of the Markov chain Monte Carlo (MCMC chain) to be performed. Default value: 4000.
• Use - The number of iterations to be used in parameter estimation. Default value: 2000.
• Keep - The thinning parameter defining the number of random draws to save. Default value: 5.
• wgt - the choice-set weight parameter; possible values are 1 to 10. This parameter only needs to be specified if estimating a model using a share dependent variable. Default value: 1.
• xcoding - A number that specifies the way in which each attribute will be coded. We code each attribute as categorical, which is the value 0. Prices could technically be labeled as continuous, but for ease of calculations and consistency, we code all variables are categorical.

Time Spent Per Set

The time spent on each conjoint set is also included in the results. This data is useful to eliminate low-quality responses. Responses that answered a set too fast (under 2 seconds) should generally be eliminated from the results.

Analyzing Concept Profiles

A powerful benefit of conjoint analysis is quantifying how each concept would fare in the market. We can easily see the product with the most utility would be Cookie Dough, Medium, for $5 USD. But what about the top three products? Or the bottom three products? In the ice cream example, there were 24 hypothetical products. Unique to the SurveyKing platform is the ability to scroll through each concept in ranked order, to see what profiles faired the best or worst (or offer the most utility). The reporting section will automatically include the table shown below:

To get these figures from the Excel output file, you could create a table with all possible combinations, and use sumproduct to calculate to total utility. Here is an example .

Conjoint Analysis by Question Segments

Sometimes it's important to analyze different segments, such as gender. To do this, add a multiple-choice question to your survey for each segment you wish to study. In the reporting section, you can choose "Conjoint Segment Report." From here, select the appropriate question, and the report will output a data table for each answer. Using the ice cream example, you may notice "Males" prefer "Cookie Dough," while "Females" prefer "Vanilla." These are additional data points to fine-tune your marketing efforts.

Here is an interactive example of a conjoint comparison report unique to the SurveyKing platform. The first question asks for gender and the second question asks for a preferred ice cream concept. You can see males prefer "Cookie dough" with a utility of 23.06, while females prefer "Vanilla" with a utility of 25.63. Each gender segment lists flavor as the most important attribute. The report also includes a segmented ranking of concepts.

Analyzing Concept Market Share

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Sample Survey Data

Conjoint analysis tips.

• Keep descriptions simple - For both attributes and levels, keep the descriptions as short as possible. This will make picking choices easier and reduce survey fatigue.
• Images - Because of limited space, we recommend using images inside of each level sparingly. When images are used, we recommend that each image be custom-made for this project with a size no larger than 150px X 150px.
• Additional descriptions - Let's say you are researching a new phone. If you have a weight level of 7oz and 11oz, people won't be able to gauge that difference. You would want to say (ideally in the question text), "Use the iPhone 7 as a baseline weight, that weight would be considered average" Then the size product labels would be "Light," "Average," "Heavier."
• Be aware of incorrect conjoint content - There is a popular online video that explains conjoint analysis in Excel. The video uses "Dummy Variables" to compute the regression. This would be incorrect for two reasons. Excel cannot do logistic regression without any addons. Also, removing dummy variables is unnecessary if logistic regression is done correctly. The video codes a three-level attribute with 1's and 0's, which results in collinearity. Logistic regression assigns categorical data to a unique number. Like in our example, a four-level attribute would have the numbers 1, 2, 3, or 4, depending on what concepts were displayed.

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What Is Conjoint Analysis? How It Works and When To Use It

Conjoint Analysis is a sophisticated market research method that guides businesses on which product or service profile will be most successful for them.

Table of Contents: 

• How does conjoint analysis work? 

When to use conjoint analysis 

• Different types of conjoint analysis 

How to create a conjoint analysis survey, with example questions

How does conjoint analysis help interpret preferences .

• Alternatives to conjoint analysis
• How to run a conjoint analysis study with quantilope 

How does conjoint analysis work?  

Conjoint analysis is a technique where respondents are presented with a set of product or service concepts and asked to choose their preferred one. Within each description are multiple features (attributes) of that product/service, and options that can be compared on a like-for-like basis.

For example, if you were researching toothpaste you might present some of the following options for 100ml tubes with different price points, flavors, and benefit claims:

• Colgate - $3.70 - spearmint - plaque removal
• Crest - $3.25 - fresh mint - whitening
• Sensodyne - $4.20 - cool mint - gum health

Respondents are asked to make trade-offs between multiple products. However, the number of attributes included within each product description should be limited - ideally no more than six or seven - so that decision-making isn’t too complicated for the respondent; especially because in a real-life shopping scenario, consumers generally would not compare any more than this number of attributes when making a purchase decision.

Back to Table of Contents

Conjoint analysis is invaluable for any research into the impact of different product features on consumers’ purchase intent. If asked directly, in either quantitative or qualitative research, consumers will often say that all attributes are equally important, or won’t be able to say exactly which are more motivating than others in their purchase decision. A conjoint’s market simulation approach forces respondents to make trade-offs in the same way they might when making real-world decisions. Even if consumers are unaware of which attributes sway their decision, a conjoint analysis will reveal them. Some common business questions that can be answered with conjoint include: 

• What product/service configuration maximizes potential market share and/or revenue?
• What price point(s) are ideal for a given configuration?
• Can we increase price without negatively impacting share?
• What additional value can be offered to offset a pricing increase?
• How is share impacted if competitors change their pricing strategy or value props?

The consumer preferences extracted from conjoint can be fed into sales, marketing, and advertising strategies in any business. Product design, product development, product management, branding issues, package design, pricing research , and market segmentation exercises all benefit from conjoint analysis. Back to Table of Contents

Different types of conjoint analyses 

All conjoint studies compare total ‘packages’ of different products/services (the unique combination of attributes that each has), as well as the variations on those different attributes (called ‘attribute levels’ - for example within the attribute of ‘flavor’, the levels might include spearmint, fresh mint, and cool mint). How each attribute and level affect a respondent’s choice is calculated into a numerical value called a preference score (also known as a utility score). This can then be used to model ideal product scenarios, combining motivating attributes with optimum price points to see how they would affect projected market share and/or revenue.

Within this method there are different types of conjoint analyses; three examples are given below.

1. Choice-based conjoint analysis

Also known as discrete choice conjoint analysis, CBC is the most-used form of the method. One of its main benefits is that it reflects a realistic scenario of choosing between products rather than questioning respondents directly about the importance of each attribute. Respondents are shown sets of 3-5 concepts at a time and asked to choose their favorite, then the importance of each attribute is inferred from their choices. This is a powerful way to understand which features are most important to include in a new product and how to price it. From this information, brands can derive optimal product configurations.

2. Adaptive conjoint analysis

Adaptive conjoint analysis is a flexible approach to CBC, adapting as a survey progresses so that the choice sets that each respondent sees depend on the preferences they have expressed up to that point. Tailoring the questions to each individual streamlines the approach to CBC, as it doesn’t waste time showing product concepts that the respondent would not find appealing, thus cutting down the length of the survey. Respondents can also find this type of conjoint experience more engaging when they see the survey is reacting to their personal preferences.

3. Self-explicated conjoint analysis

This version of conjoint analysis takes the focus off the package of attributes as a whole and instead zooms in on the attributes and attribute levels. Respondents are able to eliminate attributes they wouldn’t consider at all, as well as choose their favorite and least favorite attribute levels. The remaining levels are then rated against the most/least favorite level. The importance of the favorite attribute in the context of the product as a whole is calculated, and a utility score is given for each attribute and attribute level. This CBC method doesn't demand the same level of statistical analysis as other types of conjoints, but it isn’t best suited to pricing research as price can’t be fairly compared to other attributes.

In product research

Suppose a fast food chain wants to introduce a new burger to its menu. Some of its business questions before launching this new menu item might be around ingredients, calorie content, taste, and price. The new burger has a number of potential options that it could offer to the market but the fast food business needs to be sure that the options it chooses will have optimal uptake among their diners. Using conjoint analysis, the fast food chain could test the following combinations of product features to respondents, asking them to identify their preferred burger:

Option 1:         Plant-based, spicy, 650 calories, $5.25

Option 2:        Prime beef, spicy, 790 calories, $4.29

Option 3:        Prime beef, with cheese, 820 calories, $4.99

Option 4:        Plant-based, with cheese, 900 calories, $6.19

Rather than asking respondents about ingredients, taste, calories, and price separately, conjoint analysis presents the features within a realistic product context and analyzes the results to reveal which features are the most powerful in driving purchase.

In service research

Similarly, service propositions can be presented with a variety of feature combinations for respondents to choose from. For example, a TV & broadband service set of options might look like this:

Option 1:        Fast broadband, basic TV bundle plus sports channels, $50 per month

Option 2:       Superfast broadband, basic TV bundle, and no sports channels, $90 per month

Option 3:       Superfast broadband, TV with movies and sports channels, $120 per month

Option 4.       Fast broadband, TV with movies but no sports channels, $100 per month

Conjoint analysis will reveal which parts of the service respondents attach the most importance to and which they will pay more for. Back to Table of Contents

Consumers are faced with product decisions every day and there's a lot of different aspects that go into choosing a certain product over another - but often, we don't know what those are. Many standard usage and attitude questions don't quite get into the nitty-gritty details of consumer decision making that businesses need to succeed. Conjoint analysis (a type of advanced methodology) helps researchers unravel consumers' complex decision-making by effectively breaking down product offerings into attributes and levels. By breaking down the overwhelming amount of products (and their features) into distinct attributes, businesses can hone in on what specific features of a product actually impact final decision making. Researchers can present respondents with different attributes in various combinations to understand the overall preference (and therefore, value) of each individual feature.   Through conjoint analysis, researchers can quantify trade-offs consumers are willing to make between different attributes, providing insights into their underlying preferences. Knowing this, a business is then in a position to use the most preferred features during the final product design phase, to develop a solid pricing strategy, to craft an influential marketing campaign, and ultimately, to enjoy a successful final product launch. 

Alternatives to conjoint analysis  

As mentioned above, the alternative to using a conjoint analysis for understanding decision making might be left to standard usage and attitude questions such as 'Which product do you prefer' or 'How much do you like this product'?; a s you might guess, these outputs aren't going to give you nearly the same actionable insights as a conjoint analysis will. 

While conjoint analysis is one of the best methodologies a brand can use to understand feature importance, there are some alternatives for those who don't have access to conjoint or who prefer to go another route:

MaxDiff (Maximum Difference Scaling):

MaxDiff is similar to conjoint analysis in that it forces respondents to make tradeoffs, but the difference is that it focuses on the most and least preferred features individually, rather than evaluating the features as part of set combinations (which is more realistic to a true shoppers' experience). 

Van Westendorp Price Sensitivity Meter (PSM):

For brands looking to make a decision around pricing specifically, Van Westendorp is a great method to use to understand how price sensitive your consumers are. Respondents are asked a series of questions to determine acceptable price ranges for a given product (i.e at which price point the product is considered too cheap, too expensive, a bargain, or justified). The intersection of all these price ranges helps identify an optimal price range. But remember, pricing is just one aspect that a brand could test in a conjoint analysis, creating a much more cohesive understanding of a product rather than looking at price alone. 

Key Driver Analysis (KDA):

The end goal for researchers running a conjoint analysis is really to learn what's driving consumer purchases. Another advanced methodology that's effective in uncovering this kind of information is, as it's aptly named, the Key Driver Analysis (KDA). A KDA identifies the overall factors or variables that have a significant impact on a particular outcome - such as product purchase intent in this case. This is a useful method when looking to understand the overall impact a variable might have on consumers' decisions, while a c onjoint analysis is better at understanding and optimizing individual features. 

How to run a conjoint analysis study with quantilope

quantilope’s advanced, automated conjoint analysis method identifies the relative importance of product attributes and attribute levels in a category to help you create a product that offers the optimal configuration of those attributes. The market simulator will predict how market share would change as a result of lowering or increasing the price, or by tweaking any other attributes that make up the product. It can also show how preferences vary by customer segment.

All you have to do is decide which attributes you would like respondents to trade off against each other. Once you have your set of attributes, you simply need to drag the pre-programmed method into your survey before it goes live . Upon survey data capture, quantilope’s AI-driven tools analyze the data using statistical techniques.

Check out quantilope’s conjoint analysis approach to pricing research for an example of how this method can be applied for this particular use case. This brief demo video also shows how straightforward it is to set up a conjoint study on quantilope's platform, as well as how simple it is to interact with findings and create the optimal product profile - in this case, an energy drink. The video demonstrates how altering the price can affect market share, and how changing other aspects such as ingredients and packaging can affect potential uptake.

To learn more about how your brand can leverage quantilope's conjoint analysis for your own brand needs, get in touch with us below: 

Request a demo!

Related posts, quirk's virtual event: fab-brew-lous collaboration: product innovations from the melitta group, brand value: what it is, how to build it, and how to measure it, what are brand perceptions and how can you measure them, how can brands build, measure, and manage brand equity.

An introduction to conjoint analysis

Last updated

1 April 2024

Reviewed by

Customers have different preferences that play a role in their purchase decisions. For businesses, meeting these different needs can be challenging. However, conjoint analysis can help make data-driven decisions that optimize products and services, making them more appealing to customers. 

Read on to learn more about the benefits of conjoint analysis and how it can help businesses make informed decisions about product development, pricing, and marketing strategies.

Market analysis template

Save time, highlight crucial insights, and drive strategic decision-making

• What is conjoint analysis?

Conjoint analysis is a survey-based statistical analysis method to understand how customers value products and services and why they make certain choices when buying. 

A product or service comprises multiple conjoined attributes or features, and this is what conjoint analysis focuses on. A conjoint analysis breaks down a product or service into its attributes and tests the different components to reveal customer preferences. 

• Why is it important for researchers?

It helps measure the value the consumer places on each product attribute.

It predicts a combination of features that will have the most value to customers. 

It helps segment customers according to their perceived preferences. This helps with tailoring market campaigns to the right target customers. 

It enables researchers to get customer feedback about an upcoming product. 

• Uses of conjoint analysis

Conjoint analysis is primarily used to make informed decisions relating to:

Buyer decisions

Customer preferences

Market sales

New product pricing

Selection of the best service or product feature

Market campaign validation

• Why use conjoint analysis in surveys?

Conjoint analysis pinpoints what customers value the most, thus revealing their preferences, what they’re prepared to “trade off”, and why.  

• Two types of conjoint analysis 

Two types of conjoint analysis are:

Discrete choice-based conjoint (CBC) analysis

CBC is the most common form of conjoint analysis that asks customers to mimic their buying habits. It asks respondents to choose between a set of product or service concepts. For instance, the choice-based conjoint analysis format presents questions such as "Would you rather?". 

The advantage of discrete choice-based conjoint is that it reflects a realistic scenario of choosing between products rather than directly questioning respondents about each attribute's significance. 

Adaptive conjoint analysis (ACA)

This flexible approach adopts a questionnaire procedure that tailors questions to address personal preferences. The adaptive conjoint analysis targets the respondent's most preferred attribute, thus making the analysis more efficient. 

• When to use it? 

Businesses use conjoint analysis for the following:

Conjoint analysis in pricing

Businesses can use conjoint analysis to ask customers to compare different product features to determine how they value them. It’s an excellent way to learn what features customers are willing to pay for. 

When business owners fully understand what customers value, they can determine the price they’re willing to pay for their products or services. 

Conjoint analysis in sales & marketing

With conjoint analysis, businesses discover customer preferences, allowing them to create marketing campaigns that will target their preferences and increase sales. 

Also, findings of a conjoint analysis could help determine whether there’s enough market for a new product or service.

Conjoint analysis in research & development

With conjoint analysis, product developers can define customer needs and bring the right product or service idea to life. 

In addition, at the beginning of product development , a conjoint analysis will help reveal the concepts that aren’t valued by customers, allowing businesses to eliminate them at the early stages. This saves time and valuable resources and minimizes the risk of a failed product launch. 

• How to do a conjoint analysis

The steps of performing a conjoint analysis are as follows:

Step 1: Define the study problem

Defining the problem establishes the purpose of the experiment. Whether you want to understand your customers better, find a perfect pricing strategy, or predict the market share, problem definition will define the scope of the study. 

In this step, the business owner must consider the target audience and craft specific, meaningful questions. 

Step 2: Break down the product or service into attributes

The next step is to determine the list of attributes of your product or service. Attributes should have varying levels in real life, be clearly defined, and be expected to influence customer preferences and exhibit strong correlations. 

For instance, if you sell cars, the attributes could be engine capacity, trim level, fuel efficiency, color, pricing, warranty, and design. Again, remember to use short descriptions to avoid misunderstandings. 

Step 3: Choose the conjoint analysis methodology

The next step is to organize the questionnaire according to the type of conjoint analysis preferred. 

Choosing CBC is effective when you want respondents to select a preference from a set of choices. ACA is appropriate when you want more accurate information on an individual level. 

Step 4: Deploy the questionnaires to your target respondents 

The questionnaire should have varying features so that the researcher can observe the attributes driving the choice. If the ACA method is used, ask the respondents to rank the attributes based on their needs. 

When the rankings are complete, the researchers get a clear picture of which feature(s) are highly rated by respondents and which aren’t.

Step 5: Data collection and analysis

This step involves collecting data accordingly and using it for decision-making. The rating given by respondents is a raw set of data. The business owner then assigns weights to each category. 

Finally, you can determine the attribute that ranks as the most important, and this will give you information about what customers value the most in your product or service. 

• Five advantages of conjoint analysis

The advantages of using conjoint analysis include the following:

Researchers can determine customer preferences at an individual level.

It reveals the hidden drivers of why customers make certain choices.

It’s a perfect tool for experimenting with attributes such as price before launching a new product or service. 

Conjoint analysis is highly flexible and can be used to develop almost every product or service.

It’s a versatile method that realistically reflects an everyday purchase decision.

• Conjoint analysis examples

The following are two real-world examples of conjoint analysis: 

Example one: A manufacturer seeking to launch a new laptop

When launching a new laptop, manufacturers must know what customers value the most to ascertain what feature draws them to their offerings. Therefore, businesses must conduct a conjoint analysis. The manufacturer will develop a questionnaire that will gather insights from the respondents. 

The attributes that define the laptop are:

The operating system is either Microsoft Windows, Linux, or MacOS. 

The processing speeds

Storage space: is it a 500GB hard drive or 1TB?

Battery life

Screen size

With the help of conjoint analysis, the manufacturer puts a value on each attribute and tailors the product to what’s valued most by a customer. Findings of customer preferences allow the manufacturer to design the "best" laptop technically possible.  

Example two: A restaurant owner seeking to attract a broad customer base 

The restaurant owner may want to differentiate themselves from the competition and attract a wider customer base. They will conduct a conjoint analysis based on what people value the most to understand customer choices. 

People go to restaurants for several reasons, including:

Quality of food

Meal purposes (business, tourist, family, etc.)

Type of food served (seafood, Chinese food, etc.)

The restaurant owner will carry out a conjoint analysis based on the above criteria. The survey response will reveal what customers value the most and allow the restaurant owner to maximize the highly valued feature.

What is an attribute in conjoint analysis?

It’s a product characteristic such as price, size, brand, or color. 

What are attribute levels?

Attribute levels are the values that each characteristic can take. For instance, the attribute shape can have small, medium, large, or extra-large levels. 

How do you identify an attribute?

When defining an attribute, use a language that a customer understands. You can also use images to minimize confusion.

How many people do you need for conjoint analysis?

The sample size for a conjoint analysis depends on the target market. If the target market is relatively small, use a small sample size and vice versa. A general rule of thumb is to use sample sizes that range from 150 to 1,200 respondents. 

What are the real-life applications of conjoint analysis?

