research paper for direct marketing

• Open access
• Published: 15 September 2020

Direct marketing in health and medicine: using direct mail, email marketing, and related communicative methods to engage patients

• James K. Elrod 1 &
• John L. Fortenberry Jr. 1 , 2  

BMC Health Services Research volume  20 , Article number:  822 ( 2020 ) Cite this article

12k Accesses

5 Citations

1 Altmetric

Metrics details

Direct marketing—the delivery of messages via mail, the Internet, and similar routes directly to consumers—is used extensively by healthcare organizations to attract and inform current and prospective patients of health and medical offerings and opportunities. Examples of direct marketing include direct-mail marketing, telemarketing, and Internet marketing, with routes being selected on the basis of their ability to reach desired audiences. The various avenues offered by direct marketing afford options to address most any sought group.

Direct marketing is one of the most recognized forms of marketing communication, thanks in large part to its widespread use and direct engagement of consumers. While some applications clearly have the potential to irritate consumers (e.g., junk mail in post boxes, spam in email inboxes), direct marketing can be deployed in manners respectful of recipients and, in such cases, it can prove to be a helpful communications asset. To aid others in understanding this particular conveyance method, this article presents an overview of direct marketing and shares deployment insights and experiences from Willis-Knighton Health System.

Conclusions

Direct marketing provides a useful communications pathway, permitting health and medical institutions to educate and enlighten desired audiences. Given instances of overuse and misuse by organizations, however, great care must be taken to design and deploy direct marketing initiatives inoffensively. If well designed and respectfully implemented, direct marketing affords significant communications utility, earning a valued place in the marketing communications arsenals of healthcare establishments.

Patient acquisition and retention activities constitute some of the most important tasks conducted by health and medical establishments [ 1 , 2 , 3 , 4 , 5 , 6 ]. Without consistent and, ideally, growing patient volume, institutional viability becomes uncertain, potentially threatening the very existence of given medical providers and, in turn, the livelihoods of their employees and the health status of the people they serve [ 1 , 7 , 8 , 9 ]. Seen in this light, inabilities to attract and retain patients carry consequences well beyond the walls of given healthcare establishments, extending deeply into markets and even impacting community health [ 10 , 11 ]. This fact, of course, provides significant motivation, compelling many healthcare providers to direct intensive efforts toward ensuring that ongoing streams of patients are secured, fostering viability that affords mutual benefits [ 1 , 7 ].

In pursuing viable patient streams, communication proficiencies are essential, affording health and medical establishments with the ability to engage, inform, and attract current and prospective customers. Ultimately, this yields all-important patient volume and institutional market share [ 3 , 5 , 12 , 13 ]. Pathways abound for reaching desired audiences, with one in particular, known as direct marketing, effectively delivering messages from given healthcare institutions directly to sought audiences, typically via mail, telephone, or Internet communications tools [ 1 , 7 ]. Direct marketing is heavily utilized by health services organizations, with applications ranging from postcards mailed to prospects which introduce newly available medical technologies to emails which invite recipients to attend the open houses of given healthcare establishments [ 1 , 5 , 6 , 7 , 8 ].

Direct marketing is one of the most recognized forms of marketing communication, thanks in large part to its widespread use and direct engagement of consumers. While some applications clearly have the potential to irritate consumers (e.g., junk mail in post boxes, spam in email inboxes), direct marketing can be deployed in manners respectful of recipients and, in such cases, it can prove to be a helpful communications asset [ 14 , 15 , 16 ]. To aid others in understanding this particular conveyance method, this article presents an overview of direct marketing and shares deployment insights and experiences from Willis-Knighton Health System.

Definition and overview

Direct marketing is one of many elements constituting the broad discipline of marketing, formally defined as “a management process that involves the assessment of customer wants and needs, and the performance of all activities associated with the development, pricing, provision, and promotion of product solutions that satisfy those wants and needs” [ 1 ], p. 288. Promotion, as evidenced in this definition, is a core feature of marketing, earning inclusion as one of the Ps in the classic expression known as the four Ps of marketing (i.e., Product, Price, Place, Promotion). The promotion aspect of marketing essentially entails any and all elements associated with engaging audiences, with the core pathways for engagement being depicted in a descriptive model known as the marketing communications (or promotions) mix [ 1 , 17 ].

Classically illustrated, the marketing communications mix contains five principal avenues of communication; namely, advertising (i.e., the paid use of mass media to deliver messages), personal selling (i.e., the use of sales agents to personally deliver messages), sales promotion (i.e., the use of incentives, such as contests and free giveaways, to encourage patronage), public relations (i.e., the use of publicity and other unpaid promotional methods to deliver messages), and direct marketing (i.e., the delivery of messages via mail, the Internet, and similar routes directly to consumers) [ 1 , 7 ]. Healthcare providers examine each of these communicative avenues, selecting one or more believed to be most capable of reaching target audiences, with the ultimate goal being to encourage patronage or compel some other desired action [ 1 , 9 ].

Direct marketing is characterized by its conveyance of information directly to individuals. Unlike advertising, which uses mass media to deliver messages to broad audiences en masse, hoping to entice interested parties into some form of desired exchange, direct marketing engages individuals directly by sending, for example, a promotional brochure, email message, or similar conveyance straight to the intended recipient. It is a highly targeted form of communication and, as such, is highly measurable, as responses to various solicitations can be tracked with relative ease [ 14 , 15 , 16 , 18 ]. Historically, direct marketing often brought to mind telemarketing or direct mail, but times have changed. Today, telemarketing has been deemed by society to be so undesirable that its use is now highly regulated, diminishing opportunities and associated presence considerably. Even though direct mail often is characterized by recipients as junk mail, it continues to be used quite heavily, although shifts to electronic forms of communication have diminished its popularity. The Internet indeed has ushered in numerous direct marketing opportunities, ranging from email appeals to newsletter distribution to social media engagements. This particular avenue is evolving rapidly and almost certainly represents the future of direct marketing methods [ 1 , 14 , 16 ].