You can use conjoint analysis to test the appeal of new products such as soft drinks, footwear, or home appliances. 

How do you calculate market share in conjoint analysis?

You can determine market share by taking a business's sales over a period and dividing it by the industry's total revenue over the same period.

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The Plain-English Guide to Conjoint Analysis

Published: February 23, 2022

Sometimes, commercials really get me.

T-Mobile 's Super Bowl commercial this year is a prime example — "What's for Dinner?" demonstrates the infuriating process of choosing what to do for dinner for a young couple, and it's gold .

The reason T-Mobile's ad was so relatable is because of their market research. They looked at what their target audiences wanted — including their thought processes, what informs their decisions, and the trade-offs they're willing to make for their products.

To accomplish all of these important factors in one go, many companies use conjoint analysis.

What is Conjoint Analysis?

Conjoint analysis is a market research tactic that attempts to understand how people make decisions. A common approach, the conjoint analysis combines realistic hypothetical situations to measure buying decisions and consumer preferences.

Think about buying a new phone. Attributes you might consider are color, size, and model. The reason phone companies include these specs in their marketing is due to research such as conjoint analysis.

Would consumers purchase this product or service if brought to market? That's the question conjoint analysis strives to answer. It's a quantitative measure in marketing research, meaning it measures numbers rather than open-ended questions. Questions on the phone company survey would include price points, color preference, and camera quality.

Surveys intended for conjoint analysis are formatted to reflect the buyer's journey.

For instance, notice in this example for televisions, the specs are the options and the consumer picks what best reflects their lifestyle:

This direct method of giving consumers multiple profiles to then analyze is how conjoint analysis got its name. These answers are helpful when determining how to market a new product.

If answers on the phone company survey proved that their target audience of adults ages 18-25 wanted a green phone from $400-600 and a camera with portrait mode, advertisements can cater directly to that.

The conjoint analysis shows what consumers are willing to give up in order to get what they need. For instance, some might be willing to pay a little more money for a larger model of a phone if their preference is larger text.

Types of Conjoint Analysis

Choice-based conjoint (CBC) and Adaptive Conjoint Analysis (ACA) are the two main types of conjoint analysis.

Choice-based is the most common form because it asks consumers to mimic their buying habits. ACA is helpful for product design, offering more questions about specs of a product.

Choice-based conjoint analysis questions are usually presented in a "Would you rather?" format. For example, "Would you rather take a ride-share service to a location 10 minutes away for $13 or walk 30 minutes for free?" The marketer for the ride-share service could use answers from this question to think of the upsides to show off in different campaigns.

ACA leans towards a Likert-scale format (most likely to least likely) for its attribute-based questions. Respondents can base their preference on specs by showing how likely they are to buy a product with slight differences — for example, similar cars with different doors and manufacturers.

How To Do A Conjoint Analysis

To create a conjoint analysis, you'll first need to define a list of attributes about your product. Attributes are usually four to five items that describe your product or service. Consider color, size, price, and market-specific attributes, such as lenses if you're selling cameras.

Additionally, try to keep in mind your ideal respondents. Who do you want to answer your survey? A group of adult men? A group of working mothers? Identify your respondent base and ask specific questions catered to that target market.

The next step is to organize your questionnaire depending on the type of conjoint analysis you want to conduct. For instance, to run an adaptive conjoint analysis, you will present questions with a Likert-scale.

You can use a conjoint analysis tool to create and modify your survey. Then, you can distribute your questionnaire through multiple channels, including email, SMS, and social media.

For more ways to introduce product marketing into your company, check out our ultimate guide here .

Examples of Conjoint Analysis

Sawtooth Software offers a great example of conjoint analysis for a phone company:

The analysis puts three different phone services next to each other. The horizontal column of the model identifies which service is offering a certain program, described by the vertical values. The bottom row shows a percent value of consumers' preferences.

QuestionPro offers this fun, interactive conjoint analysis template about retirement home options. The survey gives you a scenario and asks your course of action. For instance, it asks if you would sign a rental agreement for retirement home housing immediately, and considers specs like rent, meals, size, etc.

Conjoint analysis isn't limited to existing products. They're also very helpful for figuring out if a brand-new product is worth developing. For instance, if surveys show that audiences would be into the idea of an app that chooses clothes for consumers, that could be a new venture for clothing companies in the future.

Looking to create a conjoint analysis of your own? Check out our top four conjoint analysis tools below.

Conjoint Analysis Tools

1. qualtrics.

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Qualtrics is an easy-to-use survey tool that offers comprehensive product insights. You can create, modify, distribute, and analyze a conjoint analysis in one place. All it takes is four steps — define your attributes, build and modify your questions in the survey editor, distribute the survey, and analyze the results. 

What We Like: Qualtrics goes beyond product insights — this powerful software also captures customer, brand, and employee experience insights.

Pro Tip: Leverage email to invite respondents to take your survey. With Qualtrics, you can embed a survey question directly in your email survey invite. 

2. Cojoint.ly

Conjoint.ly offers a complete toolbox for product and pricing research — including a Product Description test, an A/B test, and a Price Sensitivity test. You can also source your own respondents for your survey or buy quality-assured respondents from Conjoint.ly.

What We Like: Users can simply choose a tool that best fits their research question. These tools are organized under four main categories: pricing research, features and claims, range optimization, and concept testing.

Pro Tip: If you want to "try before you buy," you can use Conjoint.ly's Quick Feedback tool. For a small price, you get around 50 respondents to provide feedback within a 6-hour window.

3. 1000minds

1000minds offers an adaptive conjoint analysis tool. Meaning, each time a choice is made, it adapts by formulating a new question to ask based on all previous choices. This makes the survey feel more like a conversation.

What We Like: We're impressed by the scalability of 1000minds. The tool allows you to include as many participants as you like, potentially in the thousands.

Pro Tip: You can use their conjoint analysis templates or build your own model from scratch. 

4. Q Research Software

Q is analysis software that is specifically designed by market researchers. Its conjoint analysis tool is ideal for choice-based analyses. Users can create experimental designs, analyze the data, and generate reports. 

What We Like: Q cuts through the grunt work with automation — including cleaning and formatting data, updating surveys, and producing reports.

Pro Tip: With just a few clicks, you can export any reports or visualizations from Q to PowerPoint and Excel.

A conjoint analysis requires a solid survey design and analysis, but the extra effort is often worth it. By going the extra mile, you can access insights into your audience's preferences and buying decisions — which is invaluable when determining how to market a new product or service.

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Conjoint Analysis Definition, Types, and Examples

Conjoint analysis is a market research technique used to understand how consumers value different features of a product or service. It involves presenting respondents with a series of hypothetical scenarios and asking them to choose their preferred option.

Table of contents

What is Conjoint Analysis?

What are the types of conjoint analysis.

• Why is Conjoint Analysis important for Researchers?

Benefits of Conjoint Analysis

Drawbacks of conjoint analysis, conjoint analysis examples, tips for using conjoint analysis.

Have you ever wondered how companies determine the perfect combination of product features that will appeal to their customers? One popular technique used by market researchers is called “conjoint analysis.” This method involves presenting survey respondents with different product configurations and asking them to rate or rank their preferences.

By analyzing the data, researchers can identify which product attributes are most important to consumers and how they interact with one another. In this article, we’ll dive into the definition, types, and examples of conjoint analysis to help you better understand this valuable research tool.

Conjoint analysis is a statistical technique used in surveys to understand how people make decisions and evaluate products or services based on their attributes. It involves presenting participants with a series of hypothetical scenarios that vary in the attributes of the product or service being evaluated. By analyzing the choices participants make in these scenarios, researchers can determine the relative importance of different attributes and how they affect overall preference.

Conjoint analysis can provide valuable insights into consumer behavior and help businesses make informed decisions about product development, pricing, and marketing. It is commonly used in market research, product design, and customer satisfaction studies.

• Hot-Button Conjoint Analysis This type focuses on the emotional response of respondents to features and aspects of products or services. It can provide valuable insights into the correlation between emotional responses and purchase decisions.
• Pairwise Comparisons Choice-based analysis is a survey-based method used in market research, new product design, government policy-making, and the social sciences to understand people’s preferences and shape products and policies accordingly. It is based on the 1000minds PAPRIKA technique , which uses questions based on choosing between pairs of alternatives to determine people’s utilities (weights).
• Grid Analysis Grid analysis is a type of market research technique that helps to evaluate the attractiveness of different product or service features. It can help companies determine which features are most important and make sure they include them in their products. Grid analysis can also be useful in helping to identify which features consumers are willing to pay a premium for and which ones they aren’t as interested in. This can be helpful in developing pricing strategies and product design.
• Rating Scale Analysis Rating scale analysis of conjoint data is a type of analysis used to assess consumer preferences and make decisions about product features and marketing strategy. It is different from other forms of conjoint analysis, such as choice-based conjoint analysis, as it does not directly link to behavioral theory. It is limited in the number of attributes that can be included in the study, but it provides an effective way to understand consumer preferences and make decisions about product features and marketing strategy.
• Tree Analysis Tree analysis is a type of conjoint analysis often used in market research to understand the customer’s preferences for different product attributes. This type is different from other analyses in that it uses a hierarchical structure to organize and rank customer preferences. For example, a tree analysis could differentiate between a brand preference, such as “HP” vs. “Dell” versus the actual product attributes, such as processor type, hard disk size and amount of memory.
• MaxDiff Conjoint Analysis MaxDiff analysis is a type of market research methodology used to determine the relative values of combinations of features by asking customers to rate them from best to worst. It is similar to other forms of conjoint analysis, such as Choice-Based Conjoint (CBC) Analysis, Adaptive Conjoint Analysis (ACA), and Full-Profile Analysis, but differs in that it presents a smaller set of product profiles for evaluation. This makes the task easier for respondents, and MaxDiff can also be used with other research techniques to provide more detailed insights into customer preferences.
• Multi-Way Analysis A multi-way analysis is used to measure the reactions to a range of product attributes by creating a matrix of choices. Unlike traditional analysis which only presents a single attribute or feature to the respondent at a time, multi-way analysis presents multiple attributes or features to the respondent for consideration in a single-choice task. This allows the researcher to understand how different combinations of attributes affect the respondent’s preference. Multi-way analysis can also be used in combination with other forms of conjoint analysis such as choice-based conjoint (CBC), adaptive conjoint analysis (ACA), full-profile conjoint analysis, MaxDiff conjoint analysis, and hierarchical Bayesian Analysis (HB).
• Choice Modeling Choice modeling is a type of analysis that looks at the choices that customers make when they are presented with several options. It is used to understand the trade-offs that consumers make when evaluating different attributes of a product, and can be used to uncover hidden drivers that may not be apparent to respondents. It also mimics realistic choices or shopping tasks and can be used to develop needs-based segmentation in some cases.

Why is Conjoint Analysis Important for Researchers?

Conjoint analysis is one of the most important tools for researchers as it helps them to gain insights into a consumer’s preferences and decision-making processes on an individual level. It allows for a deeper study of the consumers and attributes involved to create a needs-based segmentation, providing user-based affirmation of what is most valued in the product or service. This helps researchers to understand the trade-offs that consumers make when they evaluate multiple attributes simultaneously, giving them insight into the real and hidden drivers that may not be readily apparent.

Furthermore, researchers are able to measure consumer preferences and analyze data to gain statistically relevant insights representative of a larger group. As a result, conjoint analysis has become the gold standard for preference research and is used by many businesses in different industries across the globe.

By using surveys, businesses can measure the value that different features have for consumers. This information can be used to create products and services that better meet the needs of customers. By understanding what customers value most, businesses can create offerings that increase satisfaction.

Conjoint analysis is a technique that can be used to find the best combination of product features by surveying customers. First, determine the features you would like to examine, and select the target customers to survey. Then, reach out to customers with a survey that presents them with different combinations of features and asks them to rank them based on their preference. After the surveys are returned, analyze the results to determine the optimal feature set for your needs.

This analysis can be used to estimate the market share of new products by gathering data from customers on their preferences for different product alternatives and attributes. This data is then used to create a choice model which estimates the likelihood of each product being chosen by potential customers.

Conjoint analysis is a tool that can help businesses identify which product features are most valuable to customers. By conducting a conjoint analysis survey, businesses can determine which features are the most important to their customers and develop a marketing strategy that is most successful.

Conjoint analysis can be used to evaluate the effectiveness of advertising campaigns by determining what consumers are willing to pay for certain features and attributes. By analyzing the data collected from a conjoint study, marketers can gain a better understanding of what consumers are willing to buy, which allows them to refine their advertising strategies.

Conjoints require careful consideration of multiple attributes and levels, which can lead to a complex design. As the number of attributes and levels increases, so does the complexity of the design, making it difficult to manage and analyze.

Conjoints often involve asking respondents to evaluate a large number of product profiles, which can lead to respondent fatigue. This can result in lower response rates and lower-quality data as respondents may not be fully engaged in the survey.

The results of conjoint analysis are specific to the attributes and levels included in the design. This means that the results may not be generalizable to other products or markets, limiting the usefulness of the analysis.

Conjoints assume that respondents make decisions based on a rational evaluation of the attributes and levels presented to them. However, in reality, decision-making is often more complex, and emotional and psychological factors can also play a role.

Here are four examples of how conjoint analysis can be used in real-world scenarios:

• Hotel Room Preferences – A hotel chain wanted to know which room features were most important to their guests, such as the size of the room, the view, and the amenities. Using this analysis, they presented survey respondents with different room configurations and asked them to rate their preferences. The analysis revealed that a spacious room and a good view were the most important factors for guests.
• Fast Food Menu Optimization – A fast food chain was looking to optimize their menu by determining which items and prices would be most appealing to customers. Using conjoint analysis, they presented survey respondents with different menu options and asked them to rank them. The analysis revealed which items were the most popular and at what price points they were most appealing.
• Car Purchase Decisions – An automotive manufacturer wanted to understand which car features were most important to consumers when making a purchase decision. Using this analysis, they presented survey respondents with different car configurations and asked them to rate their preferences. The analysis revealed that safety features, fuel efficiency, and performance were the most important factors for consumers.
• Smartphone Preferences – A smartphone manufacturer was planning to launch a new device and wanted to understand which features would be most appealing to consumers. They presented survey respondents with different phone configurations and asked them to rank them by their preference. The analysis revealed that the most important factors for consumers were screen size, battery life, and camera quality. With this information, the manufacturer was able to optimize their new phone’s features and pricing strategy to better meet customer preferences.
• Know the purpose of the analysis and the questions you are trying to answer.
• Identify the factors that are important to customers and the attributes of your product or service that you want to measure.
• Test and refine the design of the questions to ensure they accurately measure the preferences of customers.
• Create scenarios that best reflect what customers would experience in the real world.
• Analyze the data collected and interpret the results to get the most out of your conjoint analysis.
• Leverage the results to create models that help you make better, more informed decisions.
• Consider partnering with a professional data analysis firm for additional insight.

In conclusion, conjoint analysis is a powerful tool for understanding consumer behavior and preferences. It provides a systematic way to evaluate and compare different attributes of products or services and their impact on overall preference. There are several types of conjoint analysis, including full-profile, adaptive, and choice-based, each with their own strengths and weaknesses.

Examples of applications include new product development, pricing research, and customer satisfaction studies. By using conjoint analysis, businesses can gain insights into what factors drive consumer decision-making, and use that knowledge to make informed decisions about product development, pricing, and marketing strategies.

FAQ on Conjoint Analysis

What is conjoint analysis and how does it work.

Conjoint analysis is a market research technique used to determine how consumers value different features of a product or service. It works by presenting participants with a series of hypothetical product or service profiles that vary in terms of their attributes (such as price, quality, and design), and asking them to choose their preferred option from each set.

What are the advantages of using conjoint analysis in market research?

Conjoint analysis can provide valuable insights into how consumers make decisions and what factors influence their choices. It can also help businesses understand how to price their products or services, design new products or services, and target specific consumer segments.

How do you design a conjoint analysis study?

To design a conjoint analysis study, you need to first identify the attributes that are most relevant to your product or service. You then need to create a set of product or service profiles that vary in terms of these attributes, using a statistical technique called fractional factorial design. Finally, you need to recruit participants and present them with the profiles, asking them to choose their preferred option from each set.

What are some limitations of conjoint analysis?

Conjoint analysis relies on participants' ability to accurately evaluate and compare different product or service profiles. If the profiles are too complex or if participants are not familiar with the attributes being tested, the results may not be reliable. Additionally, conjoint analysis assumes that participants make decisions based solely on the attributes presented, which may not be true in real-world situations.

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An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research

• Perspectives
• Published: 23 October 2014
• Volume 2 , pages 19–40, ( 2015 )

Cite this article

• James Agarwal 1 ,
• Wayne S. DeSarbo 2 ,
• Naresh K. Malhotra 3 , 5 &
• Vithala R. Rao 4  

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This review article provides reflections on the state of the art of research in conjoint analysis—where we came from, where we are, and some directions as to where we might go. We review key articles, mostly contemporary published since 2000, but some classic, drawn from the major marketing as well as various interdisciplinary academic journals to highlight important areas related to conjoint analysis research and identify more recent developments in this area. We develop an organizing framework that attempts to integrate various threads of research in conjoint methods and models. Our goal is to (a) emphasize the major developments in recent years, (b) evaluate these developments, and (c) to identify several potential directions for future research.

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Research Methodology: An Introduction

Avoid common mistakes on your manuscript.

1 Introduction

Conjoint analysis is one of the most celebrated research tools in marketing and consumer research. This methodology which enables understanding consumer preferences Footnote 1 has been applied to help solve a wide variety of marketing problems including estimating product demand, designing a new product line and calibrating price sensitivity/elasticity. The method involves presenting respondent customers with a carefully designed set of hypothetical product profiles (defined by the specified levels of the relevant attributes), and collecting their preferences in the form of ratings, rankings, or choices for those profiles.

Since the introduction of conjoint analysis in marketing research over four decades ago, a remarkable variety of new models and parameter estimation procedures have been developed. Some of these include the move from nonmetric to metric orientation and orthogonal experimental designs in the 1970s, developments in choice-based and hybrid conjoint including adaptive conjoint model in the 1980s, the growing popularity of hierarchical Bayesian and latent class models in the 1990s, and the adaptability of conjoint models to online choice tasks, incentivized contexts, group dynamics, and social influences in the past decade. Several earlier review articles in marketing and consumer academic research have documented the evolution of conjoint analysis. Footnote 2 This manuscript provides an organizing framework for this vast literature and reviews key articles, critically discusses several advanced issues and developments, and identifies directions for future research. Cognizant of the fact that conjoint analysis has matured, this review is selective in the choice of articles, some classic but mostly contemporary focusing on the developments during the period post-2000; that have made or have the potential for having maximal impact in the field. Hopefully, this interdisciplinary review will encourage conjoint scholars to evolve beyond existing conjoint models and explore new problems and applications of consumer preference measurement, develop new forms of data collection, devise new estimation procedures, and tap into the dynamic nature of this methodology.

2 An Organizing Framework for Conjoint Analysis

The developments in conjoint research have naturally drawn from a variety of disciplines (notably choice behavior and statistical theory). The conceptual framework shown in Fig.  1 attempts to integrate various threads of research across five major categories: ( A ) Behavioral and Theoretical Underpinnings , ( B ) Researcher Issues for Research Design , ( C ) Respondent Issues for Data Collection , ( D ) Researcher Issues for Data Analysis , and ( E ) Managerial Issues Concerning Implementation . This framework considers all three relevant stakeholders: the researcher, the respondent, and the manager.

A framework for organizing contemporary research in conjoint analysis

3 (A) Behavioral and Theoretical Underpinnings

3.1 a1. behavioral processes in judgment, preference, and choice.