One of the most critical tasks associated with direct marketing pertains to building lists of prospects who will be targeted with associated solicitations [ 5 , 14 , 15 ]. Prospect lists often are purchased from vendors who specialize in the provision of such, permitting healthcare establishments to designate recipient characteristics (e.g., ZIP code, gender, age, interests, etc.) and request lists of candidates meeting associated criteria. Although more labor intensive, health and medical institutions can opt instead to build their own lists. Assembling these lists typically begins by asking current customers if they would like to receive collateral, such as monthly newsletters, invitations to special events, promotional messages detailing new healthcare options, and so on, adding those desirous of such to a direct marketing database. Invitations to join mailing lists, subscribe to social media news conveyances, and the like also can be inserted into other marketing communications, with those opting in being added to associated direct marketing repositories. With concerted efforts over time, custom lists will grow and become true institutional assets, typically exceeding the value derived from their more generic, purchased counterparts [ 5 , 7 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].

How lists are used arguably is just as important as quality of given lists. Simply collecting contact details and sending solicitations whenever healthcare institutions please is decidedly poor form and likely will engender the animosity of recipients. This practice historically has been used by many organizations and unfortunately continues to this day, perpetuating negative feelings regarding direct marketing [ 5 , 7 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Such animosity can be reduced or eliminated entirely by practicing what is known as permission marketing, requiring that institutions request and be granted permission before forwarding solicitations to intended recipients and, for those granting permission, offering easy methods to opt out of future solicitations [ 16 , 22 ].

Delivery without permission to do so can damage institutional reputations and lead to senders being labeled “junk mailers,” “spammers,” and the like for directing unsolicited and often unwanted correspondence to individuals. As tempting as it might be to send promotional messages without an invitation, it should be avoided, as the main aim of healthcare communications rests with establishing a productive dialogue, a mission immediately destroyed by intruding on the personal space of audiences [ 5 , 6 , 7 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Of course, beyond the assembly of lists and their proper use, the information contained in direct marketing pieces must have value to recipients. If healthcare providers develop direct marketing programs that fulfill these mandates, they can expect good things from their associated programs.

Institutional background, deployment history, and context within marketing communications

From its earliest of days, dating back to 1924, Willis-Knighton Health System has emphasized communications excellence, something which in present times remains a strategic priority, compelling extensive communicative experimentation and innovation. Headquartered in Shreveport, Louisiana and situated in the heart of an area known as the Ark-La-Tex where the states of Arkansas, Louisiana, and Texas converge, Willis-Knighton Health System holds market leadership in its served region where it delivers comprehensive health and wellness services through multiple hospitals, numerous general and specialty medical clinics, an all-inclusive retirement community, and more. The institution’s achievement of market leadership is attributed, in part, to communications prowess, permitting Willis-Knighton Health System to effectively engage current and prospective patients, evoking interest and attention, ultimately leading to burgeoning patient volume and customer loyalty.

Today, Willis-Knighton Health System leverages the power of the full marketing communications mix, deploying all of its components, including direct marketing. The establishment’s use of direct marketing has been fairly consistent over its history, best characterized as a modest deployment, primarily used to complement other forms of marketing communication. Its use in this manner stems simply from Willis-Knighton Health System’s preferences for other forms of marketing communication which, based on the institution’s experiences, are more effective at achieving its designated communications goals. For direct marketing’s part, associated applications deployed by Willis-Knighton Health System have evolved as the particular medium of communication and consumer preferences have advanced over time.

Of major forms of direct marketing, telemarketing, which entails contacting desired audiences via telephone, historically has been used sparingly by Willis-Knighton Health System due to the practice’s disruptive nature. In recent decades, direct mail, which entails sending solicitations via post, has accounted for the majority of direct marketing deployments by the institution, with the typical application being in the form of postcards introducing new physicians, communicating new services, and so on to inform and enlighten recipients, as demonstrated by the example presented in Fig. 1 . Willis-Knighton Health System’s most prominent direct mail effort is the institution’s healthy lifestyles magazine known as Vim & Vigor [ 23 ]. This magazine features a variety of health and wellness stories, along with helpful details about Willis-Knighton Health System and its associated services. Vim & Vigor is mailed to individuals and institutions throughout the Ark-La-Tex region and serves as a helpful vehicle for building and maintaining awareness.

A postcard promoting Willis-Knighton Health System’s Quick Care Urgent Care Center

Postal distribution of Vim & Vigor is complemented by electronic distribution via Willis-Knighton Health System’s website, with recent issues being viewable at the following link: https://www.wkhs.com/about/vim-vigor . This move was inspired by very clear trends which indicate increasing use of and preferences for Internet-based communications by consumers. This particular avenue of communication is capturing an expanding share of the institution’s direct marketing dollar as paper-based and posted methods fall out of favor as Internet communications continue to develop and proliferate [ 3 ]. In keeping with this trend, Willis-Knighton Health System has directed more attention toward electronically-distributed direct marketing applications, with the most notable initiatives being email-directed communications and subscriptions to social media platforms, following a permission marketing protocol.

Although occupying a relatively small portion of Willis-Knighton Health System’s overall marketing communications budget, direct marketing is fulfilling its designated purpose, complementing other marketing communications deployed by the institution. The medium, of course, is capable of more robust deployments, with the method and manner of given applications being determined based on the wants and needs of particular establishments. Regardless of approach and intensity, care must be taken to ensure that deployments are respectful of recipients, something that is essential in order for direct marketing to fulfill its intended purpose of informing and enlightening recipients.

Willis-Knighton Health System’s observations from its direct marketing experiences suggest a number of strengths, compelling the avenue’s inclusion in the institution’s marketing communications mix composition. Chief strengths of direct marketing are described as follows.

Ability to be precisely targeted

As suggested by its name, direct marketing is forwarded directly to individual recipients, affording precise targeting which permits personalization and minimizes wasted circulation [ 5 , 6 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Something as simple as addressing a parcel using a person’s actual name, as opposed to “Resident” or something equally nondescript, can improve the chances that the parcel will be noticed. This is all the more the case for more robust forms of customization, such as inserts which reference the recipient by name, and, with permission marketing elements in place, forwarding information for which the individual has already expressed an interest in receiving. Without such customization, direct marketing applications are more likely to be disregarded, constituting circulation which delivers no value. Since the goal of direct marketing is to engage, the investment required to compel someone to actually look at pieces received makes perfect sense. Doing the opposite and deciding to simply send generic parcels will foster inattentiveness and yield a diminished return on investment. As such, healthcare institutions engaged in direct marketing should take advantage of the communicative method’s precision targeting capability and customize conveyances, accordingly.

Highly measurable performance

Unlike many forms of marketing communication, direct marketing happens to be highly measurable, something enhanced further by particular creative applications, permitting establishments to better determine return on investment [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Direct mail pieces, for example, can feature a particular telephone number in the given message, providing reasonable assurances that calls directed to that number were generated by the noted direct mail campaign. A newsletter distributed to recipients which conveys a special offer that is only promoted in the given communication provides similar opportunities to ascertain impact by tying results to the particular direct marketing campaign. Direct marketing applications placed via the Internet permit a wealth of tracking opportunities through such things as the inclusion of web links tied to given campaigns, the ability to access data analytics details, including recipient click behaviors, and more, giving perhaps the greatest utility for assessing campaign results. Such measurability aids in shaping and honing approaches by reviewing experiences of prior campaigns, making adjustments as needed for improvements in future campaigns. This also is most helpful in determining—and justifying—marketing communications budgets.

Potential to convey significant information

Direct marketing efforts have the potential to telegraph significant amounts of information [ 5 , 6 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Whereas a 30-second television commercial can only effectively convey a limited amount of detail, direct marketing, courtesy of its delivery methods, can feature a wide range of information. This is especially helpful in the health services industry where offerings typically are highly complex, necessitating robust details in order for consumers to make informed decisions. A direct mail parcel posted to residents might, for example, contain a multipage brochure, information booklet, or other form of collateral, conveying extensive facts and figures which can be consumed at the leisure of recipients. Internet communications offer the same potential, including the convenience of featuring helpful web links which can be directly accessed by the recipients of given communications. This particular attribute actually works quite well for reinforcing other forms of marketing communication, with advertising building top-of-mind awareness for, say, a given medical procedure, and a complementary direct marketing campaign delivering enhanced information to bolster awareness of the given innovation, yielding effective marketing communications synergies.

Limitations

Motivations to use direct marketing are counterbalanced by a series of limitations which must be factored into applications so as to minimize or avoid associated effects. Notable limitations are described as follows.

Potential for intrusiveness

Direct marketing, courtesy of its direct engagement attribute, has the potential to intrude on the privacy of recipients, something which is magnified when organizations carelessly and selfishly deploy the medium of communication [ 5 , 6 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Many have experienced, for example, unrelenting telephone solicitations, junk mail cluttering mailboxes and hampering one’s ability to locate meaningful parcels, and spam messages crowding email inboxes and diminishing associated utility. Such nuisances have harmed the reputation of direct marketing, necessitating extreme care in its deployment. As described in prior sections, the potential for intrusiveness can be diminished or eliminated entirely with the establishment of a protocol which requires receipt of permission prior to forwarding direct marketing communications to targets. Doing so eliminates wasted circulation and ensures that direct marketing efforts are not reputationally damaging by overstepping the boundaries of recipients.

Potential to be overlooked

The volume of direct marketing efforts forwarded to consumers generally is staggering, overwhelming recipients who often will not take the time to separate meaningful and relevant communications from the many which are not. The end result of this proliferation is consumer inattentiveness to direct marketing appeals [ 5 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. This, of course, makes it very difficult for institutions forwarding targeted, relevant, and respectful direct marketing communications to break through the clutter to win the attention of recipients. Stories of people visiting their mailboxes, grabbing parcels, and disposing of junk mail without as much as a second glance abound, as do accounts of individuals opening their email inboxes and deleting messages en masse, not wishing to take the time to screen the sea of solicitations often flooding their accounts. Breaking through the clutter is challenging for anyone engaged in direct marketing, with perhaps the best method for doing so being through the deployment of highly creative applications which capture attention. These, of course, must be infused with communications which are relevant and delivered respectfully.