The developments in the judgment and decision-making research offers great potential for conjoint analysis to better understand the behavioral processes in judgment, preference, and choice. We now know how, and increasingly, why characteristics of task and choice option guide attention and how internal memory and external information search affect choice in path-dependent ways. Footnote 3 Recent research illustrates that preferences are typically constructed rather than stored and retrieved [ 111 ].

Judgments and choices typically engage multiple psychological processes, from attention-guided encoding and evaluation to retrieval of task-relevant information from memory or external sources, including prediction, response, and post-decision evaluation and updating. Attention is more important in decisions from descriptions (e.g., the full-profile approach of conjoint analysis) whereas memory and learning is more relevant in decisions from experience through trial and error sampling of choice options [ 77 ]. Footnote 4 On the other hand, in decisions from experience, recent outcomes are given more weight and rare events get underweighted. In a similar vein, the insight that evaluation is relative from prospect theory continues to gain support [ 184 ]. Since neurons encode changes in stimulation, rather than absolute levels, absolute judgments are much more difficult than relative judgments. Relative evaluation includes other observed or counterfactual outcomes from the same or different choice alternatives, as well as expectations.

Also relevant to conjoint analysis are the recent extensions of decision field theory (DFT) and models of value judgment in multiattribute choice [ 94 ]. In these models, attributes of choice alternatives are repeatedly randomly sampled and each additional acquisition of information increases or decreases the valuation of an alternative in a choice set, ending when the first option reaches a certain threshold. DFT as a multilayer connectionist network has also been applied to explain context effects such as similarity, attraction, and compromise effects [ 143 ]. For instance, conjoint models that capture compromise effect result in better prediction and fit compared to traditional value maximization models [ 102 ].

While stimulus sampling models typically assume path-independence, choice models are often biased toward early-emerging favorites resulting in reference-dependent subsequent evaluations [ 97 ] and distortion of the value of options, i.e., decision by distortion effect [ 16 , 158 ]. Also, studies of anchoring suggest that the priming of memory accessibility (and hence preference) can be changed by asking a prior question and remains strong in the presence of incentives, experience, and market feedback [ 9 ]. Not only are there short-term changes, but long-term effects on memory have been shown; for example, measuring the long-term effects of purchase intentions on memory and subsequent purchases [ 28 ].

The recently developed query theory (QT) [ 95 ] on preference construction is a process model of valuation describing how the order of retrievals from memory plays a role in judging the value of objects, emphasizing output interference. Weber et al. [ 185 ] show that the queries about reasons supporting immediate versus delayed consumption are issued in reverse order thus making the endowment effect disappear. Similar to Luce’s choice axiom, the support theory (ST) [ 176 ] is a model of inference about the probability of an event that uses the relative weight of what we know and can generate about the event (its support) and compares it to what we know and can generate about all other possible events [ 184 ]. Since competing hypotheses are often generated by associative memory processes from long-term memory, irrelevant alternative hypotheses may well be generated and occupy limited-capacity working memory [ 52 ]. This has implications for conjoint research as consumers with greater working memory capacity can include more alternative hypotheses (i.e., explicit disjunctions) and thus greater discrimination and lower judged probability of the focal brand being chosen.

Recently, the dual process models of System 1 and System 2 processes proposed by Kahneman [ 97 ] have gained popularity. Psychological models have distinguished between a rapid, automatic and effortless, associative, intuitive process (System 1) and a slower, rule-governed, analytic, deliberate, and effortful process (System 2). The extent and the process in which the two systems interact [ 57 ] is a topic of debate. Both cognitive and affective mechanisms have been demonstrated to give rise to the discounting of future events such as in delayed versus immediate consumption [ 185 , 178 ]. These theories have implications for conjoint data collection for technology products or durable goods.

3.1.1 Suggested Directions for Future Research

While time-discounted utility models are useful in inter-temporal choice, there is also a need to incorporate various behavioral effects in conjoint models. Conjoint modelers can extend and augment inter-temporal utility specifications by using temporal inflation parameters representing differences in “internal noise” used by behavioral researchers. An example is the recent critique by Hutchinson et al. [ 86 ] of the theoretical assumptions made by Salisbury and Feinberg’s [ 150 ] stochastic modeling of experimental data, where they empirically tested alternate models of choice and judgment with respect to assumptions relating to “internal noise” and “uncertainty about anticipated utility” as well as the stochastic versus deterministic nature of the scale parameters.

Conjoint analysts can develop utility models that extend prospect theory and neuron-encoded relative judgments to better understand how consumers select reference points and how multiple reference points might be used in relative evaluation [ 184 ]. For instance, individual heterogeneity can be captured through a distribution of reference points rather than a single reference point such as reference price [ 50 ].

Decision-makers may pay equal attention to all possible outcomes than is warranted by their probabilities and linger at extreme outcomes to assess best and worst choices in choice-based conjoint studies. Cumulative prospect theory that explains the evaluation of outcome probabilities relative to its position in the configuration of outcomes [ 175 ] can provide a useful avenue for research for this problem.

The power of affect, feelings, and emotions in consumer judgment, preference, and choice is now well established [ 118 ]. Future conjoint research should incorporate the mechanisms of the dual process model, i.e. System 1 and System 2 models [ 97 ]. Also, decision affect theory provides a framework that incorporates emotional reactions to counterfactual outcome comparisons such as regret or loss aversion [ 34 ]. In a risky choice situation, the fit with self-regulatory orientation can also transfer as affective information into the choice task which could be modeled [ 78 ].

3.2 A2. Compensatory Versus Noncompensatory Processes

Much of the conjoint research assumes that the utility function for a choice alternative is additive and linear in parameters. Footnote 5 The implied decision rules are compensatory. Generally speaking, linear compensatory choice models do not address simplifying choice heuristics such as truncation and level focus that can result in an abrupt change in choice probability. Yet, noncompensatory simple heuristics are often more or at least equally accurate in predicting new data compared to linear models that are criticized for over-fitting the data [ 67 , 89 , 103 ]. While the linear utility model has been the mainstay in conjoint research, Bayesian methods, including data augmentation, can easily accommodate nonlinear models and can deal with irregularities in the likelihood surface [ 6 ]. Recently, Kohli and Jedidi [ 103 ] and Yee et al. [ 190 ] propose dynamic programming methods (using greedy algorithm) to estimate lexicographic preference structures.

Noncompensatory processes are particularly relevant in the context of consideration sets, an issue typically ignored by the traditional conjoint research (e.g., [ 67 , 91 ]). Many advocate a noncompensatory rule for consideration and a compensatory model at the choice stage (consider-then-choose rule), albeit some critics question the existence and parsimony of a formal consideration set (see Horowitz and Louviere [ 81 ] who find the same utility function at the two-stage versus one-stage only). For instance, in a study estimating consideration and choice probabilities simultaneously, Jedidi et al. [ 91 ] find that both segment-level and individual Tobit models perform better than the traditional conjoint model which ignores both consideration as well as error component in preference. Similarly, Gilbride and Allenby [ 67 ] estimate a two-stage model using hierarchical Bayes methods, augmenting the latent consideration sets within their MCMC approach. Recently, Hauser et al. [ 76 ] propose two machine-learning algorithms to estimate cognitively simple generalized disjunctions-of-conjunctions (DOC) decision rules, and Liu and Arora [ 114 ] develop a method to construct efficient designs for a two-stage, consider-then-choose model. Footnote 6 Stuttgen et al. [ 164 ] propose a continuation of the line of research started by Gilbride and Allenby [ 67 ] and Jedidi and Kohli [ 89 ] that does not rely on compensatory trade-offs at all. These finding are consistent with economic theories of consideration set wherein consumers balance search costs with option value of utility maximization to achieve cognitive simplicity.

3.2.1 Suggested Directions for Future Research

It seems that combining lexicographic and compensatory processes in a two-stage model using the greedoid algorithm in the first stage is a promising research route to follow as it enhances the ecological rationality of preference models (see [ 67 , 103 ], and [ 89 ]).

Several interesting behavioral processes such as the formation and dynamics of the consideration set still need to be understood. Given the technological advances (i.e., eye-tracking technology) in dealing with noncompensatory processes and satisficing rules, it behooves conjoint researchers to adapt such methods in the future (see [ 164 , 173 ], and [ 157 ]).

Knowledge about cue diagnosticity Footnote 7 and take-the-best (TTB) strategy performs really well when the distribution of cue validities is highly skewed. Several other heuristics have also performed well including the models that integrate TTB and full information [ 80 , 109 ]. We encourage conjoint researchers to incorporate cue diagnosticity in estimating noncompensatory models.

While the recognition heuristic (RH) for inference in cases in which only one of two provided comparison alternatives is recognized as a useful tool, the debate is whether recognition is always used as a first stage in inference or whether recognition is simply one cue in inference that can be integrated (see [ 132 , 142 ]) without any special status. For future research, RH can be potentially applied in conjoint-choice contexts that are characterized by rapid, automatic, and effortless processes (i.e., System 1 process [ 97 ]) typical in low-involvement routine products.

3.3 A3. Integrating Behavioral Learning and Context Effects

Conjoint analysis has made some significant gains in incorporating behavioral theory into preference measurement. Recently, Bradlow et al. [ 20 ] investigated how subjects impute missing attribute levels when they evaluate partial conjoint profiles using a Bayesian “pattern-matching” learning model. Respondents impute values for missing attributes based on several factors including their priors over the set of attribute levels, a given attribute’s previously shown values, the previously shown values of other attributes, and the covariation among attributes. Alba and Cooke [ 3 ] critique that not all attributes are spontaneously inferred and even when inference is natural, symmetry may be violated such that the probability of imputing cause (e.g., quality) from effect (e.g., price) may deviate from the probability of imputing effect from cause. When information is intentionally retrieved, the weighting function may reflect uncertainty about the accuracy of the profiles or the ability to retrieve them.

There is substantial research in conjoint analysis to demonstrate context effects. Conjoint models in marketing research have assumed stable preference structures in that preferences at the time of measurement are the same as at the time of trial or purchase. However, context effects produce instability when the context at measurement does not match the context at decision time [ 15 , 110 ]. DeSarbo et al. [ 45 ] introduced a Bayesian dynamic linear model (DLM)-based methodology that permits the detection and modeling of the dynamic evolution of individual preferences in conjoint analysis that occur during the task due to learning, exposure to additional information, fatigue, cognitive storage limitations, etc. (see [ 113 ]). Also, see Rutz and Sonnier [ 149 ] for Bayesian modeling (i.e., DLM method) of dynamic attribute evolution due to market structural changes for more details.

Kivetz et al. [ 102 ] find that incorporating the “compromise effect” leads to superior predictions and fit compared with the traditional value maximization model. Recently, Levav et al. [ 110 ] demonstrated using experimental studies that normatively equivalent decision contexts can yield different decisions, which challenges the assumption that people maximize utility and possess a complete preference ordering. This type of research attempts to bridge consumer psychology with marketing science. Other related work involving dynamic preference structures include Netzer et al. [ 129 ], Evgeniou et al. [ 59 ], Bradlow and Park [ 19 ], Fong et al.[ 64 ], Ruan et al. [ 148 ], Rooderkerk et al. [ 145 ], De Jong et al. [ 38 ], and Elrod et al. [ 56 ].

3.3.1 Suggested Directions for Future Research

There is clearly a need for more rigorous work to incorporate behavioral effects in preference measurement. While this may create a conflict between isomorphic goal of fit and paramorphic goal of predictive validity [ 130 ], a greater dialogue and collaboration between the two research camps is essential for improved quality of conjoint research.

Future research in this conjoint arena should examine other documented behavioral effects such as asymmetric dominance, asymmetric advantage, enhancement, and detraction effects (see [ 2 ]).

Since preference formation is a dynamic process dependent on learning and context effects, future researchers should attempt to further develop and use flexible models and dynamic random-effects models such as those used by Liechty et al. [ 113 ], and Bradlow et al. [ 20 ]. Many of the flexible models developed to capture dynamics in repeated choice (e.g., [ 107 ]) could be adapted to conjoint preference measurement.

It would be worthwhile to investigate how choice probabilities change in choice-based conjoint and choice simulators when context effects and consumer expertise are built directly into the model as these may affect the likelihood and form of missing attribute inference [ 3 ].

3.4 A4. Group Dynamics and Social Interactions

A vast majority of choice models assume that a consumers’ latent utility is a function of brand attributes, and not the preferences of referent others. However, some scholars have examined the influence of referent others in a dyadic and network context. For instance, Arora and Allenby [ 10 ] develop a Bayesian model to estimate attribute-specific influence of spouses in a decision-making context and discuss how and whom marketers can target communication messages effectively. Using a Bayesian autoregressive mixture model, Yang and Allenby [ 189 ] demonstrate that preference interdependence due to geographically defined networks is more important than demographic networks in explaining behavior. Ding and Eliashberg [ 47 ] proposed a new model that explicitly considers dyadic decision-making in ethical drug prescriptions in the context of physician and patients. The issue of reducing hypothetical biases (e.g., socially desirable responses [ 48 ]) in group dynamics through innovative methodology, such as incentive-aligned conjoint studies, is critical.

Some exciting work has started using conjoint models in the domain of group dynamics and social interactions [ 129 , 29 , 68 , 88 , 127 , 159 ]. Footnote 8 With the availability of “sentiment analysis” tools, firms are now able to extend beyond ratings data and capture a torrent of online textual communications from a variety of social media including blogs, chat rooms, new sites, YouTube, and Twitter. Footnote 9 Recently, Sonnier et al. [ 159 ] using the web crawler technology and automated classification of sentiments were able to demonstrate that positive and negative comments increased the dynamic stock while negative comments decreased it and that such effects are masked when the comment volume is aggregated across valence. Based on the theory of social contagion [ 88 ], Narayan et al. [ 127 ] study the behavioral mechanisms underlying peer influence affecting choice decisions and find that consumers update their inherent attribute preferences in a Bayesian manner by utilizing the relative uncertainty of their attribute preference and that of their peers and use peer choices as an additional attribute. This particular study is significant as the authors mitigate problems of endogeneity, correlated unobservable variables, and simultaneity by setting up a preinfluence and post-influence conjoint experimental design. Most recently, Kim et al. [ 101 ] introduced a holistic preference and concept measurement model called PIE for conjoint analysis which is a new incentive-aligned data collection method which allows a consumer to obtain individualized shopping advice through other people.

3.4.1 Suggested Directions for Future Research

In the promising domain of group dynamics and social interactions for technology-based products, one important research question would be to ask what role can internal versus external motivations of online information disseminators play in changing the posterior beliefs and preference structure of consumers [ 69 ]? For example, very little is known as to what motivates opinion leaders and early adopters to not just possess but share information with others.

There is a vast potential for conjoint models to draw from consumer research on reference group formation and social influences on buyer choice behavior such as internalization, identification, and compliance [ 141 , 156 ]. In this area, barter conjoint offers a promising potential to model the effects of information diffusion among subjects and how endowment and loss-aversion effects [ 101 , 22 , 49 ] induce individuals to behave differently than conventional choice behavior.

We issue a call for scholars to explore further developments in conjoint models that capture online recommender systems and social interactions given the rising importance of social media [ 32 ]. Existing algorithms using Classification and Regression Trees, Bayesian Tree Regression, and Stepwise Componential Regression can be further combined to develop an optimal sequence of questions to predict online visitor’s preference [ 37 ]. Additional research into problems involving multiple decision makers with multiple utility functions (e.g., in business-to-business applications) would prove valuable.

4 (B) Researcher Issues for Research Design

Conjoint researchers have long dealt with the problem of large number of attributes and levels with the help of experimental designs. The specific choice will depend on a variety of factors including objectives of the research, cost, time, statistical sophistication, and the need to develop individual-level estimates, etc. We focus on the research designs related to conjoint approaches that are more popular: choice-based conjoint analysis, menu-based experimental choice, and maximum difference best/worst conjoint method. We also briefly discuss some recent developments in experimental design and handling of large number of attributes.

4.1 B1. Choice-Based Conjoint Analysis

Choice-based conjoint (CBC) analysis describes a class of hybrid techniques that are among the most widely adopted market research methods for conjoint analysis (see [ 137 ]). Footnote 10 The early choice-based hybrid models used stage-wise regression, compositional models to fit self-explicated data, and the decompositional model at the segment level. However, hybrid models were later extended to allow for parameter estimation at the individual level using self-explicated data for within-attribute part-worth estimation, and using the full-profile approach for improving estimates of attribute importance.

Recent developments have allowed for estimation at the individual level through Bayesian estimation [ 71 , 167 ], even though a respondent provides only a small amount of information within CBC. In the same vein, it is not clear whether segments obtained from CBC are similar to those found from post hoc clustering of part-worths [ 25 ]. One aspect of choice-based models, particularly with the development of multinomial logit estimation procedures, is the property of independence of irrelevant alternatives (IIA) that forces all cross-elasticities to be equal. However, researchers have developed ways to deal with the IIA assumption by employing mixed-logit or random-parameters logit that allows for flexible variance-covariance structures. Building on recent work by Louviere and Meyer [ 116 ] and Louviere et al. [ 117 ], Fiebig et al. [ 61 ] argue that much of the heterogeneity in attribute weights is accounted for by a pure scale effect (i.e., holding attribute coefficients fixed, the scale of the error term is greater) leading to scale heterogeneity MNL model. Also noteworthy is the recent development in detecting and statistical handling of attribute nonattendance in which respondents focus on a subset of attributes only in choice-based conjoint. Scarpa and colleagues use two different panel mixed-logit models to account for response pattern of repeated exclusion that influence model estimation (see [ 154 ], [ 155 ], and [ 24 ]).

4.1.1 Suggested Directions for Future Research

Several marketing scholars (see [ 130 ], [ 70 ], and [ 83 ]) identified the importance of advanced research into the direct modeling of behavioral effects on decision-making and choice (e.g., in choice-based conjoint analysis). The research issues include understanding of such behavioral phenomena as self-control, context effects, inattention, or reference dependence. The embedding of meta-attributes such as expectations, goals, motivations, reference groups, and social networks might also prove gainful in conjoint analysis.

Another potential area of study is the modeling of individual-level structural heterogeneity. More specifically, are there some combination of attribute levels that create a change in the structure of the utility function utilized by a specific consumer? While conjoint scholars have explored compensatory vs. noncompensatory models for a given choice-based conjoint task, work involving potential regime shifts during the task by consumer would prove insightful (see [ 63 ]).

4.2 B2. Menu-Based Experimental Choice

In menu-based conjoint analysis, customers are asked to pick several features from a menu of features or products that are individually priced. If the utility of each feature is above a certain threshold, it is chosen and the utilities of all the chosen features are maximized simultaneously resulting in multiple chosen alternatives [ 112 ]. The responses therefore entail a binary vector of choices for each respondent for each of the menu scenarios in the experiment. This is quite akin to choosing a bundle of items [ 31 ] from a larger set or designing a product using a product configurator as buying, for instance, a Dell laptop. Configurators represent a promising form of conjoint data collection in which the respondent self-designs the best product configuration [ 112 ]. Recently, Levav et al. [ 110 ] argue that in a mass customization decision (such as using a configurator), consumers can often lose their self-control in assessing utility correctly in repeated choice situations due to bounded rationality and the depletion effects of their mental resources [ 181 ]. Dellaert and Stremersch [ 40 ] borrowing from choice theory and task complexity theory also demonstrated that consumers’ product utility had a positive effect on mass customization utility while task complexity had a negative effect, albeit lower for experts.

In addition to the many menu choices that it generates, menu-based choice represents a modeling challenge that is distinct from the traditional single-choice analysis of data from choice-based conjoint experiments—e.g., using multinomial logit models or multinomial probit models. The Bayesian modeling approach in this context, entailing a constrained random-effects multinomial probit model [ 112 ], incorporates constraints in menu choices (e.g., firm-level design or production constraints) as well as heterogeneity in customers’ price sensitivities and preferences for the variety of customized options a firm can offer. In this multiple choice modeling scenario, researchers can assess the intrinsic worth of each feature, their price sensitivities, and model correlations among them for each individual. Web-based menus would allow firms to offer mass-customized services with every potential customer visiting their web site.

4.2.1 Suggested Directions for Future Research

Given the ability of menu-based conjoint to provide individual-level information and the growing reality of web-based mass customization, we encourage researchers to further study customer heterogeneity in demand and new channels of information exchange to maximize customer value.

Conjoint scholars can add to our understanding of mass-customized choice processes by explicating individual traits, task factors, and decision strategies that influence customization complexity. To further refine the model, future conjoint scholars can incorporate a more general distance model that can explicitly account for the relative differences between attribute levels, unlike the 0–1 pattern-matching model (see [ 20 ]). This can be accomplished by combining conjoint analysis and MDS to impute missing attribute levels. When the number of attributes is large, mapping between attributes and some higher-order dimensions can be developed (i.e., conjoint utility functions) a la MDS methods. Methods of reverse mapping can yield part-worth values for the original attributes. But, this approach needs to be developed and validated.

One other promising line of research here would be to study whether consumers enjoy mass customizing a product or service, and at what levels of complexity will they make suboptimal choices. It is possible that consumers also overspend their mental capacity early in the configuration sequence triggering a tendency to accept the default alternative in subsequent decisions, even when such decisions involve few options that would require less capacity to evaluate. A related issue in need of further investigation is minimizing the dysfunctional consequences of information overload in conjoint studies.

4.3 B3. Maximum Difference Scaling—Best/Worst Conjoint

Based on a multinomial extension of Thurstone’s model for paired comparisons, Finn and Louviere [ 62 ] developed a univariate scaling model (MaxDiff) that can be utilized to measure brand-by-attribute positions, develop univariate scales from multiple measures, etc. Swait et al. [ 166 ] describe how to generalize or extend MaxDiff to conjoint applications which they call Best/Worst conjoint analysis or B/W. In the B/W method respondents choose the two attribute levels which are, respectively, “best” and “worst” for each product profile. With such data, the method enables the estimation of separate attribute effects for each attribute independently of its part-worths. This is an important advantage over the traditional additive conjoint and choice models that do not allow for such separation [ 166 ]. B/W experiments have also been found to contain less respondent error than choice-based conjoint models containing the same attributes and levels [ 166 ]. Other advantages include allowing for ties in evaluations unlike ranking tasks and a more discriminating way to measure attribute importance than either rating scales or the method of paired comparisons. Also, it has greater predictive validity as an importance measurement than either ratings scales or the method of paired comparisons. B/W measurements are scale-free and thus ideal for comparison across different cultural groups that use scales quite differently [ 33 ] without any need to make prior assumptions regarding the scaling of evaluation and choice. Consequently, maximum difference scaling has been used extensively in Best/Worst Conjoint Analysis. However, some limitations include evaluating both positive and negative attributes, effects of having only best or worst features versus best and worst, collinearity, and sequence effects, among others. For example, MaxDiff results are shown to be less accurate at the “best” end but augmentation (e.g., Q Sort) improves MaxDiff results on “best” items [ 53 ].

4.3.1 Suggested Directions for Future Research

Best/Worst allows for ties in evaluations and for skewed preference functions, unlike ranking tasks. Whether or not B/W and choice-based conjoint produce equivalent part-worth utilities, after adjusting for the difference in respondent error, is currently unknown as the results have been mixed [ 166 ]. More research is needed to further validate the B/W method.

More recently, Marley and Louviere [ 121 ] have developed several different probabilistic B/W choice models: the Consistent Random Utility B/W choice model, the MaxDiff model, the biased MaxDiff model, and the concordant B/W choice model (see also [ 122 ]). However, questions remain about whether the B/W method can be used in accordance with the random utility theory. A related question is whether the judgments respondents make in a B/W task could be used as though they had made in an alternative-based choice, ranking, or rating using compensatory rules.

4.4 B4. Developments in Experimental Design

Rating-based methods in marketing conjoint studies have frequently utilized resolution III designs (or orthogonal arrays), which assume that some main effects are confounded with some two-level interactions. In general, orthogonal designs for linear models are efficient as measured by A-, D-, and G-efficiency computed from eigenvalues of the \( {\left(X\hbox{'}X\right)}^{-1} \) matrix (recently, Toubia and Hauser [ 169 ] proposed the criterion of managerial efficiency, M-efficiency, as well). Kuhfeld [ 106 ] showed that the OPTEX procedure produces more efficient designs; however, it fails to achieve the perfect level balance or the proportionality criteria of orthogonal arrays. In the case of choice-based conjoint methods, Huber and Zwerina [ 85 ] show that achieving utility balance increases the efficiency. Building on their work, Sandor and Wedel [ 151 ] develop Bayesian-based efficient designs (through relabeling, swapping, and cycling) that minimize the standard errors with higher predictive validity. Subsequently, Sandor and Wedel [ 152 ] develop efficient designs that are optimal for mixed-logit models by evaluating the dimension-scaled determinant of the information matrix of the mixed multinomial logit model. Because choice-based conjoint model is nonlinear, both minimal overlap and utility balance in the choice set are desirable. Rose et al. [ 146 ] extend the Sandor and Wedel study to construct statistical S-efficiency that optimizes Bayesian designs for a given sample size based on parameter values, random-parameters logit mixing distributions, and model specifications [ 146 , 99 ]. However, the trade-off is that choice task difficulty typically is accompanied with greater measurement response error, and thus a lower response R-efficiency.

Despite several developments, some limitations remained, such as the need to obtain repeated observations from each respondent, the use of aggregate-customization design that was optimal for the average respondent only, and the challenge of computing ordinary Fisher’s information matrix. This was later partly addressed by Sandor and Wedel [ 153 ] who used a small set of different designs for different consumers to capture respondent heterogeneity. Recently, Yu et al. [ 191 ], using the generalized Fisher information matrix, proposed an individually adapted sequential Bayesian approach to generate a conjoint-choice design that is tailor-made for each respondent. The method is superior both in estimation of individual-level part-worths (and population-level estimates) and choice prediction compared to benchmarks such as aggregate-customization and orthogonal design approaches. Further, this method is less sensitive to low-response accuracy as compared to the polyhedral method proposed by Toubia et al. [ 171 ] and their subsequent adapted method [ 172 ]. New developments are also emerging in the area of choice set designs with forced choice experiments. For example, Burgess and Street [ 21 ] developed procedures to construct near-optimal designs to estimate main effects and two-level interactions with a smaller numbers of choice sets and they derive the relevant mathematical theory for such designs; see [ 21 , 163 , 161 , 162 ] for detailed descriptions.

4.4.1 Suggested Directions for Future Research

Newer methods of adaptive questions based on active machine-based learning method are proving very successful over market-based, random, and orthogonal-design questions when consumers use noncompensatory heuristics; see Abernethy et al. [ 1 ] and Dzyabura and Hauser [ 54 ]. We encourage more research along this direction.

The trade-off between S- and R-efficiency is an interesting issue to resolve going forward. While greater S-efficiency yields smaller variance, increasing R-efficiency by reducing task complexity with attribute overlap reduces S-efficiency. While inconclusive, more research needs to be done whether efficient experimental designs contribute more to the precision of choice model estimates in light of task complexity (see [ 99 ]).

4.5 B5. Handling a Large Number of Attributes

A comprehensive review of various methods for dealing with large number of attributes is available in Rao et al. [ 140 ]. Several scholars are currently working on the issue of handling large numbers of attributes [ 35 , 128 ]. For instance, Dahan [ 35 ] simplified the conjoint task (using Conjoint Adaptive Ranking Database System) by asking respondents to choose only among a very limited number of sets that are perfectly mapped to specific utility functions proposed in advance by the researcher. Park et al. [ 134 ] proposed a new incentive-aligned web-based upgrading method for eliciting attribute preferences in complex products (e.g., cameras); this method enables participants to upgrade one attribute at any level from a large number of attributes allowing for dynamic customization of the product. Their empirical application shows that the upgrading method is comparable to the benchmarked self-explicated approach, takes less time, and has a higher external validity.

Recently, Netzer and Srinivasan [ 128 ] proposed a web-based adaptive self-explicated (ASE) approach to solve the self-explicated constant sum question problem when the number of product attributes becomes large. The ASE method breaks down the attribute importance question into a ranking of the attributes followed by a sequence of constant sum paired comparison questions for two attributes at a time thus replacing the importance measurement stage of the traditional self-explication model. The attribute importance is estimated by using a log-linear regression model (with OLS estimation) which gives the benefit of estimating standard errors as well. The ASE method significantly and substantially improved predictive validity as compared to the self-explication model, adaptive conjoint analysis, and the fast polyhedral method.

As with the large number of attributes problem, researchers should also consider the number-of-levels effect. As the number of intervening attribute levels increase, the derived importance of an attribute also increases. Prior studies have linked this phenomenon to data collection methodology, measurement scale of the dependent variable, and parameter estimation procedures [ 179 ], but results are somewhat inconclusive. More recently, De Wilde et al. [ 39 ] explain this phenomenon by focusing on selective attention, and argue that attentional contrast directs attention away from redundant attribute levels and toward novel attributes in sequential evaluation procedure (e.g., in traditional full-profile conjoint analysis and choice-based conjoint).

4.5.1 Suggested Directions for Future Research

The search for methods for coping with large number of attributes has been identified as one of the key areas for future research [ 18 ]. An approach that holds promise is to have subsamples of respondents provide data on a subset of attributes with some linkages among the sets as in bridging conjoint analysis. Hierarchical Bayesian methods can then be applied to such data to estimate part-worths at the individual level. We encourage conjoint scholars to further advance this line of research.

Given scant research, there is a need for studies, using simulations as well as empirical data, to compare the relative efficacy of the different methods in handling large number of attributes. Future research should assess how measurement technique, attribute representation, and experimental design will influence the relative novelty of an attributes’ levels at the time of measurement. Further, conjoint scholars should engage in developing algorithms that are sensitive to level balance across attributes, especially for unbalanced designs.

5 (C) Respondent Issues for Data Collection

Over the years, conjoint research has focused either on preference ratings (or rankings) of a number (between a dozen to thirty) of carefully designed product profiles (a la ratings-based methods) or on stated choice for each of several choice sets of product profiles, including a no choice option. When the number of attributes becomes large (i.e., over six), methods such as adaptive methods or partial profile methods have been employed. These approaches have come to a stable situation. Not many research issues seem to exist in this arena. Rather, we will focus on newer methods such as using incentive alignment and willingness to pay, barter conjoint and conjoint poker, meta-attributes and complexity of stimuli, and the role of no-choice option given their recent development and future research potential.

5.1 C1. Incentive Compatibility and Willingness to Pay

Ding et al. [ 48 ] found strong evidence in favor of incentive-aligned choice conjoint in out-of-sample predictions and a more realistic preference structure that exhibited higher price sensitivity, lower risk-seeking behavior, and lower susceptibility to socially desirable behaviors. This development has cast doubt on the assumption that purchase intent and choice are related in stated preference data. However, a real challenge is for researchers to implement incentive alignment in really new or complex products when it is not cost effective to offer real product to each participant or to generate all product variations.

Dzyabura and Hauser [ 54 ] addressed the cost issue by implementing an active machine-learning algorithm which approximates the posterior with a distributional variation and uses belief propagation to update the posterior distribution. The questions are selected sequentially to minimize the expected posterior entropy by anticipating the potential responses, i.e., to consider or not to consider. Their study confirms that consumers use cognitively simple heuristics with relatively fewer aspects and that the adaptive questions search the space of decision rules efficiently. Ding [ 46 ] addressed the issue of “all product variations” by developing a truth-telling mechanism by incentivizing conjoint participants which becomes the Bayesian Nash Equilibrium. The BDM procedure ensures that it is in the best interest of a participant to have his or her inferred willingness to pay equal to his or her true willingness to pay.

Conjoint methods are typically used for measuring the willingness to pay (WTP). WTP becomes more relevant in the context of incentive-aligned upgrading of attributes [ 134 ]. Wathieu and Bertini [ 183 ] used categorization theory to argue that a moderately incongruent price differential is more likely to induce deliberation when a new benefit is added or augmented beyond consumer expectations. Dong et al. [ 51 ] proposed a Rank Order mechanism that predicts preferences for a list of reward products, instead of an individual’s monetary value for one product, and gives or sells the top-rated one to the respondent. They recommend the WTP mechanism when there is only one real product and price can be estimated from preference measurement task; and the Rank Order method when two or more real versions of the product are available regardless of whether or not WTP can be estimated.

The contingent valuation method, typically used to determine the WTP for a nonmarket good, is subject to exaggeration bias which stems from factors such as new product enthusiasm, an attempt to influence the decision to market the product, or a tendency to be less sensitive to total costs [ 93 , 180 ]. One approach is to calibrate the responses into quasi-real ones based on self-assessed certainty; however, the latter measure can also be fraught with survey bias. The second approach has been transforming the hypothetical WTP into real WTP assuming a functional relationship. Park and MacLachlan [ 133 ] propose an exaggeration bias-corrected contingent valuation method in which the individual compares the real WTP with an independent randomly drawn spurious WTP and then takes the larger one as his or her hypothetical WTP. The real WTP is only assumed to be related randomly with the hypothetical WTP rather than have a functional relation.

Voelckner [ 180 ] found significant and substantial differences between WTP reported by subjects when payment of the stated price is real or hypothetical. The author compared hypothetical and real WTPs across and within four methods of measuring WTP (i.e., first-price sealed bid auction, the Vickrey auction, contingent valuation, and conjoint analysis). There was evidence of overbidding bias as a result of perceived competitive pressure resulting in higher WTPs for auctions compared to methods based on stated preference data. Recently, Miller et al. [ 125 ] compared the performance of four approaches to measure WTP based on direct versus indirect assessment and hypothetical versus actual WTP with real purchase data. Their findings show that respondents are more price-sensitive in incentive-aligned settings than in nonincentive-aligned settings and in real purchase setting, and are better suited to assess WTP for product prototypes. Overall, recent developments in this domain have been very significant with a promising future outlook.

5.1.1 Suggested Directions for Future Research

While the Rank Order method of incentive compatibility has proven very valuable in motivating truth responses, there is still a need to sort out a host of issues such as desired versus undesired products to be included in the list, the incentive value of products, and whether the incentive list should be revealed before or after the conjoint task.

Given that WTP is a latent construct, research for its validation should be undertaken employing SEM methodology; for instance employing an induced value experiment that provides incentive-compatible estimates of WTP may come closest to mapping the true representation of WTP as a latent construct [ 134 , 46 ].

Giving respondents time to think (TTT) in a contingent valuation study by designing a quasi-experimental study that mimics realistic decision contexts may alter the WTP. How does information and time affect responses to contingent valuation conjoint studies? This is an excellent opportunity for bridging research in consumer psychology, marketing science, and environmental and information economics [ 27 ].

While WTP research typically focuses on estimating marginal rates of substitution (i.e., WTP for marginal changes in product attributes), there is potential scope for data enrichment by combining stated preference and revealed preference; the former providing robust estimates for substitutability and the latter providing robust estimates for predicting uptake behavior (see [ 126 ] for associated statistical estimation methodologies).

5.2 C2. Barter Conjoint and Conjoint Poker

Barter conjoint approach collects substantially larger amount of pairwise data (offers submitted or not and the responses to offers received) without demanding much additional effort, as well as potentially improving the quality of data by allowing information diffusion among participants during preference measurement. Ding et al. [ 49 ] using two studies and two holdout tasks found that the barter conjoint significantly outperformed both incentive-aligned and hypothetical CBC in out-of-sample prediction. Toubia et al. [ 173 ] recently developed and tested an incentive-compatible conjoint poker game and compared it with incentive-compatible choice-based conjoint using a series of experiments. Their findings indicate that conjoint poker induces respondents to consider more of the profile-related information presented to them (i.e., greater involvement and motivation) as compared with choice-based conjoint. Similar to the incentive-compatible mechanisms that add motivation to respondents [ 48 ], conjoint poker motivates respondents toward truth telling.

5.2.1 Suggested Directions for Future Research

Future research in these relatively new approaches could be developed in a number of different directions. For example, applications of barter and poker methods could also be tested for products that are less desirable, allowing for increases or decreases in group assignments, and/or allowing for multiple trades.

There is the restriction that the barter requires synchronized implementation and simultaneous bartering which makes online conversion somewhat cumbersome. Future barter research should examine newer procedures that do not tend to promote possible endowment and loss-aversion effects. Finally, the current estimation method does not model any dynamic effects in preference formation despite the various stages of the barter.

5.3 C3. Meta-Attributes and Complexity of Stimuli

Conjoint researchers need to recognize that consumers often think of products in terms of “meta-attributes” including needs, motivations, and goals which may correspond to bundles of attributes [ 130 ]. Research in judgment and decision-making has incorporated the role of multiple goals and how situational and task factors including goal-framing effects [ 123 ] activate and chronically elevate their accessibility which in turn determine decision rules—e.g., deontological goal of “what is right”, consequentialist goal of “what has the best outcomes”, versus affective goal of “what feels right” [ 13 ]. Also, consequences associated with an attribute that is central in consumers’ hierarchy of goals are likely to generate primary appraisals [ 118 ]. These meta-level preferences can impact decision-making and they tend to be more stable than context-specific preferences. We know that customers think of products in terms of meta-attributes and hierarchy of goals, and that attributes that serve a consequentialist goal are more likely to be accessible and appraised [ 118 , 130 ].

In the context of complex stimuli, i.e., really new products, the role of uncertainty and consumer learning mechanisms through mental simulation and analogies is critical. Some advances have been made in this domain (see [ 73 , 79 ]), but the results are still preliminary. In a related vein, there is also evidence of inconsistency between the importance of attributes as estimated in value-elicitation surveys (i.e., stated preferences) and those implied by actual choices (i.e., revealed preferences). Horsky et al. [ 82 ] empirically demonstrate that attributes may be differentially weighted in stated preference versus actual choice as a function of their tangibility, such that tangible and concrete attributes are weighted more heavily in choice since consumers are under pressure to justify their decisions. Going forward, we offer the following issues for future research.

5.3.1 Suggested Directions for Future Research

One big challenge is to conceptually map the relationship between physical (i.e., concrete) attributes and meta-attributes in a way that can be translated into product design specifications. Some concrete attributes may lose their meaning when interpreted at a higher level of abstraction and generality, thus undermining the validity of responses [ 31 ].

The other challenge is methodological, although some work in this domain has started using factor analysis, text mining, and tree-based methods (e.g., Classification and Regression Trees, Bayesian Tree Regression) as valuable tools in this respect [ 37 , 66 ]. While factor analysis is feasible, it lacks the ability to create maps between physical attributes and meta-attributes. We encourage continued research in this area.

5.4 C4. The Role of the No Choice Option

Parker and Schrift [ 135 ] argued that the mere addition of a no-choice option to a set changes the consumers’ judgment criteria from comparative judgment (i.e., attribute-based processing) to an evaluative judgment (i.e., alternative-based processing). Through a series of studies, the authors demonstrate that the mere addition of a no-choice option (i.e., rejectable choice set) leads to alternative-based recall (encoding and retrieval) and information processing, greater weights being given to attributes that are enriched (more meaningful when evaluated alone) and those that meet consumers’ minimum needs, and ultimately a change in preference. The perceived difference between alternatives will be increasingly smaller the further the attributes are from the consumers’ threshold. Consistent with the literature on context effects [ 15 ], this study confirms that consumers shift their preference structure between a forced choice context and a rejectable choice context and ultimately choice shares. It is conceivable that every decision a consumer makes has a no-choice option and conjoint scholars should design studies that add the no-choice option when it is feasible and salient for consumers. Further, Botti et al. [ 17 ] suggest that mostly all choices consumers make are restricted or constrained in some manner.