Database management challenges

As noted in prior sections of this article, the success of direct marketing campaigns is heavily reliant on the quality of the recipient list. Quality must extend beyond correct contact details, reaching into deeper things like communication preferences (e.g., mail, telephone, email), content desired (e.g., promotional messages from the host organization, promotional messages from partner organizations), relevance to the recipient (e.g., sports medicine services for athletically-inclined individuals, senior-related health services for senior citizens), and the like. These details must be managed properly and updated in a timely fashion, with this exercise representing a significant challenge [ 5 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 24 ]. Proper database management also is essential for ensuring that direct marketing permissions, which can and do change often, are accurate. Those opting in or opting out must be registered as such immediately as part of prudent efforts to ensure that audiences are addressed as they desire. Resources obviously are required for database management endeavors, ranging from information systems to personnel charged with overseeing and effecting processes. Despite associated challenges, such investments have the potential to dramatically improve direct marketing efforts.

Operational reflections

For administering any component of the marketing communications mix, Willis-Knighton Health System advises establishing a baseline foundation of resources, including (1) top leadership support and commitment, (2) financial resources sufficient for funding communications activities, (3) competent personnel charged with effecting given initiatives, and (4) formal processes permitting effective planning, implementation, and evaluation of initiatives. Adequate resources set the stage for productive audience engagement endeavors, minimizing chances of resource-depleting and reputation-damaging mistakes which, in the realm of marketing communications, often are very public, given the open circulation of such conveyances. These resources also ensure competencies in using given marketing communications mix components, with proper deployment being essential for realizing desired outcomes.

Beyond the advisories conveyed elsewhere in this article, Willis-Knighton Health System suggests that health and medical establishments considering the use of direct marketing make certain that they carefully consider the total costs of the particular applications under examination. Minimally, healthcare entities will need to consider the costs associated with (1) development of creative content, (2) production of associated collateral, such as printing in the case of direct mail, (3) purchasing or building a recipient list, including database management expenses, (4) distribution fees—such as postage in the case of direct mail—associated with forwarding direct marketing communications to recipients, and (5) labor costs associated with effecting given campaigns. Once expenditures are tallied, healthcare providers then are positioned to compare the costs of the proposed direct marketing campaign with the costs of other forms of marketing communication, providing a useful evaluative measure for marketing communications planning. Such examinations aid in ensuring that total costs are considered whenever contemplating direct marketing campaigns, permitting health and medical organizations helpful assistance in determining the most prudent avenues available for achieving designated communicative goals.

Direct marketing provides a useful communications pathway, allowing health and medical institutions to educate and enlighten desired audiences. This sets the stage for acquiring patronage and resulting market share, yielding numerous mutual benefits for given establishments and their communities. As with all forms of marketing communication, care must be taken to deploy direct marketing properly, capitalizing on its strengths while avoiding applications that evoke its limitations. Database assembly and management activities are particularly critical and a permission marketing mindset is imperative for achieving the best results. Further, given instances of overuse and misuse by organizations, great care must be taken to design and deploy direct marketing initiatives inoffensively. If well designed and respectfully implemented, direct marketing affords significant communications utility, earning a valued place in the marketing communications arsenals of health and medical organizations.

Availability of data and materials

Not applicable.

Fortenberry JL Jr. Health care marketing: tools and techniques. 3rd ed. Sudbury: Jones and Bartlett; 2010.

Google Scholar  

Feinberg D. Why advertise … and why not? Mark Health Serv. 2011;31(2):3–5.

PubMed   Google Scholar  

Elrod JK, Fortenberry JL Jr. Formulating productive marketing communications strategy: a major health system’s experience. BMC Health Serv Res. 2018;18(Suppl 3):926.

Article   Google Scholar  

Elrod JK, Fortenberry JL Jr. Billboard advertising: an avenue for communicating healthcare information and opportunities to disadvantaged populations. BMC Health Serv Res. 2017;17(Suppl 4):787.

Berkowitz E. Essentials of health care marketing. 4th ed. Burlington: Jones and Bartlett; 2017.

Thomas RK. Marketing health services. 3rd ed. Chicago: Health Administration Press; 2014.

Fortenberry JL Jr. Cases in health care marketing. Sudbury: Jones and Bartlett; 2011.

Kotler P, Shalowitz J, Stevens RJ. Strategic marketing for health care organizations: building a customer-driven health system. San Francisco: Jossey-Bass; 2008.

Fortenberry JL Jr. Nonprofit marketing. Burlington: Jones and Bartlett; 2013.

Schiavo R. Health communication: from theory to practice. 2nd ed. San Francisco: Jossey-Bass; 2014.

Parvanta CF, Nelson DE, Harner RN. Public health communication: critical tools and strategies. Burlington: Jones and Bartlett; 2018.

Elrod JK, Fortenberry JL Jr. Healthcare establishments as owner-operators of digital billboards: making the most of excellent roadside visibility and high traffic counts to better connect with patients. BMC Health Serv Res. 2018;18(Suppl 3):928.

Elrod JK, Fortenberry JL Jr. Driving brand equity in health services organizations: the need for an expanded view of branding. BMC Health Serv Res. 2018;18(Suppl 3):924.

Nash E. Direct marketing: strategy, planning, execution. 4th ed. New York: McGraw-Hill; 2000.

Stone B, Jacobs R. Successful direct marketing methods: interactive, database, and customer marketing for the multichannel communications age. 8th ed. New York: McGraw-Hill; 2008.

Kotler P, Keller KL. Marketing management. 14th ed. Upper Saddle River: Prentice Hall; 2012.

Elrod JK, Fortenberry JL Jr. Catalyzing marketing innovation and competitive advantage in the healthcare industry: the value of thinking like an outsider. BMC Health Serv Res. 2018;18(Suppl 3):922.

Perreault WD Jr, Cannon JP, McCarthy EJ. Basic marketing: a marketing strategy planning approach. 19th ed. New York: McGraw-Hill; 2014.

Belch GE, Belch MA. Advertising and promotion: an integrated marketing communications perspective. 11th ed. New York: McGraw-Hill; 2018.

Andrews JC, Shimp TA. Advertising, promotion, and other aspects of integrated marketing communications. 10th ed. Boston: Cengage; 2018.

O’Guinn TC, Allen CT, Scheinbaum AC, Semenik RJ. Advertising and integrated brand promotion. 8th ed. Boston: Cengage; 2019.

Godin S. Permission marketing: Turning strangers into friends and friends into customers. London: Simon and Schuster; 1999.

Elrod JK. Breadcrumbs to cheesecake. Shreveport: R&R Publishers; 2013.

Tapp A, Whitten I, Housden M. Principles of direct, database, and digital marketing. 5th ed. London: Pearson; 2013.

Download references

Acknowledgments

A special note of thanks is extended to Marilyn Joiner and the greater Willis-Knighton Health System family for their helpful assistance throughout the development and publication of this article.

About this supplement

This article has been published as part of BMC Health Services Research Volume 20 Supplement 1, 2020: Marketing communications in health and medicine: perspectives from Willis-Knighton Health System. The full contents of the supplement are available online at http://bmchealthservres.biomedcentral.com/articles/supplements/volume-20-supplement-1 .

Article processing charges were funded by Willis-Knighton Health System.

Author information

Authors and affiliations.

Willis-Knighton Health System, 2600 Greenwood Road, Shreveport, LA, 71103, USA

James K. Elrod & John L. Fortenberry Jr.

LSU Shreveport, 1 University Place, Shreveport, LA, 71115, USA

John L. Fortenberry Jr.

You can also search for this author in PubMed   Google Scholar

Contributions

The authors jointly developed the submitted manuscript, with each performing critical roles from early conceptualization through to the production of the full manuscript. The manuscript resulted from a collaborative effort. Both authors read and approved the final manuscript.

Authors’ information

JKE is President and Chief Executive Officer of Shreveport, Louisiana-based Willis-Knighton Health System, the region’s largest provider of healthcare services. With over 55 years of service at the helm of the institution, JKE is America’s longest-tenured hospital administrator. A fellow in the American College of Healthcare Executives and honoree as a Louisiana Legend by Friends of Louisiana Public Broadcasting, he holds a bachelor’s degree in business administration from Baylor University, a master’s degree in hospital administration from Washington University School of Medicine, and an honorary doctorate of science and humane letters from Northwestern State University of Louisiana. He is the author of Breadcrumbs to Cheesecake , a book which chronicles the history of Willis-Knighton Health System.

JLF Jr. is Chair of the James K. Elrod Department of Health Administration, James K. Elrod Professor of Health Administration, and Professor of Marketing in the College of Business at LSU Shreveport where he teaches a variety of courses in both health administration and marketing. He holds a BBA in marketing from the University of Mississippi; an MBA from Mississippi College; a PhD in public administration and public policy, with concentrations in health administration, human resource management, and organization theory, from Auburn University; and a PhD in business administration, with a major in marketing, from the University of Manchester in the United Kingdom. He is the author of six books, including Health Care Marketing: Tools and Techniques , 3rd Edition, published by Jones and Bartlett Learning. JLF Jr. also serves as Vice President of Marketing Strategy and Planning at Willis-Knighton Health System.

Corresponding author

Correspondence to John L. Fortenberry Jr. .

Ethics declarations

Ethics approval and consent to participate, consent for publication.

Figure 1 is published with permission of Willis-Knighton Health System. All required approvals for publication were obtained.

Competing interests

JKE and JLF Jr. are both employed with Willis-Knighton Health System.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Elrod, J.K., Fortenberry, J.L. Direct marketing in health and medicine: using direct mail, email marketing, and related communicative methods to engage patients. BMC Health Serv Res 20 (Suppl 1), 822 (2020). https://doi.org/10.1186/s12913-020-05603-w

Download citation

Published : 15 September 2020

DOI : https://doi.org/10.1186/s12913-020-05603-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Direct marketing
• Marketing communications

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

The direct marketing-direct consumer gap: qualitative insights

2001, Qualitative Market Research: An International Journal

Related Papers

International Journal of Public Administration in the Digital Age

Pratap chandra Mandal

Companies require information about customers to understand them, know their preferences, and develop relationships with them. Companies employ a number of direct and digital marketing channels to collect information and intelligence about customers. Marketers adopt a number of unfair practices for collecting information through direct and digital marketing and this raises concerns about consumer privacy. The article discusses the various aspects of consumer privacy and the need for protecting consumer privacy. The article further focuses on the various regulations enforced by regulatory bodies and governments of countries to protect customers. Various initiatives taken by companies to protect customers are discussed. Direct and digital marketing channels allow companies to collect information and intelligence about customers and to influence them. However, companies should be sensitive to customer concerns. This will help companies in building long-term customer relationships.

Journal of Direct Marketing

Martin Block

Lisa O'Malley , Maurice Patterson

The relationship marketing paradigm is gaining increasing credence in consumer markets. Marketers need to get close to their customers to establish exchange relationships, and this “intimacy” is achieved using database technology and direct marketing communications. However, in implementing relationship marketing in this way, the potential exists for the use of technology to result in invasions of individual consumer privacy. The literature on relationship marketing and privacy are reviewed, and the findings of six exploratory focus group discussions, conducted in the UK, are presented. The study suggests that what some consumers define as “intrusion” is similar to what has been called “intimacy” by marketers. Nine propositions are presented which reflect the implications of current approaches to relationship marketing in consumer markets. In particular, the integral elements of meaningful relationships are absent or inhibited as a result of consumer concerns over intrusions of their privacy. Thus, marketers need to adapt their attitudes, behaviours, and processes in order to accommodate the participation of the consumer in the relationship. The implications for marketers are identified, and directions are offered for future research.