5.4.1 Suggested Directions for Future Research

Potential distortions as arising due to variations in choice sets need to be examined by-product/service class, type of experimental design, method of administration, etc. to fully understand the impact of the specific methodology selected to perform conjoint analysis.

A number of interesting subareas on the impact of choices made when a “no choice” option is included need further investigation. These include the frequency in which the “no choice” option is selected, the impact of “no choice” selection on estimated importance, and whether the choices are sequenced or staged (i.e., first consider, then decide to choose) [ 114 ].

6 (D) Researcher Issues for Data Analysis

Major developments in the estimation procedures relevant for the conjoint researcher include Hierarchical Bayesian, Latent Class, and Polyhedral Estimation approaches. Further, opportunities exist in integrating multiple sources of data to obtain robust conjoint results.

6.1 D1. The Hierarchical Bayesian (HB) Approach

The HB method of estimation is helpful in tackling the challenge in conjoint analysis to estimate accurate part-worths at the individual level without imposing excessive response burden on the respondents. HB methods have been known to improve on finite mixture-based individual-level estimates which tend to be more stable than estimates that are based on individual data [ 4 ]. Following earlier pioneering work, Footnote 11 Allenby et al. [ 5 ] utilized the Bayesian method and the Gibbs sampler to extend research by incorporating prior ordinal constraints on conjoint part-worths and found better internal cross-validation on the data. Often, there is a logical or practical ordering of the attribute levels that exists in the real world.

Subsequently, Srinivasan and Park [ 160 ] proposed a new method to optimize the full-profile design for a large number of attributes and provided a heuristic procedure to weigh together the part-worth estimates of the self-stated and full-profile data on a smaller number of core attributes. By differentiating between core and noncore attributes, they predicted preference for a new stimulus by using the optimal weight and conjoint part-worths for the core attributes and the self-explicated part-worths for the noncore attributes. Andrews et al. [ 8 ] showed that HB models performed well even when the part-worths came from a mixture of distributions and were robust to violations of the underlying assumptions. In almost all instances, the Bayesian method has been found to be comparable or even superior to the traditional methods both in part-worth estimation and predictive validity. Sandor and Wedel [ 153 ] demonstrated that heterogeneous designs which take into account Bayesian design principles of prior uncertainty and respondent heterogeneity showed substantial gains in efficiency compared with homogeneous designs. Heterogeneous designs consist of several subdesigns that are offered to different consumers and can be constructed with relative ease for a wide range of conjoint-choice models. Footnote 12

Ter Hofstede et al. [ 167 ] proposed a general model (finite mixture regression model) that includes the effects of discrete and continuous heterogeneity as well as self-stated and derived attribute importance in hybrid conjoint studies. As a departure from earlier studies, they treat self-stated importance as data rather than as prior information, and include them in the formulation of the likelihood thus helping them investigate the relationship of self-stated and derived importance at the individual level. Furthermore, the order constraints derived from the self-stated importances are “hard” constraints, ignoring the relative distance between importances and measurement error in the self-stated part-worths, which may result in the stated order differing stochastically from the “true” underlying order. Their study shows that including self-stated importance in the likelihood leads to much better predictions than does considering them as prior information. An excellent resource on HB methods in marketing and conjoint analysis can be found in Rossi et al. [ 147 ].

6.1.1 Suggested Directions for Future Research

It has not been conclusively demonstrated in what contexts consumer heterogeneity is better described by a continuous [ 4 ] or by a discrete distribution [ 44 ], pointing to a need for further research to resolve this issue (see also Ebbes et al. [ 55 ]). Still, we believe that the HB method is a preferred approach when a large number of part-worths need to be estimated compared to more classical methods of estimation that can use up a large number of degrees of freedom and where the likelihood function may have multiple maxima [ 84 , 138 ].

More research is required to examine the potential effects of distributional misspecification concerning the likelihood, prior, and hyper prior distributions in HB conjoint analyses (not just prior sensitivity).

6.2 D2. The Latent Class Approach

Market segmentation remains one of the most important uses for conjoint analysis based on the estimated attribute part-worths [ 31 , 105 , 168 , 186 ]. Historically, segments were developed in a rather disjointed two-step fashion (clustering after estimating individual-level conjoint part-worths). This resulted in various problems, for instance, in highly fractionized designs, the estimated individual-level part-worths are often unstable and are stochastic and quite different loss functions are optimized using these disjointed methods. In this light, there has been research dedicated to simultaneously performing this two-step approach more parsimoniously; for instance an early example includes the Q-factor analytic procedure that maximizes the predictive power of the derived segment-level utility function. DeSarbo and colleagues provide alternative cluster-wise regression based formulations for such benefit segmentation approaches utilizing conjoint analysis [ 42 ].

Following these deterministic cluster-wise approaches, a number of latent class or finite mixture-based solutions to simultaneously perform conjoint and market segmentation analysis had been developed. The advantages of these simultaneous procedures are that they employ stochastic frameworks involving mixtures of conditional distributions which allow for heuristic tests for the optimal number of segments (via AIC, BIC, CAIC, ICOMP, etc. heuristics), Footnote 13 fuzzy posterior probability of memberships that permit fractional membership in more than one market segment, and a stochastic approach that allows for computation of the standard errors of the estimated part-worths. Many such latent class conjoint procedures also allow for heteroscedasticity among groups of consumers as well as for variation within these groups’ responses. Interested readers are referred to several early articles by DeSarbo and colleagues (cited in DeSarbo and DeSarbo [ 42 ]). In the last decade, these authors develop a host of latent class models that can be applied to conjoint analysis, addressing the issue of segment identification. Chung and Rao [ 31 ] develop a comparability-based balance (COBA) model that accommodates bundle choices with any degree of heterogeneity among components (products) and incorporates consumer preference heterogeneity that can be used for segmentation and optimal bundle pricing.

Much of the early literature involved modeling heterogeneity through the use of individual-level traditional conjoint analysis. Bayesian conjoint analysis and latent class conjoint analysis had initially focused on the modeling of metric data. In more recent times, effort has been devoted to conjoint-choice experiments. This was motivated by the fact that conventional rating-based (metric) conjoint analysis depends on a consideration (rating) task that does not link directly to any behavioral theory. We feel that employing actual choice between alternatives is more realistic than the conventional approach of using mere artificial rankings and ratings. As such, we applaud the development of such latent class conjoint procedures for the analysis of choice data.

6.2.1 Suggested Directions for Future Research

Latent class models all typically assume that the respondent belongs to one and only one underlying segment allowing for the calculation of posterior probabilities. By definition, these posterior probabilities for each respondent sum to one, indicating a convex combination of these segment memberships. These individual-level predictions obtained from such finite mixture-based models tend to be rather poor depending upon the degree of separation of the centroids of the conditional segment-level support distributions and the within segment variation, thus limiting the range of the predictions. We encourage the development of new methods for improved prediction.

Segment identifiability remains a problem with such latent class segmentation procedures in conjoint analysis since individual differences in the estimated individual-level parameters are rarely well predicted by demographics, psychographics, etc. This same problem lies with respect to the estimated segment-level parameters as well. Even with explicit reparameterization of the mixing proportion via the concomitant approach, it is uncommon to be able to shed sufficient light on describing the derived market segments vis-à-vis traditional individual difference measurements. We encourage the development of new methods in improving segment identifiability.

Using the ideas of Hidden Markov Models [ 129 , 65 , 144 ], additional research is required to investigate the dynamic nature of such derived market segments including switching segment memberships over time, the evolution of different market segments over context or consumptive situations, and the time path of changing parameters.

6.3 D3. The Polyhedral Estimation Approach

Toubia et al. [ 171 ] proposed and tested a new “polyhedral” choice-based question-design method that adapts each respondent’s choice sets on the basis of previous answers by that respondent. Footnote 14 The simulations conducted suggest that polyhedral question design does well in many domains, particularly those in which heterogeneity and part-worth magnitudes are relatively large. In particular, the polyhedral algorithms hold potential when profile comparisons are more accurate than self-explicated importance measures and when respondent fatigue is a concern due to a large number of features. For example, in product development scenarios, managers may want to learn the incremental utility of a large number of features allowing them to screen several features quickly [ 138 ].

Toubia et al. [ 170 ] validated the polyhedral approach and found that it was superior to the fixed efficient design in both internal and external validity, and slightly better than the adaptive conjoint method. However, the polyhedral approach is highly sensitive to errors in the early choices. Despite mixed results of the polyhedral questions especially when response error is high, Toubia et al. [ 172 ] subsequently proposed and tested a probabilistic polyhedral method by recasting the polyhedral heuristic into a Bayesian framework which includes prior information in a natural, conjugate manner. This method shows potential to improve accuracy in high response-error domains by minimizing the expected size of the polyhedron (i.e., choice balance) and also by minimizing the maximum uncertainty on any combination of part-worths (i.e., post-choice symmetry). Evgeniou et al. [ 58 ] introduce methods from statistical learning theory to conjoint analysis that compares favorably to the polyhedral heuristic.

While, Toubia et al. [ 172 ] demonstrated improved accuracy in using probabilistic polyhedral method, the analytic-center estimation does not yet perform as well as the HB method. Abernethy et al. [ 1 ], using complexity control machine learning, demonstrate robustness to response errors inherent in adaptive choice which outperforms polyhedral estimation proposed by Toubia et al. [ 170 ]. More recently, Dzyabura and Hauser [ 54 ] developed and tested an active machine-learning algorithm to identify noncompensatory heuristic decision rules based on prior beliefs and respondent’s answers to previous questions. Currently, research that frames the fast polyhedral method in HB specification (GENPACE) has shown to outperform FastPACE under certain conditions [ 177 ].

6.3.1 Suggested Directions for Future Research

We suggest future conjoint scholars working with the polyhedral algorithm to combine self-explicated data within the framework of stated choice data to improve the estimation as shown by Toubia et al. [ 171 ] and Ter Hofstede et al. [ 167 ] in traditional conjoint analysis. Such self-explicated data can help constrain the rank order of part-worths and thereby shrink the polyhedral confidence region for estimated part-worths.

Combining analytic-center (AC) estimation with Bayesian methods may broaden the scope and applicability of the polyhedral algorithm when respondent heterogeneity and response accuracy in stated choice are both low. Also, the polyhedral ellipsoid algorithm can perhaps be further broadened to newer domains of application including situations marked by a lack of nondominance, choice balance, and symmetry—criteria that are presupposed in the current algorithm.

6.4 D4. Integrating Multiple Sources of Data

Based on existing research, conjoint analysis could also benefit substantially by combining multiple sources of data. Traditionally, preference measurement studies have relied on data provided explicitly by consumers during the preference measurement task. Both stated and revealed preference data provide information on the utility of offerings, and thus one source of data can be integrated as a covariate in a model of the other [ 82 ]. Further, Allenby et al. [ 6 ] recommend that information across datasets may be combined by forming a joint likelihood function with common parameters that will result in more precision. For example, stated preference data may require corrections for various response biases, while revealed preference data may require information controlling for contextual effects.

An interesting development by Ashok et al. [ 11 ] is the structural equation models (SEM) that integrate softer variables (e.g., attitudes) into binary and multinomial choice models to explain choice decisions. They compare the limited information model (without latent variables) in which factor scores for the exogenous latent variables are included in the utility function as error-free variables with the full information model with latent variables. In general, full information estimation methods yield structural parameter estimates that are significantly more precise than those obtained by using two-stage limited information approach where latent constructs are treated as error free instead of as random variables.

Furthermore, there is potential for combining stated preference data with auxiliary revealed preference data. For instance, researchers could look at qualitative and observational research techniques to capture response latencies, eye movement, and other psychosomatic patterns. Haaijer et al. [ 74 ] demonstrated that response time is related to preference and choice uncertainty such that shorter response times represent more certain choices. In a very recent study, Toubia et al. [ 173 ] conduct two eye-tracking studies (using Tobii 2150 tracker) to compare incentive-compatible conjoint poker with incentive-compatible choice-based conjoint. The assumption is that choice-based conjoint participants make choices based on a smaller subset of attributes resulting in decreased visual attention for a large proportion of attributes and levels.

The different approaches to modeling consumer preference (e.g., compositional model, decompositional model, subjective expected utility model, etc.) are based on the inherent assumption of traditional utility theory and attribute processing. However, consumer researchers for some time now have also established the power of affect, feelings, and emotions in consumer judgment, preference, and choice [ 136 ]. Unfortunately, not much research has been done to integrate the traditional utility-based paradigm with such affective responses in conjoint experiments. The concept of “attribute prominence” consisting of attribute importance and emotionality would better capture choice than merely using cognitive-based importance measures as earlier suggested by Luce et al. [ 118 ].

6.4.1 Suggested Directions for Future Research

A promising area in need of more work is the marriage of discrete choice models with latent variables such as attitudes and perceptions. Following Ashok et al. [ 11 ], we encourage more researchers to integrate latent constructs in discrete choice models such as attitude, satisfaction, service quality perception, and other widely used marketing-based perceptual constructs. A related area is the marriage of scanner-panel data with multinomial choice, where nonproduct attributes such as consumer attitudes and motivations and store level data may drive brand purchase along with product attributes [ 60 , 165 ].

Additional research should be aimed at understanding the underlying mechanism (rules and heuristics) that determines consumers’ decisions and develop measures of the decision process variables—decision problems, decision contexts, social situations, and individual factors.

We believe that integrating multiple sources of data in innovative ways can add to the reliability, validity, and generalizability of conjoint studies in the future. The integration of qualitative aspects and emotional reactions of consumers with stated preference data in forming preferences and choices is an important research avenue [ 43 ]. While aesthetic stimuli pose special challenge in designing a factorial design due to the difficulty of decomposing what is essentially unitary or holistic stimuli, researchers are encouraged to work creatively in harnessing the benefits of such auxiliary data.

Conjoint analysis provides an exacting measurement of consumer preferences, but to design a product or set marketing variables a firm must often do so in light of the actions and potential actions of its competitors. We are now beginning to see equilibrium (or nonequilibrium) models, which include the reactions of firms, competitors, and customers, coupled to conjoint analyses. One example is Kadiyali et al. [ 96 ]. More work needs to be done in this promising line of research.

7 (E) Managerial Issues Concerning Implementation

We now discuss selected implementation issues relevant for the manager including product optimization, market value of attribute improvement, optimal pricing, and product line decisions.

7.1 E1. Product Optimization

The primary goal of traditional conjoint analysis was to find a parsimonious manner of estimating consumer utility functions and deriving attribute (level) importances. In this effort, one could design a product with maximum utility whose attribute levels correspond to the highest estimated utility values. While the problem was first formulated as a zero–one integer programming model, a more general and thorough approach to product design optimization was developed by Green and colleagues with their Product Optimization and Selected Segment Evaluation (POSSE) procedures. Soon thereafter, efforts were directed to extend single-product design optimization heuristics to entire product lines introducing two objective functions (the buyer’s and seller’s welfare problem). This marked a critical development in product optimization research that triggered a flurry of research.

Another major advance in this field was the idea that consumers’ preference structures were dynamic rather than static (due to variety seeking, learning, and fatigue), which calls for models that can capture the dynamics and respondents heterogeneity (for a review, see Wittink and Keil [ 188 ]. More recent artificial intelligence and engineering optimization approaches to product line optimization using conjoint analysis include Belloni et al. [ 14 ], Wang et al. [ 182 ], Luo [ 119 ], and Michalek et al. [ 124 ]. Recently, some progress has been made by Luo et al. [ 120 ] wherein they propose a hierarchical Bayesian structural equation model by incorporating subjective characteristics along with objective attributes in new product design. Their results indicate that by collecting additional information about consumers’ perceptions of the subjective characteristics, the proposed model provides the product designer with a better understanding and a more accurate prediction of consumers’ product preferences compared to traditional conjoint models. We encourage more research in this area such as testing the virtual-reality prototypes [ 36 ], instead of physical prototypes, when attributes are large in number and therefore expensive.

7.1.1 Suggested Directions for Future Research

A line of research with promising potential is the area of improving preference measurement for really new products as opposed to incrementally new products. In attempts to improve preference measurement by building consumer knowledge, more research needs to be conducted to fully understand consumer inferential techniques in reducing uncertainty (i.e., consumer-initiated analogy generation and marketer-supported analogy). More needs to be done on how consumers think and learn about really new products pre-, during, and post adoption stages, and how we can modify measurement techniques to maximize the predictive accuracy of preference measurement.

We believe that attribute-based conjoint models are potentially limited and that further investigation should proceed at least as far as customer-ready prototypes for a spectrum of design concepts. The prototypes are likely to provide more accurate information on customer reactions and costs and more accurate information on the attribute levels achieved (rather than expected) with particular designs. One possible direction for future extension is to combine this with other related methods such as Neural Network Approaches and Genetic Algorithms to gain better prediction. See Chung and Rao [ 32 ] for modeling of unobserved attributes in experiential products using virtual expert model.

7.2 E2. Market Value of Attribute Improvement

Predicting performance in the marketplace and gaining insight into the value of design features are important goals of market research. One question of managerial relevance is whether or not attribute improvement can be measured in terms of cost-benefit analysis. In other words, given that an attribute improvement (positive change) always comes with a price increase (negative change), there is a trade-off involved in its impact on market share. Ofek and Srinivasan [ 131 ] show that the market value of an attribute improvement (MVAI) can be expressed as the ratio of the change in market share due to an improvement in attribute to the ratio of decrease in market share due to change in price. These authors tested this approach using five portable camera mount products described on five attributes each varied at three levels. They estimate the MVAI for each of the attributes and show that these have less bias than the commonly used attribute values computed by averaging the ratio of weights of attribute and price across individuals. They also demonstrated that profitability of attribute improvements decreased when factoring in competitive reactions. The firm should undertake attribute improvement if MVAI exceeds the cost of attribute improvement. To mimic a real-world situation, MVAI can incorporate choice set, competitive reactions, and heterogeneity of respondents and translate utilities into choice probabilities [ 138 ].

7.2.1 Suggested Directions for Future Research

Future research should pay more attention to the dynamic issue of consumer choice or preference (both before product design and before product launch), which means that studies should extend over multiperiods and respondents should be able to upgrade [ 138 , 100 ]. Also, research should be done after product diffusion (i.e. multiperiod analysis), as attributes’ importance will change as consumers gain more experience with the products as will the market value of the attribute.

Meta analyses in this area would be particularly desirable. More specifically, publishing research on tracking the monetary implications of pursuing optimal conjoint design implementations in different commercial scenarios would prove a great aid in advancing more applications of conjoint analyses.

7.3 E3. Optimal Pricing

Kohli and Mahajan [ 104 ] propose a model for determining the price that maximizes the profit of a product that has been screened based on share criterion. They do so by incorporating the effect of measurement and estimation error in demand estimates which in turn affects the price that maximizes profit. They model heterogeneity in individual reservation prices by assuming that the variance of the distribution is constant but the mean is normally distributed. Jedidi and Zhang [ 90 ] further develop this method to allow for the effect of new product introduction on category-level demand, and Jedidi et al. [ 92 ] describe a method for estimating consumer reservation prices for product bundles. Chung and Rao [ 31 ] evolve the issue of optimal pricing to the level of bundle choice models which employ attribute-based products (i.e., components) of a bundle as the ultimate unit of analysis in estimating the utility of the bundle. Reservation price for bundles is higher for attributes regarded as desirable or complimentary.

More recently, Iyengar et al. [ 87 ] describe a conjoint model for the multipart pricing of products and services. Given that for many product and service categories there is a two-way dependence of price and consumption (fixed fee and usage-based fee), Iyengar et al. [ 87 ] incorporate the effects of consumption on consumer choice and the uncertainty of service usage (by using a quadratic utility function). A benefit of their model is its ability to infer consumption at different prices from choice data which can aid marketers in their market share maximization objectives.

Ding et al. [ 50 ] demonstrate that consumers demonstrate two behavioral regularities in relation to how their utility functions depend on the role of price: consumers infer quality information from a product’s price and they have a reference price for a given product. Consumer heterogeneity is captured through an individual-specific reference point and an individual-specific information coefficient. They demonstrate that the classic economic model where price serves the allocative purpose is more relevant for inexperienced or uninvolved customers. On the other hand, price maximally serves as an informational price cuing quality where customers are the most involved. This piece of research is one of the pioneering first steps in integrating behavioral regularities into classic utility models in pricing research. Kannan et al. [ 98 ], through an online choice experiment on digital versus print products, propose a model to account for customers’ perceptions of substitutability or complementarity of content forms in developing pricing policies for digital products. Research on product line extensions has traditionally treated this issue as substitutes, although it is possible that customers may perceive digital products as imperfect substitutes or even complements to printed products. Bundling and pricing strategies are determined by capturing customers’ heterogeneity in their perceptions of substitutability and complementarity by estimating parameters of the model using a finite mixture (FM) model.

7.3.1 Suggested Directions for Future Research

Along the lines of Iyengar et al. [ 87 ], future research can examine computationally efficient methods for optimal selection of product features and prices. There is also potential for factoring in the effect of competitive actions and reactions on multipart pricing.

Future researchers can look into additional behavioral regularities built into the utility model such as a reflexive shape around the reference point and the effect of dynamic competition. This would be a useful area for the application of game theoretic models employing alternative strategies and competitive scenarios.

7.4 E4. Product Line Decisions

The optimal product line design problem belongs to the class of NP-hard combinatorial optimization problems. A number of optimization algorithms have been applied to solve such difficult problems including dynamic programming, beam search, genetic algorithms, and Lagrangian relaxation with branch and bound [ 12 , 23 ]. More recently, alternative heuristics have been devised employing conjoint and choice models. Michalek et al. [ 124 ] recently presented a unified methodology for product line optimization that coordinates positioning and design models to achieve realizable firm-level optima. Their procedure incorporates a general Bayesian representation of consumer preference heterogeneity, and manages attributes over a continuous domain to alleviate issues of combinatorial complexity using conjoint based consumer choice data. Tsafarakis et al. [ 174 ] devise particle swarm optimization technology for the problem of optimal product line design and employ a Monte Carlo simulation to favorably compare its performance to the use of genetic algorithms. In addition, these authors use concepts from game theory to illustrate how the proposed algorithm can be extended to incorporate retaliatory actions from competitors using Nash equilibrium concepts.

7.4.1 Suggested Directions for Future Research

Future research in this area should extend such models beyond linear and continuous cost functions, to accommodate mixed level product attributes (discrete and continuous), to handle category expansion and pioneering advantages, and allow for the enactment of various designated offensive and defensive strategies.

It would also be useful to extend such computer science-based procedures to accommodate multiple objectives for optimization in conjoint applications.

8 Conclusion

From the rigorous psychometric tradition from which conjoint analysis has evolved, a plethora of advances have been made. In this manuscript, we have attempted to integrate several substantive issues of interest in conjoint analysis within an organizing framework that impacts major stakeholders (i.e., researcher, respondent, and manager). For each of the five categories in our framework, we summarize recent developments in the field, provide some critical insights, and present suggested directions for future research. We hope that conjoint scholars will gainfully employ this organizing framework as a repository for drawing additional new insights and conducting future research. We believe that research in conjoint continues to be vibrant and the recent advances, developments, and directions discussed in this paper will contribute to the realization of the tremendous potential of conjoint analysis.

In conclusion, our paper makes several contributions to the literature (including the recent book by Rao [ 139 ]). First, our review incorporates an organizing framework based on the behavioral and theoretical processes underlying several issues related to the researcher, the respondent, and the manager in conjoint analysis. We have an expanded and provided recent coverage of the behavioral and theoretical underpinnings (see section A) that sets the tone for the rest of the review. Second, our framework allocates adequate attention to critical issues surrounding the three major stakeholders: the researcher, the respondent, and the manager. Third, we cite publications from major marketing and nonmarketing journals across disciplines. Fourth, our paper also sets a comprehensive research agenda going forward, 55 research directions in total, which can be leveraged for future development of conjoint analysis methodology. Finally, we believe that a review paper on conjoint analysis will be able to draw wide readership and citation by scholars in the future, thereby enhancing the impact factor of this journal.

The part-worth conjoint analysis model is basic and may be represented by the following formula: \( {U}_x=\sum_{i=1}^m\sum_{j=1}^{k_i}{\alpha}_{ij}{x}_{ij} \) where \( {U}_x \)  = overall utility of an alternative; \( {\alpha}_{ij} \) = the part-worth contribution or utility associated with the j th level ( j , j  = 1, 2…. ki ) of the i th attribute ( i , i  = 1, 2…. m ); \( {k}_i \)  = number of levels of attribute I ; \( m \)  = number of attributes; \( {x}_{ij} \)  = 1 if the j th level of the i th attribute is present and = 0 otherwise.

These include Green and Srinivasan [ 71 , 72 ], Wittink and Cattin [ 187 ], Carroll and Green [ 25 ], Hauser and Rao [ 75 ], and Rao [ 138 , 139 ].

Recent developments in tools in psychology including functional imaging and neural recordings, process tracing tools, and modeling tools such as mediation and multilevel analysis have benefitted this research stream. For instance, process models consider intervening variables and intermediate stages between the start and end of the decision by incorporating additional external search information and internal memory-based information.

The beta-delta model explains greater discounting of future outcomes when immediate rewards are available than when all rewards are in the future, by an exponential delta process that always operates and an additional exponential beta process that only operates when immediate rewards are present. For instance, in decisions from descriptions, certainty in the probability dimension and immediacy on the delay dimension are given extra attention, and consequently decision weight, as captured by prospect theory and Laibson’s beta-delta model of time discounting [ 108 ].

The function is typically, U  =  ΣXβ , where X ’s are attributes or functions of attributes such as X 1 X 2 .

Liu and Arora [ 115 ] found asymmetric effects in design efficiency loss. When the true model is conjunctive, compensatory designs have significant loss of design efficiency. However, when the true model is compensatory, the efficiency loss from using a conjunctive design is significantly lower.

Cue diagnosticity is an information processing technique based on metacognitive insights about past inferential accuracy that helps in distinguishing between two alternatives. TTB is an inferential strategy based on memory retrieval mimicking lexicographic decision rule in choice using the most diagnostic cue.

For instance, Chevalier and Mayzlin [ 29 ] find that differences in the number of ratings (volume) and the average rating (valence) across online book retailers (Amazon and Barnes and Noble.com) affected relative sales. Similar results have been found wherein online customer movie ratings are related to future box office revenues [ 41 ].

For an interesting online study of user engagement conducted by Yahoo using web-based user and content data (click through rate) and tensor segmentation technique, see [ 30 ].

Experimental choice analysis often combines discrete choice responses, a logit model, fractional and factorial designs [ 25 ]. Interested readers can refer to some of the seminal papers in choice-based logit models reviewed earlier [ 138 , 75 ].

In one of the earlier studies, Cattin et al. [ 26 ] employed a Bayesian procedure to improve the prediction of holdout profiles by using self-stated utilities to derive a prior distribution. This prior distribution was used in the estimation of the individual-level part-worths from the full-profile evaluations. Subsequent to their pioneering work, several noteworthy studies have estimated the importances from full-profile data under various real-world constraints derived from the order information in the self-stated data thereby improving the predictive performance of the model (see reviews by Hauser and Rao [ 75 ] and Rao [ 138 ]).

In HB practical applications, there usually is no attempt to optimize the design blocking, so there is no reason to expect the particular trade-offs individual subjects see to provide a meaningful basis for a Bayesian update of the priors provided by the population means. However, Sawtooth Software assigns blocks by drawing randomly from the full design for each subject resulting in better HB estimates. For asymmetric designs, random designs can be more efficient overall than purely orthogonal designs.

AIC is Akaike’s Information Criterion, BIC is Bayesian Information Criterion, CAIC is Consistent Akaike’s Information Criterion, and ICOMP is Information Complexity. For technical details of segment retention criteria, the reader is referred to Andrews and Currim [ 7 ].

Polyhedral “interior-point” algorithms (Fast Polyhedral Adaptive Conjoint Estimation or FastPACE) design questions that quickly reduce the range of feasible part-worths that are consistent with the respondent’s choices. The estimation methods employed are hierarchical Bayes and “analytic center”, a new estimation procedure that is a by-product of polyhedral question design. The analytic center is the point that minimizes the geometric mean of the distances to the faces of the polyhedron thereby yielding a close approximation to the center of the polyhedron.

Abernethy J, Evgeniou T, Toubia O, Vert J-P (2008) Eliciting consumer preferences using robust adaptive choice questionnaires. IEEE Trans Knowl Data Eng 20(2):145–155

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Agarwal, J., DeSarbo, W.S., Malhotra, N. et al. An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research. Cust. Need. and Solut. 2 , 19–40 (2015). https://doi.org/10.1007/s40547-014-0029-5

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What is Conjoint Analysis -  Examples & Use Cases

What Is Conjoint Analysis?

Conjoint analysis is a marketing research method that leverages statistical analyses and mathematical models to quantify survey respondents' preferences for product features, determine what attributes impact product demand, and can also predict how the market will respond to new products pre-launch.

These research methods use specialized surveys to ask respondents to pick between bundles, and the importance of specific product features is then analytically derived from the results. Once the survey results are collected, the organization can determine how valuable each product feature is and if they should implement them before launch.

For example, a TV manufacturer might conduct a survey where they give the respondents bundles with different features to pick from. Each bundle might include screen type, size, brand, and price. Respondents will choose which bundles are more appealing to them, and the TV manufacturer can determine how much each respondent values certain features.

Conjoint analysis is becoming increasingly popular among market researchers and other organizations because it provides valuable insights that are difficult to obtain elsewhere.

Why Should Your Business Use Conjoint Analysis? 

Conjoint analysis helps organizations optimize features and pricing by giving them the means to quantify and study consumer preferences. Using conjoint analysis, organizations can measure the importance of different factors by having customers choose between realistic product packages. Organizations can deduce the importance of various factors, such as pricing and product features, by modeling utilities after the data derived from conjoint exercises. When using conjoint analysis in tandem with market segmentation , you can easily narrow down which customers prefer what features or services.  

If you ask consumers directly what is important to them, the answer is usually everything.

A Conjoint Analysis Example 

For example, most respondents will say that price is very important when buying a smartphone. However, high-price brands like Apple have a very strong market share. Factually, price is less important than product features like the brand, the features, etc. Optimizing features and pricing requires more accurate quantification of consumer preferences. Respondents are not asked to state the importance of different factors. Instead, they are asked to pick between realistic options (products with features and pric es). The importance is derived from the choices they made. This is why conjoint analysis is often used over other methods.

For market research agencies, purchasing survey software is an important decision. For example, many agencies evaluate survey software on three dimensions: functionality, price per complete, and quality of service. Conducting a conjoint analysis across these three dimensions provides insight into tradeoffs between the three.

At IntelliSurvey, we pride ourselves in our ability to immerse respondents in the conjoint exercise. Our video walkthrough of a survey conjoint with an agency’s lead generation activities is a great example

What Makes an Effective Conjoint Analysis?

Effective conjoint analysis models feature natural relationships between respondents and product attributes. Conjoint exercises should appear “au naturel” to customers to yield the most accurate data possible. One way to accomplish this is to tailor conjoint models specifically for critical customer segments; various segments will weigh attributes differently, and taking a one-size-fits-all approach may invalidate the data.

Incorporate Natural Relationships Between Respondents and Attributes

The best conjoint analysis models incorporate natural relationships between respondents and product attributes. Effective conjoint analysis models feature surveys that closely resemble the final decision point; for example, if you conduct a conjoint analysis for phone plans, you might want to replicate a large phone carrier’s bundle price range.

If imperfectly contextualized, the presentation might not reveal natural attribute preferences. Over time, attention to detail on the look and feel has decreased for practical reasons; organizations run more conjoints, and directional results are often enough. However, the best conjoint results leverage UI elements close to the final product to obtain more detailed results.

Tailor Conjoint Exercises for Critical Customer Segments

Where applicable, organizations should tailor conjoint exercises for critical customer segments. For example, an airline may create different conjoint models for frequent and infrequent flyers. Regular airline customers may receive a free, first-class upgrade, so measuring first-class against economy will have to incorporate more factors than just pricing preferences. 

For many organizations, it can be challenging to analyze these complexities and adapt their exercises to provide options for critical customers, but it is crucial for gaining valuable insights across all customer segments.

Streamline Presentation for Mobile Devices

Roughly half of the online survey traffic is mobile, meaning voluminous choice options will not properly fit on a smartphone or similar device’s single screen. In these exercises, scrolling can become too intense for respondents to focus on and cause them to disengage.

It is possible to limit conjoint exercises to desktop users, but this creates a selection bias in survey responses. In many cases, acquiring data from younger respondents becomes challenging. Conjoint presentations have been streamlined, often with intro pages to give details to keep the task manageable on a mobile device. 

It is critical for organizations to review and test conjoint exercises on an online device before launching to ensure proper data quality.

Exploring Types of Conjoint: 4 Examples of Conjoint Analysis

There are four different types of conjoint analysis methods that organizations employ during market research: choice-based conjoint (CBC) analysis, adaptive choice-based (ACB) conjoint analysis, adaptive conjoint analysis (ACA), and menu-based conjoint (MBC) analysis.

1. Choice-Based Conjoint (CBC)

Choice-based conjoint (CBC) analysis is one of the most common forms of this research method. CBC records how a respondent values different combinations of features within a product or service. 

This conjoint analysis method asks survey respondents to review a set of product concepts with potential features and allows them to select their favorite combination. Using these results, researchers can predict the market share for different scenarios, depending on the product they roll out.

2. Adaptive Choice-Based (ACB) Conjoint

Adaptive choice-based (ACB) conjoint analysis models are newer and more advanced than other conjoint models. Adaptive models are most effective when the attribute list grows because it delves deeper into the respondents' preferences. In return, this conjoint analysis method provides a thoroughly engaging experience for the respondent. ACB conjoint analysis models are more detailed and require more time to conduct than a CBC study.

3. Adaptive Conjoint Analysis (ACA)

Adaptive conjoint analysis (ACA) is a conjoint analysis research method from the 1980s that provides each respondent with a personalized experience. This conjoint analysis method gives each respondent a different survey experience based on their answers to previous questions. Although many organizations do not use ACA today, it is helpful where organizations must evaluate numerous product features or attributes simultaneously to hasten the process.

4. Menu-Based Conjoint (MBC)

Menu-based conjoint (MBC) analysis models are advanced tools that assist in analyzing menu choice experiments with multiple checks. While this conjoint analysis model provides organizations with better opportunities for modeling complex consumer preferences, it requires a high level of expertise. Many organizations will defer this to experienced professionals with the necessary platforms to make this conjoint model scalable and viable.

When Is Conjoint Analysis Used?

Conjoint analysis methods help organizations conduct market simulations, predict demand, and estimate price sensitivities for potential products. Many researchers also leverage data from conjoint analyses to organize respondents based on what product attributes and features they prefer. In short, conjoint analysis is used to help companies determine which features users value the most and assists them primarily in the three following areas: pricing, marketing efforts, and research and development.

Conjoint Analysis Helps Determine Pricing

Conjoint analysis helps businesses determine pricing for different products and services. Using conjoint analysis research methods, organizations can compare various product features to determine how consumers value each. For example, attributes that users appreciate more in service might wind up in a more expensive subscription package, while lesser-valued features might become part of a trial version.

Conjoint Analysis Informs Marketing Strategies

Conjoint analysis methods help organizations create marketing strategies by informing them of which factors are most valuable to various target audiences. Organizations can leverage respondents’ answers about which features are highly favored to determine where they should allocate their marketing resources. Conjoint analysis can also help marketers segment and target different audiences based on what features different demographics value the most.

Conjoint Analysis Assists in Research Development

Insights obtained from conjoint analyses can assist an organization in researching and developing new products and services. For example, if a competitor's product offers more features, they can deploy a survey to their users with sample packages that include those features. If users favor one sample over the others, the organization can move forward with implementing those features.

Modeling is a critical part of running a conjoint project and has evolved over the years. Conjoint experts back in the day were much more attentive to the design and presentation of the choice scenes themselves. We regularly heard concerns about whether respondents would be able to interact with the scenarios in an intuitive way. If imperfectly contextualized, the presentation might not reveal natural attribute preferences. Thus, IntelliSurvey often undertook great efforts to model conjoints that would appear “au naturel” for respondents - in market freezer doors, new car stickers, six-packs, and more.

As conjoint has gotten more "packaged", one can see much more "plug and chug" thinking about the exercises. Presumably, for many exercises, this is just fine. When the “au naturel” target interaction involves respondents picking one plan from three presented in three columns.  Such exercises are quite straightforward for data collectors to deploy, sometimes requiring just a few hours.

How IntelliSurvey Can Help

We're always jazzed to see more complex models, and presentation schematics. We're excited to work with friends old and new from the high-end modeling community, and introduce interested parties to new modeling methods. The world of bespoke still exists, and may be better than the cookbook at reliably modeling the utilities and relationships critical to your business success.

IntelliSurvey has a variety of products to help companies conduct surveys and multi-market studies. IntelliSurvey has been deploying conjoint analyses for more than 20 years and excels at creating models that are intuitive and organic to survey respondents. We help organizations optimize their features and pricing based on customer preferences, contact us for more information.

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Product development and market establishment pose significant challenges for many companies.

During the development process, the central questions are: What features do customers expect, and which ones are the most important?

Unfortunately, approximately 95% of new product launches fail because they do not meet customers' requirements, expectations, or needs.

Therefore, it's crucial to conduct research to answer these questions and avoid potential failures.

Fortunately, these questions can be answered during the development phase with the help of a conjoint analysis.

In this article we'll explore how you can leverage the power of the conjoint analysis enabling the prediction of potential consumers' behavior in advance by presenting realistic purchase scenarios to identify gaps in existing product competition.

What is the conjoint analysis?

The term conjoint comes from a combination of "considered" and "jointly," which also defines the conjoint analysis. It involves considering various product features (attributes) together and weighing them against other variants.

The Conjoint analysis originated in psychology and was developed by Robert Luce and John Tukey in 1964.

Since then, it has primarily been used in market research and product development to determine what attributes consumers want and perceive as particularly important during the development stage.

Attributes can include functions, designs, or features such as weight, size, and price. However, because consumers tend to want as many attributes as possible for the lowest cost, conjoint analysis takes a different approach from methods such as the MaxDiff Method .

The distinctive feature of the conjoint method is the combination of different attributes instead of independent comparison.

This makes it useful for high-priced products like automobiles, hardware such as laptops or smartphones, luxury goods, as well as everyday products or during the conception phase.

The concept of the analysis is simple.

Consumers are shown different products that differ in the combination of features.

This creates a realistic experience that closely mimics an everyday purchase decision.

For example, in a conjoint analysis to determine consumer preferences for types of chocolate, the filling attribute might be divided into levels such as vanilla cream, strawberries & cream, and kiwi ganache.