Journal of Business Research

Pradeep Korgaonkar

Journal of Advertising

Sara Dolnicar

International journal of health sciences

Tabrez Quadree

In the era of the competitive business landscape, every company seeks to be the category-lead but it is not an easy-going task, especially in case of Direct selling business. To be the category lead, a company is required to differentiate its brand over competitors in terms of product value proposition, pricing, customer service, and other constructs that bind the customers who have been loyal to the brand for a long time. Understanding consumers’ buying behaviour is a vital task in this regard, failing which any organisation struggle to find their position in the competitive market. Customer satisfaction refers to a person’s general attitude toward a service provider or an emotional reaction to a discrepancy between the expected service and the service received to fulfil the desired goals or needs. The first intent of the present investigation is to identify the factors contributing to Customer Satisfaction. The second intent is to evaluate the relationship between Customer Satisfa...

Journal of Consumer Marketing

There exists a vibrant literature dealing with one‐to‐one marketing and mass customization. The practice holds the promise of very satisfied customers, and profitable marketers who can create their own unassailable market positions. One of the building blocks of mass customization is knowledge of the customer and his/her complex set of preferences. There is a significant obstacle to gaining this information, namely the growing trend toward consumer privacy. Traditionally, businesses have collected massive amounts of information, hoping to identify responsive market segments. In the process, they have collected data on numerous individuals who would not realistically become customers. One solution to the problem is a strategic management approach involving an exchange of value between customers and marketers. Using a strategic approach, marketers can target the most attractive consumers, avoiding those whose lifetime value to the firm is low. By employing the relationship management ...

Journal of Consumer Behaviour

Teresa Heath

RELATED PAPERS

Gloria Enache

dfgdfgdf ghgfhfg

Élisabeth HENRY

BULLETIN OF UNIVERSITY OF AGRICULTURAL SCIENCES AND VETERINARY MEDICINE CLUJ NAPOCA. VETERINARY MEDICINE

Iuliana Gajaila

Food Security and Child Malnutrition

Revista Brasileira de Cirurgia Cardiovascular

Mauro Paes Leme de Sá

Atli Harðarson

tony taleb abd el salam

tony abo taleb , Tony Taleb , Hesham A . Abdel Kader

Revista Panamericana de Salud Pública

Antônio Frias

Journal of Geophysical Research: Earth Surface

Kory M Konsoer

Clinical Genetics

Shoaib Al Zadjali

dialnet.unirioja.es

Manuel Javier Martínez Carrillo

Developing Country Studies

Abdulmutallib A Abubakar

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society

juan camilo narvaez uribe

Journal of Perinatal Medicine

rafael ucieda

Research, Society and Development

Márcio Luiz dos Santos

Regional Anesthesia & Pain Medicine

Nana Boateng (PhD)

International Conference on Autonomic Computing, 2004. Proceedings.

Rob Barrett

Monthly Notices of the Royal Astronomical Society

Antonio Armando da Costa

fausy salomao

Jose Alejandro Tarazona Forero

Fisioterapia Brasil

A. Vinicius Soares

The Journal of Physical Chemistry B

Ahmad Sakti

Choice Reviews Online

David Eltis

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

To read this content please select one of the options below:

Please note you do not have access to teaching notes, direct marketing and relationships: an opinion piece.

Direct Marketing: An International Journal

ISSN : 1750-5933

Article publication date: 17 October 2008

The purpose of this paper is to discuss the complementary effect of relationship marketing and direct marketing and outline the foundations of direct marketing that can be enhanced by relationship marketing principles.

Design/methodology/approach

This is a personal viewpoint based on many years of working in, teaching and research of direct and relationship marketing.

The paper finds that both disciplines of direct marketing and relationship marketing have something of value to the other. The combination of the two strategies can only be of value and benefit to both customers and organisations.

Originality/value

The value of this paper is that it outlines the symbiotic strength of direct marketing and relationship marketing that allows contemporary marketers to utilise the best of both disciplines to establish and maintain strong relationships with their customers

• Direct marketing
• Relationship marketing

Harridge‐March, S. (2008), "Direct marketing and relationships: An opinion piece", Direct Marketing: An International Journal , Vol. 2 No. 4, pp. 192-198. https://doi.org/10.1108/17505930810931008

Emerald Group Publishing Limited

Copyright © 2008, Emerald Group Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

The Reach and Impact of Direct Marketing via Brand Websites of Moist Snuff

David s. timberlake.

Associate Professor, Program in Public Health, University of California, Irvine, CA.

Tim A. Bruckner

Program in Public Health, University of California, Irvine, CA.

Dmitriy Nikitin

Project Specialist, Program in Public Health, University of California, Irvine, CA.

Restricting tobacco marketing is a key element in the US Food and Drug Administration’s (FDA) public health framework for regulating tobacco. Given the dearth of empirical data on direct marketing, the objective of this study was to assess the reach and impact of promotions on sales through snuff websites.

Nine brands of snuff, representing more than 90% of market share, were monitored for content of coupons, sweepstakes, contests, and other promotions on their respective websites. Monthly sales data and website traffic for the 9 brands, corresponding to the 48-month period of January 2011 through December 2014, were obtained from proprietary sources. A time-series analysis, based on the autoregressive, integrated, moving average (ARIMA) method, was employed for testing the relationships among sales, website visits, and promotions.