In this sample conjoint analysis, the aim is to determine which types of chocolate  consumers prefer and what price they are willing to pay for each type.

The respective attributes are leveled, i.e. they are displayed in a certain form. For the chocolate  example the filling attribute is divided into the levels vanilla cream, strawberries & cream and, kiwi ganache.

Using this approach, a ranking can be created that shows which attributes are most important and which characteristics are most attractive.

This evaluation can then be used to decide on the most appealing and profitable combination for both consumers and the company.

What is the difference between the conjoint method and Discrete Choice Model?

While there are some similarities between the Conjoint analysis and the Discrete Choice Model (DCM), there are also some notable differences.

Both models are preference-structured and designed to uncover the factors that influence consumption choices .

However, the key difference lies in how respondents view the product profiles and their attributes.

In a Conjoint analysis , respondents view the product profiles in smaller groups, while in a DCM , they see all the products simultaneously.

This makes the DCM a bit more realistic in predicting buyer behavior than the Conjoint analysis.

However, it can also be overwhelming for respondents as they are presented with a large number of options.

One of the advantages of the Conjoint analysis is that it provides more information about the attributes' relativity and importance to each other, as well as their contribution to the final buying decision. This is not possible with the DCM.

Moreover, the Conjoint analysis is an excellent tool for predicting behavior before the product is launched, which is less likely when using a DCM.

The Choice-Based conjoint method

The Choice-Based conjoint analysis (CBC) is the most popular form of conjoint analysis and for good reason.

Unlike other forms, CBC analysis asks consumers to make decisions between product variants and accept trade-offs , resulting in a more detailed and realistic analysis.

This approach reflects the fact that we make numerous decisions daily where we weigh different attributes against each other.

In CBC analysis, all previously defined attributes are combined evenly to create a statistically valid ranking at the end of the analysis.

Although there are other types of conjoint analysis, such as the Adaptive Choice conjoint and the Menu-Based conjoint analysis, they are not as flexible as the CBC method and cannot be used as widely.

At Appinio, we specialize in CBC analysis and can help you gain valuable insights into consumer preferences and behavior.

Use cases for Conjoint Analysis

The conjoint analysis is a versatile market research method suitable for a variety of use cases. Three common applications of conjoint analysis are:

Concept testing Conjoint analysis is useful for testing product concepts in the early stages of development. By identifying consumer preferences and potential flaws early on, resources can be saved and the risk of a failed product launch can be minimized.

Diversification and product range expansion The conjoint method is also helpful for testing new product variants, such as different sizes, flavors, or colors, and for optimizing the product range.

Price determination The conjoint analysis can be used to determine the optimal price for a product or service. It can be used as a stand-alone method or in combination with other price analysis techniques like Van Westendorp price analysis . By testing different concepts for their willingness to pay, businesses can make informed pricing decisions.

Conjoint Analysis' best practices

When conducting a conjoint analysis, it is important to follow best practices in order to ensure accurate results.

Here are some tips to keep in mind:

• Use short and concise descriptions of product features to avoid misunderstandings that could distort the analysis.
• Use pictures to help respondents distinguish between different variants and imagine the products being tested.
• Use descriptive comparisons for attributes rather than abstract levels such as "light" or "heavy". Concrete comparisons, such as "as heavy as a similar product," are more appropriate.

To make implementation of these tips easier, consider using the Appinio Conjoint Analysis Tool. This tool provides the necessary setting options for a successful conjoint analysis.

Book a demo and our experts will support all your research needs.

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Setting up a conjoint analysis (with Appinio)

Conducting a Conjoint analysis with Appinio couldn't be easier.

Step 1: Get the survey ready

Register on the Appinio platform .

Define the 3-4 most important product features (e.g. price, design) to be tested.

Contact one of our market research experts. They will guide you with formulating the definition of the product features right up until your survey goes live.

Step 2: Send your survey live

• Our research consultants will do a final check before your survey goes live.
• See the answers coming in! Our panel responds as soon as the survey is live.

Step 3: Analyze your data

• Go to the Appinio interactive dashboard and start analyzing the data you collected.
• The results of the conjoint survey are calculated and visualised in bar graphs and tables by our research consultants to show the utilities and importance of each factor. Accordingly, the results can be used immediately for decision-making.
• Export your results to Excel, PPT or CSV at any time.

What are the advantages and disadvantages of a conjoint analysis?

Conjoint analysis offers several advantages and disadvantages that should be considered when implementing this research method.

• Conjoint analysis can help determine which product features are necessary and which ones consumers would be willing to forgo.
• The analysis can measure subconscious decisions, thanks to the many different combinations of attributes and levels that can be included.

The research design is highly flexible and can be adapted to fit almost any product or concept.

The method is incredibly versatile, covering a wide range of studies such as price willingness , design tests , or product attributes .

Disadvantages

As with any research method, there are also potential disadvantages to consider when using conjoint analysis.

For example:

• Respondents may choose luxury variants since they are not actually spending any money and therefore have no sense of making a real purchasing decision. This can lead to a discrepancy between survey results and actual market behavior.

Conclusion for Conjoint Analysis

Conjoint analysis is a powerful market research tool that offers a multitude of advantages and can be used for a wide range of use cases, particularly in the areas of product development and marketing.

Its flexibility and ability to realistically reflect everyday purchase decisions make it an essential tool for businesses looking to develop and launch successful products.

With conjoint analysis, several combinations and variants can be tested without consumers having to choose their favorites from a list of attributes, allowing for a more accurate analysis of consumer preferences.

Overall, conjoint analysis is an effective way to make informed decisions about product development and marketing strategies, ultimately helping businesses to succeed in a competitive market.

Conjoint Analysis explained

What are the types of conjoint analysis?

What are the basic steps in a conjoint analysis?

What are the main goals of a conjoint analysis?

Is the conjoint analysis quantitative or qualitative?

What industries use conjoint analysis?

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What Is A Conjoint Analysis

A conjoint analysis is a market research approach that helps you understand how people make decisions. With conjoint analyses, you see how people make trade-offs in real time, giving you much needed intel on what features to emphasize, and which ones to deprioritize.

In essence, it replicates the real-time decisions individuals make during any purchase decision. Customers are faced with many products or services in any given category. And, each of these likely comes with different features, benefits, and price points. By understanding how customers prioritize these elements, you can develop options that increase their purchase likelihood.

Business Questions Answered By Performing A Conjoint Analysis

How do you know if a conjoint analysis is right for you? When talking to prospective clients, we keep our ears open for a few key business questions. These are….

•  What features should we release?
•  What benefits are most compelling?
•  How sensitive are customers to price changes?
•  Do any features, benefits, or price points alienate customers?
•  What is the best mix of features, benefits, and price to optimize purchase interest?

If you find yourself asking these questions, especially when planning for a new product release , chances are a conjoint is worth considering.

What You Learn When Performing A Conjoint Analysis

Let’s now breakdown exactly what kind of information you’ll learn when you run a conjoint analysis. For the sake of making things tangible, we’ll pretend we have a financial services company considering launching a new payment product. We need to know how important certain features are to customer preference. Additionally, we want to understand sensitivities to price and feature changes.

Preference Importance

A key learning you’ll get from running a conjoint analysis is something called preference importance. Preference importance lets you see how important individual features are to customers’ purchase decisions. A high importance means that feature has a very strong influence on purchase decisions. In contrast, a low importance means it has very weak influence.

In the case of our pretend financial services company, we see that Fee holds a 45% importance preference. This high importance value tells us that customers really care about the product’s fee when deciding whether or not to buy. Meanwhile, Credit Score holds a 5% importance. This tells us that it has some, but very little, impact on our customer purchase decision.

In this example, looking at preference importance shows us the the premium customers put on Fees over any other decisions element. Nevertheless, our mocked up data also shows us that we can improve consideration with changes to Payment Period and, to a lesser extent, Rewards. And, it tells us that changing Credit Score won’t have a material impact.

Preference Share

Another learning you’ll get from a conjoint analysis is the preference share within a given feature. Preference share shows how much a given level is preferred over other levels. A high share means that a particular level is very strongly preferred. In contrast, a low share means it not at all preferred.

Let’s look at this again in the context of our financial services company. This time, we’re going to home in on the Fees feature. Specifically, we’ll see how much customers prefer paying one fee amount versus others. In this example, customers have a very strong preference for low fees. This is why a fee of .5% has a 65% share while a fee of 1.5% has a 5% share.

This outcome isn’t very surprising. Customers always want to pay less. However, when performed for other features (e.g. payment periods), we may see that customers are more balanced in their preferences.

Relative Utility

Lastly, running a conjoint analysis lets us understand the relative utility of levels within each feature. Said more simply, it lets us identify how sensitive customers are to changes in feature levels. And, it shows when changing certain levels really alienates customers.

Once again, let’s look at this for our financial services company. In this example, a .5% fee has a higher relative utility than a 1% fee. However, both of these options have a positive utility. That is, going from .5% to 1% still results in positive customer interest, albeit the fee hike does decrease interest a bit. However, when the fee goes to 1.5%, the story changes. There is now a negative utility. This means we are alienating customers.

Relative utility lets us see how elastic or inelastic customers are to feature changes. In the case of Fees, we’re seeing a lot of elasticity. This means that customer demand changes a lot when fees increase or decrease. Meanwhile, customers are less elastic when it comes to Payment Day. We can infer this because the graph shows far narrower shifts in relative utility as payment days change.

This represents a valuable insight for businesses looking for ways to improve a product’s economics while increasing customer demand. It tells them which features they can change the most and still keep customer interest high.

How To Perform A Conjoint Analysis

Without getting too deep into the weeds, let’s walk through the standard approach for performing a conjoint analysis.

Develop A List Of Features & Feature Levels

First things first, you need to determine the specific features you want to test. In the case of our financial services company, this would be things like payment periods, fees, rewards, etc. Then, you need to figure out what levels within those features you want to test. For instance, you may want to look at 10-day, 15-day, and 20-day payment periods. Or 1% versus 2% versus 3% fees.

Build A Digital Survey To Capture Input

Using a digital survey tool, we program these different features and feature levels to capture the data needed for subsequent analysis.

When taking the survey, respondents see unique product bundles that mix and match different feature levels. For instance, one product may show a 1% fee and a 30-day payment period. Another may show a 3% fee and a 5-day payment period. Respondents select their preferred options.

The bundle options then refresh so that respondents see a new set of bundle options. Once again, they select their favorite bundle. This process repeats multiple times across all respondents.

Analyze The Results

Once the data is in, we can dig into learning the preference importance, preference share, and relative utility of different features and feature levels. Additionally, if sample sizes permit, we also look at findings by unique segments. This tells us if individual customer groups have different preferences and elasticities.

Alternatives To A Conjoint Analysis

A conjoint analysis is extremely powerful. But, sometimes it’s overkill.

When organizations are in the early days of developing a product or service, you may not even know what features to include. As a result, it’s far too early to dive into testing feature importance, let alone the relative utility of feature levels. Instead, you’re likely better off doing product concept tests . This “dip your toe in the water” approach offers initial validation before you get too entrenched in a product idea.

Or, you may have a list of high-level features but aren’t sure which ones truly matter. In this scenario, you’re not ready to test feature levels. You just need to understand the relative value of unique features. If you find yourself in this instance, consider a MaxDiff study instead. This approach gives you clear insight into what features customers really want, and which ones they’re comfortable leaving behind.

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What Is Conjoint Analysis?

Table of Contents

When unveiling a new product or service, you need to know what your customers will value most about it. Products and services incorporate so many features these days that it can be challenging to ascertain what exactly draws customers to your offering. To better grasp customers’ wants and needs, many business leaders conduct a conjoint analysis.

Conjoint analysis is a statistical analysis and marketing research technique to measure what consumers value most about your products and services. For example, a TV manufacturer would want to know if customers value picture or sound quality more, or if they value low price more than picture quality. Conjoint analysis helps put a value on each feature, allowing you to tailor your products and services to what most consumers are seeking.

Let’s explore the conjoint analysis process – including how to conduct a conjoint analysis and how it benefits a business – and detail some conjoint analysis examples.

What is conjoint analysis?

Conjoint analysis is a tool to help you make business decisions . In an article for the Pragmatic Institute, Brett Jarvis, former global director of product management for Oracle’s Advanced Customer Services, said conjoint analysis is essentially about features and trade-offs.

“Conjoint analysis is a set of market research techniques that measures the value the market places on each feature of your product and predicts the value of any combination of features,” he wrote.

With conjoint analysis, Jarvis said, businesses ask questions of their consumers that force them to make trade-offs between features to determine what goes through their heads when deciding which products to buy. It also allows companies to perform a market analysis to simulate how the market reacts to various feature trade-offs they’re considering.

According to marketing research and analytics firm Optimization Group, conjoint analysis is based on the principle that you can better measure the relative values of a product’s or service’s features when you consider them jointly instead of in isolation.

“In business, it’s important to understand how markets value different elements of your products and services,” according to Optimization Group guidance . “Identifying these elements of higher value will enable you to optimize product development and adjust your pricing structure around the customers’ willingness to pay for specific elements.”

In addition to determining which features consumers value most about a product or service, the best conjoint analysis processes help businesses predict consumer preferences on other items they currently offer or plan to release in the future.

“One of the most important strengths of conjoint analysis is the ability to develop market simulation models that can predict consumer behavior to product changes,” according to research firm QuestionPro . “With conjoint analysis, changes in markets or products can be incorporated into the simulation to predict how consumers would react to changes.”

Anytime you launch a new product, conduct a conjoint analysis to see how the market responds to it. That way, you can make any necessary changes as quickly as possible.

Conducting conjoint analysis

When conducting a conjoint analysis, you’ll determine the features you want to examine, figure out which customers to survey, and determine how to reach participants, such as by mail, over the phone, or online.

Then, place a value or ranking on each possible feature and conduct a business survey with the selected consumers, asking about the features and feature combinations they like best. The survey presents consumers with various combinations of all possibilities and asks them to rank each combination based on their preferences. Once consumers return the surveys, analyze the results to determine the optimal feature set for your needs.

Various services and software can help you set up and properly evaluate conjoint analysis data. Software solutions can help you write survey questions, set up feature combinations, and run statistical analyses on the data so you can understand the results. Popular vendors for conjoint analysis software include Sawtooth Software, Survey Analytics, Qualtrics and XLSTAT.

Consider sending a text survey for a fast and efficient way to survey consumers. Participants can reply to questions quickly and easily through their phones’ SMS features.

Benefits of using a conjoint analysis

Conjoint analysis can benefit a company in numerous ways. Businesses need to know their customers as well as possible to support them with products and services. Through conjoint analysis, you can measure actual and perceived preferences to solidify your place in the market.

Conjoint analysis also allows you to divide your target market data into smaller chunks, segmenting customers based on survey results. This makes it easy to connect to your target customers with custom marketing campaigns that yield better results.

Other business decision-making tools and techniques include a decision matrix , decision tree , Pareto analysis , SWOT analysis and PEST analysis .

Conjoint analysis examples

To better understand the use of conjoint analysis in business, it’s helpful to study some practical examples.

A simple example by Optimization Group centers on how consumers choose a restaurant for dinner. In this example, the features studied were distance to the restaurant, relative prices and the restaurant’s atmosphere. According to Optimization Group, diners make their decision by subconsciously weighing the different factors and choosing the restaurant that best meets their needs. In this example, the first restaurant is close to the diner and inexpensive but offers a subpar atmosphere. The second restaurant may be farther away and more expensive, but it has an excellent atmosphere.

“If you chose Restaurant No. 2, the atmosphere element obviously carried more weight in your eyes than the other two elements,” Optimization Group wrote.

For restaurants, this information is critical in determining how to design their spaces, what prices to charge, and where ideal locations are.

You can find several other examples online:

• MIT’s Sloan School of Management
• Sawtooth Software
• Pragmatic Institute

Another helpful business tool is a competitive analysis , which identifies and evaluates businesses in your market that offer similar products or services.

How businesses use conjoint analysis

Crucial factors that conjoint analysis can help determine include product or service pricing, marketing direction, and research and development.

• Pricing: According to an article from Harvard Business School Online , you can use conjoint analysis to gauge how much your customers are willing to pay for your products or services. Through the analysis, you can ask users to compare different features and how they value each one. You can then set new and accurate prices based on that evaluation.
• Marketing: When the analysis shows what customers value most, you can create advertisements and marketing campaigns that target those features. Alternatively, if some features don’t resonate with customers, you now know to avoid marketing those features and can even change your products.
• Research and development: You can use your analysis to see if there is enough market for new features or even a new product type. These findings show what you should target in your research and development process.

Sean Peek contributed to the writing and research in this article.

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Conjoint analysis: the assumptions, applications, concerns, remedies and future research direction

PurposeSince the inception of the conjoint analysis technique in the year 1971, papers addressing the epistemological aspects of conjoint analysis are scant. Hence, this paper attempts to address the vacuum of qualitative discourse addressing the epistemological and methodological aspects of conjoint analysis including different issues, challenges, probable solutions, limitations and future direction of conjoint analysis in the recent decade.Design/methodology/approachFor exploring the methodological and epistemological aspects of conjoint analysis, the seminal papers on conjoint analysis were reviewed. Moreover, the authors' experience for the state-of-art review was also taken into consideration.FindingsThe findings suggest that conjoint analysis that roots back since 1971 has not seen much exploration in Asian regions and is mainly used for new product development in the field of marketing or allied areas. Moreover, the reliability and validity of conjoint analysis is always a matter of concern for the researchers that hinders this technique's wider adaptability. Thus, the paper presents some probable solutions to address the focal issues useful for improved reliability and validity of the conjoint analysis technique.Research limitations/implicationsThis paper attempts to familiarize the researchers with epistemological and methodological aspects of conjoint analysis with certain solutions to evolve beyond existing conjoint analysis dimensions in terms of improved validity, reliability, epistemological and methodological aspects of conjoint analysis (CA). Moreover, it acts as a call for research in different research domains, especially in the Asian continent.Originality/valueThere exist certain seminal research papers on epistemological aspects of conjoint analysis. However, there is a dearth of such attempt in the recent decade addressing the application issues of conjoint analysis incorporating the recent issues as well. Therefore, this paper is an attempt to usher the future researcher to understand the methodological aspects of conjoint analysis. It may prevent them from violating the basic assumptions and methodological threshold. This research technique is preferred equally by academicians and practitioners, thus making it imperative to have clarity beforehand for improved research rigor.

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Developing and validating a multi-dimensional measure of coopetition

Purpose Coopetition, namely, the interplay between cooperation and competition, has received a good deal of interest in the business-to-business marketing literature. Academics have operationalised the coopetition construct and have used these measures to test the antecedents and consequences of firms collaborating with their competitors. However, business-to-business marketing scholars have not developed and validated an agreed operationalisation that reflects the dimensionality of the coopetition construct. Thus, the purpose of this study is to develop and validate a multi-dimensional measure of coopetition for marketing scholars to use in future research. Design/methodology/approach To use a highly cooperative and highly competitive empirical context, sporting organisations in New Zealand were sampled, as the key informants within these entities engaged in different forms of coopetition. Checks were made to ensure that the sampled entities produced generalisable results. That is, it is anticipated that the results apply to other industries with firms engaging in similar business-to-business behaviours. Various sources of qualitative and quantitative data were acquired to develop and validate a multi-dimensional measure of coopetition (the COOP scale), which passed all major assessments of reliability and validity (including common method variance). Findings The results indicated that coopetition is a multi-dimensional construct, comprising three distinct dimensions. First, local-level coopetition is collaboration among competing entities within a close geographic proximity. Second, national-level coopetition is cooperation with rivals within the same country but across different geographic regions. Third, organisation-level coopetition is cooperation with competitors across different firms (including with indirect rivals), regardless of their geographic location and product markets served. Indeed, organisation-level coopetition extends to how companies engage in coopetition in domestic and international capacities, depending on the extent to which they compete in similar product markets in comparison to industry rivals. Also, multiple indicators were used to measure each facet of the coopetition construct after the scale purification stage. Originality/value Prior coopetition-based investigations have predominately been conceptual or qualitative in nature. The scarce number of existing scales have significant problems, such as not appreciating that coopetition is a multi-dimensional variable, as well as using single indicators. In spite of a recent call for research on the multiple levels of coopetition, there has not been an agreed measure of the construct that accounts for its multi-dimensionality. Hence, this investigation responds to such a call for research by developing and validating the COOP scale. Local-, national- and organisation-level coopetition are anticipated to be the main facets of the coopetition construct, which offer several avenues for future research.