Website traffic increased substantially during the promotion periods for most brands. Time-series analyses, however, revealed that promotion periods for 5 of 7 brands did not significantly correlate with monthly snuff sales.

Conclusions

The success in attracting tobacco consumers to website promotions demonstrates the marketing reach of snuff manufacturers. This form of direct marketing should be monitored by the FDA given evidence of adolescents’ exposure to cigarette brand websites.

The rise in consumption of moist snuff, illustrated by a 66% increase in sales between 2005 and 2011, 1 is the likely function of a change in product design, 2 targeted marketing, 3 and differential taxation of tobacco products. Many of these changes, which coincide with the decline in cigarette sales, reflect the tobacco industry’s efforts to market snuff to smokers and a broader demographic of consumers. It is tempting to attribute the growth of snuff sales to the traditional media channels, such as magazine advertising, because traditional channels are easy to monitor and are well documented in the literature. 4 – 6 The Conwood Company, for example, increased its expenditures for magazine advertising by tenfold from 1998 ($.4 million) to 2005 ($4.0 million). 7 This increase in advertising expenditures is one factor that may have led to the unprecedented growth in popularity of the brand Grizzly from 5 th in 2004 to 1 st in 2009. 8 Other factors such as low price, the 2006 acquisition of Conwood (manufacturing company of Grizzly) by R.J. Reynolds, and use of other media channels may have contributed to the increase in Grizzly’s market share.

Whereas the consumer magazine continues to be a prominent media channel for Grizzly, it is complemented by other forms of advertising. 9 Richardson et al reported that $1.5 million was spent on direct mail for the discount brand (27 mailings) from June 2012 through August 2012. According to the Federal Trade Commission, 10 smokeless tobacco advertising expenditures on direct mail exceeded the advertising expenditures on consumer magazines in the year 2011 ($7.5 million vs $4.8 million, respectively). Direct mail sent from the tobacco company to the tobacco consumer is one form of direct-to-consumer marketing, referred to as direct marketing. The $7.5 million spent on direct mail in 2011 underestimates total spending on direct marketing because the figure does not include expenditures on coupons, sweepstakes, entertainment events, brand loyalty programs, and other promotions. 11 The intent of direct marketing is to build relationships with customers and maintain brand loyalty through a variety of promotional events, disseminated through the mail and tobacco brand websites. 12

The various forms of direct marketing are increasingly being propagated through tobacco brand websites. This expansion is evidenced by the website marlboro.com, 13 which attracts over one million visitors each month. Visitors to the website provide product and brand preferences so that marketing can be customized to the individual consumer. Once a consumer enters identifying information on a brand website (usually name, birthdate, and address), the consumer is entered into a database and is subsequently mailed giveaways, coupons, and information about sweepstakes. Brock et al reported that upon registering for various tobacco websites, they received over 600 marketing pieces by mail in one year. 12

The dearth of empirical data on online tobacco marketing is a key research gap in the current era of tobacco regulation. 14 Ribisl noted the importance of surveying consumer exposure to online marketing and its potential impact on tobacco use. 14 Thus, the first objective of this study was to track changes in website coupons and other promotions on brand websites of moist snuff. The premise of the study is that consumers are drawn to the websites during sweepstakes that are advertised through direct mail and communicated via other sources (eg, social media). If the coupons appear frequently on the websites, then consumers may be enticed to download and redeem the coupons, subsequently leading to greater snuff sales. Without data on coupon redemption, we indirectly tested this hypothesis by examining the promotion periods of sweepstakes and contests with respect to website traffic and monthly snuff sales. The study is intended to assess the reach and impact of website promotions for the purpose of monitoring and restricting tobacco marketing. This objective is a key element in the US Food and Drug Administration’s (FDA) public health framework for regulating tobacco, 15 particularly in light of adolescents’ exposure to tobacco marketing on cigarette brand websites. 16

Website Selection and Content

A convenience sample of 9 brand websites was selected to account for most of the market share of smokeless tobacco in the United States (US) (92% in 2014). 17 The websites were skoal.com , mygrizzly.com , freshcope.com , timberwolfsnuff.com , longhornsnuff.com , redman.com , snus.tobaccopleasure.com , goredseal.com , and generalsnus.com . The 9 websites were selected to represent premium snuff brands (Skoal, Copenhagen), discount snuff brands (Grizzly, Timber Wolf, Longhorn, Red Man, Red Seal), and snus brands (Camel Snus, General Snus). These brand websites differ from corporate websites (eg, ussmokeless.com) because they engage in direct marketing and attempt to restrict website access to adult tobacco consumers. Five of the 9 websites state that a website visitor must be a tobacco user over the age of 20, whereas the remaining 4 websites state that a visitor must be a tobacco user over the age of 18. In an attempt to block entry to underage visitors, the brand websites utilize age verification systems that vary in disclosure of personal identifiable information. 18 All 9 websites require name, mailing address and date of birth, but only 2 websites (ie, mygrizzly.com and snus. tobaccopleasure.com) require the last 4 digits of an individual’s Social Security number.