INDUSTRI 4.0: TELAAH KLASIFIKASI ASPEK DAN ARAH PERKEMBANGAN RISET

AbstrakIstilah Industri 4.0 lahir dari ide tentang revolusi industri keempat. Keberadaannya menawarkan banyak potensi manfaat. Guna mewujudkan Industri 4.0, diperlukan keterlibatan akademisi dalam bentuk riset. Artikel ini bertujuan untuk menelaah aspek dan arah perkembangan riset terkait Industri 4.0. Pendekatan yang digunakan adalah studi terhadap beragam definisi dan model kerangka Industri 4.0 serta pemetaan dan analisis terhadap sejumlah publikasi. Beberapa publikasi bertema Industri 4.0 dipilah menurut metode penelitian, aspek kajian dan bidang industri. Hasil studi menunjukkan Industri 4.0 memiliki empat belas aspek. Ditinjau dari metode penelitian, sebagian besar riset dilakukan melalui metode deskriptif dan konseptual. Ditinjau dari aspeknya, aspek bisnis dan teknologi menjadi fokus riset para peneliti. Ditinjau dari bidang industri penerapannya, sebagian besar riset dilakukan di bidang manufaktur. Ditinjau dari jumlahnya, riset terkait Industri 4.0 mengalami tren kenaikan yang signifikan. Artikel ini diharapkan dapat memberi gambaran mengenai apa itu Industri 4.0, perkembangan dan potensi riset yang ada di dalamnya. AbstractIndustry 4.0: Study of Aspects Classification and Future Research Direction. The term Industrial 4.0 refers to the idea about fourth industrial revolution. In order to realize Industry 4.0, academic involvement is required in the form of research. This article aims to define the aspects and future direction of research related to Industry 4.0. Literature review of various definition and concept models of Industry 4.0. was conducted to acquire the aspects. Mapping and analysis of several publications were conducted to determine the future direction of research. Publications were sorted according to research methods, aspects and type of industry. The result shows that Industry 4.0 has fourteen aspects. Based on research methods, most of the research is done through descriptive and conceptual methods. Business and technology aspects become the focus of the researchers and most of the research is done in manufacturing industry. Based on quantities, Industrial 4.0 research has experienced a significant upward trend. This article is expected to illustrate the concept, future development and research trend of Industry 4.0.Keywords: Industry 4.0; Literature Review; Research Trend

Scenario-Based Conjoint Analysis: Measuring Preferences for User Experiences in Early Stage Design

Conjoint analysis has proven to be a useful method for decomposing and estimating consumer preference for each attribute of a product or service through evaluations of sets of different versions of the product with varying attribute levels. The predictive value of conjoint analysis is confounded, however, by increasing market uncertainties and changes in user expectations. We explore the use of scenario-based conjoint analysis in order to complement qualitative design research methods in the early stages of concept development. The proposed methodology focuses on quantitatively assessing user experiences rather than product features to create experience-driven products, especially in cases in which the technology is advancing beyond consumer familiarity. Rather than replace conventional conjoint analysis for feature selection near the end of the product development cycle, our method broadens the scope of conjoint analysis so that this powerful measurement technique can be applied in the early stage of design to complement qualitative research and drive strategic directions for developing product experiences. We illustrate on a new product development case study of a flexible wearable for parent-child communication and tracking as an example of scenario-based conjoint analysis implementation. The results, limitations, and findings are discussed in more depth followed by future research directions.

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What is conjoint analysis? The complete guide

Conjoint analysis is used to build market models and forecasts to answer questions such as "Should we build in more features, or change our prices?" or "Which of these changes will hurt our competitors most?", or "What is the optimum price to charge?" that allow the business to optimise product and service design to customer needs.

To explore or play with conjoint analysis, try our interactive Conjoint Demonstration , our simple conjoint in Excel to see how conjoint analysis works numerically, or use our free, full-featured Conjoint Explorer to design and test your own conjoint experiments.

Conjoint overview

Every customer making choices between products and services is faced with trade-offs ( see our conjoint demonstration ). Is high quality more important than a low price and quick delivery for instance? Or is good service more important than design and looks?

For businesses, understanding precisely how customers value different elements of a product or service allows product development to be optimised to give the best balance of features or quality, for the prices the customer is willing to pay. At a market level, conjoint analysis can be used to identify the best product range for different segments or market needs, by determining which features, value and price, across a set of products, would maximise customer value and market returns.

Conjoint analysis is also known as Discrete Choice Estimation, or stated preference research and is one of a range of trade-off based research techniques.

An established and powerful means of estimating customer value

With on-going development and improvements since it was invented in the 1970s conjoint analysis has become a core tool for product planning and pricing research. By understanding precisely how people make decisions and what they value in your products and services, you can work out the sweetspot or optimum level of features and services that balance value to the customer against cost to the company and forecast potential demand or market share in a competitive market situation.

It is, however, a sophisticated technique and expertise is required to ensure the design and outputs will achieve the business objectives.

Conjoint principles - attributes and levels

For example a computer may be described in terms of attributes such as processor type, hard disk size and amount of memory. Each of these attributes is broken down into levels - for instance levels of the attribute for memory size might be 1GB, 2GB, 3GB and 4GB. Play with attribute and levels in our Conjoint Explorer to see how designs can be created.

From attributes and levels to product profiles and choice tasks

Go Hands-on with our Conjoint Explorer

These attributes and levels can be used to define different products by choosing different levels for different products so the first stage in conjoint analysis is to create a set of product profiles (possible combinations of attributes and levels) to produce a set of options from which customers or respondents are then asked to choose - know as choice sets or choice tasks.

Obviously, the number of potential profiles increases rapidly for every new attribute added as the number of possible combinations increases, so there are statistical techniques and design methods to simplify both the number of profiles to be tested and the way in which preferences are tested so that the maximum amount of choice information can be collected from the smallest set of choice tasks.

Choosing the right type or flavour of conjoint analysis

The precise approach to creating 'choice tasks' depends on the which type or flavours of conjoint analysis is most appropriate to use. The most common approach is choice-based conjoint (CBC), but other flavours exist. Students often get taught full-profile conjoint using ratings or cards, for more attributes adaptive designs get used, such as adaptive conjoint analysis (ACA), menu-based conjoint, or adaptive choice based conjoint (ACBC). Economists might look at Stated Preference or Discrete Choice Methods.

Conjoint analysis might not be the right option. Other approaches such as MaxDiff, Simalto or hierarchy of needs studies, each have different ways to manage the balance between the number of attributes that can be included and the relative complexity of the choices that need to be shown in order to get good quality data.

Statistical design and analysis

A conjoint analysis study relies on appropriate statistical design in order to be able to estimate the utility models. Once all the choice tasks have been completed, analysis involves modelling what drove customers choices or preferences from the product profiles offered.

The statistical output then quantifies both what is driving the preference from the attributes and levels shown - known as utilities or part-worths and importance scores. These utilities give an measurement of value for each level, of each attribute, in terms of its contribution to the choices that were made and so shows the relative value of one level against another.

Market models - forecasting market potential

The statistical output gives a detailed quantified picture of how customers make decisions, and a set of data that can be used to build market models which can predict preferences or estimate market share in new market conditions in order to forecast the impact of product or service changes on the market. For businesses this allows them to see where and how they can gain the greatest improvements over their competitors, where they can add value for the customer, how price impacts on decisions and so forecast demand and revenue. Not surprisingly conjoint analysis has become a key tool in building and developing market strategies .

By combining these market models with internal project costings, companies can evaluate decisions in terms of Return on Investment (ROI) before going to market. For example determining what resources to put into New Product Development and in what areas. Choice-based conjoint or discrete choice modelling also form the basis of much pricing research and powerful needs-based segmentation .

"We were looking for an agency that could understand our solutions and complex customer base in order to transfer this understanding into a comprehensive customer survey. dobney.com quickly gained deep insight into the specificities of our business and designed an excellent, state-of-the-art conjoint survey. They delivered professional and individual service of a quality we had never experienced before. It was great working with dobney.com and the findings derived from the survey are invaluable for us."

Marketing Manager, Leica Microsystems

Alternatives to conjoint - from maxdiff to configurators and e-commerce mock-ups.

Conjoint analysis is relatively complex as it requires an understanding of how to use and create attributes and levels, what flavour to use, how to make the product profiles, what choice task to offer and then how to analyse the data and build the market model. It is possible to use off-the-shelf software which will provide guidance and help, but it can be also make it easy to make mistakes or generate poor designs. And conjoint analysis doesn't always fit, particularly if there are many levels, or a deeper more emotional drive to decision making. So, depending on the product or service, it is possible that off-the-shelf approaches aren't always suitable and other methods are needed. Fortunately there are a number of related approaches used as alternatives to conjoint analysis , such as MaxDiff, configurators or Simalto (also known as trade-off grids). MaxDiff is more about measuring the value from a list of items, than generating complete products, but it uses many of the same features and analytics as conjoint. Simalto, like conjoint analysis, breaks products down into attributes and levels, but then presents them as a grid of options to respondents.

A range of other research techniques including menu building (building a configured product from a range of selected options), and search and filter studies in the form of e-commerce style mock-ups where respondents hunt for their most preferred products can also be used in conjunction with or as alternatives to conjoint analysis.

Demonstrations and further reading

To see the mathematical workings, we have a fully worked up simple conjoint analysis worked example in Excel to show how conjoint analysis functions mathematically to estimate part-worths or utilities from design to analysis. Or just play with our Pizza demonstration which shows how utility estimations arise from choices about pizza preferences.

• See our interactive instant conjoint analysis demonstration showing how customer value can be calculated from choices.
• See how conjoint market models and simulators work to enable better ROI decisions based on customer values.
• Explore conjoint analysis in-depth with our online Conjoint Explorer which allows you to design and test your own attributes and levels and choice tasks, and see results.
• Conjoint analysis design principles
• Conjoint analysis types or flavours

For help and advice on using conjoint analysis for market modelling, or to carry out conjoint analysis research email [email protected]

• Conjoint Analysis overview
• Conjoint demonstration
• Attributes and levels
• Flavours of conjoint analysis
• Conjoint models
• Conjoint in Excel
• MaxDiff plus
• Uses of conjoint types
• International conjoint
• Conjoint analysis alternatives
• Conjoint Explorer

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What is conjoint analysis for market research?

What is the customer willing to pay for the product? Which product features are important for selling the product? Which features should the product development team prioritize? Which features are more important, and which are less important?

Have you ever asked yourself some of these questions and wanted to know the answer in an objective way? If so, you should consider implementing conjoint analysis into your market research.

In this article, you will learn what conjoint analysis is, how to design and execute it, and read examples of its implementation within product teams.

What is conjoint analysis?

Conjoint analysis is a statistical method often used by product managers to conduct market research and evaluate how customers value different product attributes.

For product managers, it’s important to know which attributes of the product increase the perceived value for the customers the most. This way you can focus on the most valuable features first and gain higher returns on investments in the development of the product.

Conjoint Analysis is one of the tools which can be used to gain these insights. The base assumption is that each product can be divided into different product attributes or product characteristics like product features, design elements, or price.

Consumers compare products with these attributes to find and buy a product that suits them the best. These attributes vary from product to product and are an important factor that customers use to determine the value of those products. So, these variations are used by product managers to create unique selling propositions (USPs) and find a product market fit .

With conjoint analysis a product managers can:

• Better select valuable product features for implementation
• Assess the right pricing strategy for a product
• Compare your own product with the competitors’ products
• Optimize the marketing and positioning of the product
• Find the right target customer groups and market segments

Key elements of conjoint analysis

In conjoint analysis, a product is broken down into its attributes and characteristics. The product manager identifies the attributes that are of the greatest interest for the conjoint analysis and collects the characteristics of these attributes for his product and competing products.

The attributes can include product features, design elements, prices, and brand names.

As these attributes differ between products, these differences can be used in customer surveys to identify customer preferences and gain insights for product development.

The product manager defines these differences per attribute in a set of levels like:

• Attribute — Size
• Levels — Small, medium, and large

Each product is listed in product profiles and presented to potential customers in surveys with specific questions.

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The type and style of how these surveys are built differ depending on the type of conjoint analysis you choose.

• Traditional conjoint analysis — Here Respondents rank or rate scenarios
• Choice-based conjoint analysis (CBC) — Respondents choose their most preferred scenario from a set of multiple-choice scenarios
• Discrete choice conjoint analysis (DCC) — Similar to CBC, respondents choose one preferred scenario from a limited set of options

Conjoint analysis example

Here is an example of a simple conjoint analysis comparing three different recruitment apps:

We will consider four attributes:

• User interface
• Job listings
• Resume builder

Each attribute has up to three levels:

• Average — 0
• Excellent — 2

Below is a table visualizing the three profiles of the apps:

The respondents will be asked to rank these apps in order of their preference, from most preferred to least preferred. For example, their ranking might look like this:

Another respondent’s ranking might be:

After collecting rankings from multiple respondents, the data will be analyzed to determine the utility values for each attribute level and the overall preference for each app. The results will help identify which attributes are most influential in driving app preferences and which app is most preferred overall by respondents.

The analysis of the data is a mathematical process. Analyzing conjoint survey results is complicated and prone to measurement errors. Often participants don’t know exactly why they choose one thing over the other.

Survey results can induce substantial bias in any direction and by any amount; this bias must be corrected with mathematical processes. Econometric and statistical methods are used to estimate a utility function for each attribute and level of the attribute.

These utility functions indicate the perceived value of the attribute and show how consumer preferences are prone to change when the level of the attribute changes.

How to design and execute a conjoint analysis study

To design and execute a conjoint analysis study, you must be clear about the objective of the research. Depending on the objective and the complexity of the questions, the study needs to be designed in different ways and different conjoint analysis types can be chosen.

The desired outcome could offer insights such as:

• Identifying customer preferences
• Optimizing feature sets
• Understanding pricing sensitivity

After setting the objective the product manager must:

• Define attributes and levels of each attribute — It’s a best practice to not model too many attributes per profile. Keep it between 3 to 10 attributes and 3 to 5 levels per attribute
• Design a choice set of products to provide in a survey — To not overwhelm respondents in the survey, keep the sample set small
• Design a survey questionnaire matching the preferred conjoint analysis — Based on the format, it’s necessary to use proper tooling for this step. This is especially for dynamic surveys
• Execute the survey to collect data — Try to understand the respondents’ demographics and filter out respondents who do not suit your target group right at the beginning
• Analyze the data using a proper tool — Most tools use mathematical models and methods like hierarchical Bayes estimation
• Calculate the part-worth utility value — Use a tool to understand the preference values of each attribute level
• Interpret the results with consideration of the research objective — Find out which attributes determine the preference of a profile the most
• Act on the results — Define a feature set for your product and alter the pricing model accordingly

With the results of the conjoint analysis survey and the mathematical model in the background, you can even use the model to simulate how the preference will change for a certain product when attribute levels are changed.

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Conjoint analysis case studies

Conjoint analysis is not bound to physical products. It can be used across all industries for physical products and services alike. You can also use it for different scenarios like identifying the right price or improving the product and service offering.

The following case studies illustrate how you could use conjoint analysis:

A fitness equipment manufacturer

Better mousetraps, improved phone service.

A company producing rowing machines was using conjoint analysis surveys to evaluate which features are most important for younger consumers who want to stock up on their home gyms. They asked different questions about the features of a new rowing machine model and found out that younger buyers would like to have rowing machines with the following attributes:

• Easier folding probabilities
• Touchscreens to play videos and see health metrics
• On-demand virtual coaching classes
• Silencing technology to keep noise level down
• Affordable compared to other competitors

They found out that different groups prioritize different things first. One group preferred convenience and quiet use, another prioritized high-tech interactive features, and another mainly looked at the price.

To optimize their products and services, a big pest control company used conjoint analysis to gain insights into the demands of modern customers. They asked in a survey what an improved mousetrap should look like. They figured out that:

• The trap should have an alert system synchronized with the smartphone
• The trap should send out low-frequency beeping sounds to keep mice outdoors
• There should be a mercy model which catches the mice without harming them
• There should be a subscription-based carefree service model where someone comes to your house and maintains the traps

After developing some of these insights customer satisfaction increased.

A phone answering service improved its offering by using a conjoint analysis based on the following attributes:

• On-demand, personal answering support
• Follow-up communication via text and social media messaging
• A pay-per-call model
• A company calendar management service
• An up-selling consulting service that helps customers to up-sell their products via phone

They identified easy ways to improve their services with little effort but with great value increases for their customers.

Advantages and disadvantages of conjoint analysis

Designing conjoint studies is complex. When too many product features and product profiles are chosen, respondents may often feel overwhelmed and tend to simplify the answers to questions.

The mathematical model that supports conjoint analysis is also very complex. The results and the way they’re calculated may not be easy to understand and interpret.

When conjoint analysis studies are poorly designed, they may overvalue product attributes which trigger emotional responses, and undervalue concrete features and important hard facts.

In the survey, the respondents are presented with all the attributes of a profile. In real life, the product positioning is harder and the consumers seldom have all the facts presented in this way. The conjoint analysis can therefore only be a reference and not directly put into practice.

On the other hand, conjoint analysis has numerous advantages. Above all, the fact that psychological mechanisms play a role in decision-making in conjoint analysis is an advantage. After all, emotions also play an important role in the real buying process.

In addition, conjoint analysis presents several attributes to the respondent in a combined manner, which corresponds better to reality than a survey in which individual attributes are queried.

In addition, conjoint analysis relates the various attributes to each other, which means that the most important factors for the user’s preferences can be identified.

Tools to design and execute conjoint analysis

There are plenty of tools out there that support product managers and market researchers with their conjoint analysis. The following are the most common:

• Sawtooth Software — Sawtooth Software is one of the most widely used and comprehensive tools for conjoint analysis. It offers various conjoint analysis techniques, including CBC, ACA, and MaxDiff. Sawtooth Software provides both standalone software packages (like Lighthouse Studio) for advanced users and online survey platforms (like Discover) for more straightforward studies
• SurveyMonkey — SurveyMonkey is another widely used online survey tool that supports conjoint analysis. While it may not have advanced conjoint analysis features like Sawtooth Software or Qualtrics, it can still be used for basic conjoint studies
• Conjoint.ly — Conjoint.ly is an online platform dedicated to conjoint analysis and related research methods. It offers automated conjoint analysis and simulation tools to analyze results and derive insights

Final thoughts

With the right preparation and a good selection of attributes and levels, conjoint analysis can give a product manager helpful insights into consumer needs. It can be used for pricing and competitive product analysis. At the same time, conjoint analysis can provide helpful insights into consumer behavior during the initial market research for a new product.

Due to the simplicity of the survey, there is no obstacle for the survey participants to take part in the conjoint study. Participants are only called upon to compare different profiles, which closely simulates a real purchase process. As a result, psychological mechanisms that play a role during the buying process are also included and flow into the conjoint analysis results.

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