A research staff member logged in and tracked the websites weekly from November 2013 through December 2014. The adult staff member gained access to the websites by creating an account based on his/her own personal information. The weekly tracking of the websites entailed the creation of archival copies of coupons, sweepstakes, and other promotions that were entered into a database. This process allowed the research team to document changes in the promotions that occurred throughout the observation period. Most of the promotions applied to all website visitors and were not customized to the individual consumer. Coupons, for example, indicated savings on a can/tin, tub or roll without restrictions on the type of tobacco leaf-cut (eg, fine-cut) or flavoring. Searches on the websites trinketsandtrash.org and online-sweepstakes.com were then conducted to document promotions (excluding coupons) from an earlier period, January 2011 through October, 2013. The starting date of January, 2011 was chosen on the basis of cost and availability of data on sales and Internet traffic. The website online-sweepstakes. com is considered one of the largest online listings of sweepstakes, 19 and includes expired sweepstakes dating back to 2002. TrinketsandTrash.org, a website supported by the School of Public Health at Rutgers University, has an engine for searching for marketing materials from the tobacco industry by tobacco brand, item date, keywords, etc. Searches from the online sources yielded descriptions, start dates, and expiration dates of the sweepstakes for each of the 9 brands. The searches completed data collection for a time-series analysis of promotions, Internet traffic, and snuff sales from January 2011 through December 2014.

Internet Traffic and Sales

Unique visitors to the 9 websites, aggregated monthly from 2011 through 2014, were estimated from the proprietary firm Compete, a Kantar Media Company. The estimates were derived from a multisource panel of more than 2 million Internet users composing a nationally representative sample of adult Internet users in the US. 20 The weighted estimate for unique visitors represents the number of adult Internet users who made one or more visits to a given snuff website within the month. Compete utilizes a sophisticated algorithm for integrating the data sources (ie, panel data, clickstream data), followed by normalization techniques for weighting and projecting metrics (eg, website visitation) from the panel to the population of adult Internet users in the US.

The sales data for this study were obtained from the Nielsen Company’s Convenience Track System. 17 This data source was chosen because snuff purchases, assessed from barcode readings and in-store audits, were obtained from a nationally representative sample of US convenience stores. These stores, which account for 93% of smokeless tobacco sales nationwide, 1 include independent stores, chain and non-chain stores. The sales data were reported as sale units aggregated every 4 weeks from 2011 through 2014. Because the sales data (N = 52 4-week periods) did not exactly correspond to the website traffic (N = 48 months), sales data from one 4-week period (late December to mid-to-late January) were excluded from each year. This adjustment yielded a total of 48 data points with a slight lag between website traffic and sales data for the earlier part of a given year. For example, website traffic for January 1 through January 31, 2011 preceded sales data for the 4-week period spanning January 22 through February 19, 2011.

Time-Series Analysis

We applied autoregressive, integrated, moving average (ARIMA) methods, recommended in the epidemiologic literature, 21 to test the relation among promotions (excluding coupons), website traffic, and sales. The 4 types of online promotions included in the time-series analysis were games, contests, sweepstakes, and giveaways. Coupons were excluded from the analysis because of their ever presence on the websites ( Table 1 ). Positive associations with monthly snuff sales (or website traffic) occur when the volume of sales (measured in cans of snuff) exceeds the volume expected from periods when promotions were not held. Snuff sales, however, may exhibit temporal patterns such as a rising or declining trend in sales, seasonality, or the tendency for low values to be followed similarly by low values. These patterns, collectively referred to as autocorrelation, complicate correlational tests because the expected value of snuff sales is not the mean of sales from earlier months.

Characteristics of Online Coupons for 9 Snuff Brands, Tracked Weekly from November 2013 through December 2014

Time-series methods, developed by Box and Jenkins, 22 address the issue of autocorrelation by empirically identifying and removing patterns in the dependent variable. The time-series routines include autocorrelation parameters via an ARIMA error term such that the residual sales exhibit no autocorrelation and have a monthly expected value of 0. The ARIMA approach uses autoregressive (AR) and moving average (MA) parameters to model the tendency to remain elevated, be depressed, or oscillate. AR parameters best describe patterns that persist for relatively long periods, whereas MA parameters parsimoniously describe less persistent patterns. The integrated (I) parameter serves as a “differencing” operator if a series exhibits a strong trend (ie, non-stationary mean).

The 2 panels in Figure 1 illustrate the ARIMA process by plotting the sales of Grizzly snuff before (top panel) and after (bottom panel) removal of autocorrelation. The top panel shows strong trend and seasonality in sales. By contrast, the bottom panel shows no such patterns; sales in each month are serially independent. We use this “residual” data as the dependent variable series when examining the relation between promotions (or web traffic) and sales volume.

Plots of Grizzly Raw Sales and Residual Sales across 48 Months Following the Detection and Removal of Autocorrelation

After applying time-series routines to each of the 9 dependent variables (ie, sales of the 9 brands), we inserted the promotions variable into the equation to determine whether it correlated positively with a concurrent rise in sales. The variable had a binary response denoting the presence or absence of any promotion, which most often was a contest or sweepstake. Contests and sweepstakes were treated the same in the analysis because there was no reason to believe that they differed regarding website traffic or snuff sales. Multiple promotions occurred concurrently for some brands, but were too infrequent to model on a continuum. Furthermore, unlike the coupons, the contests and sweepstakes occurred intermittently throughout the observation period, thereby facilitating analysis of the binary variable for most of the brands. For example, the brands Skoal and Grizzly held promotions for 13 and 24 months, respectively, of the 48 total months observed in the study. We also examined the relationship between the promotions and website traffic in a separate set of models. Furthermore, we repeated the analysis of snuff sales using website traffic as the independent variable to examine whether website activity, more generally, corresponded with an increase in snuff sales. For all analyses, we specified a concurrent relation (ie, lag of 0 months) due to the slight lag already induced by the exclusion of 4 data points (see section Internet traffic and sales). All analyses were conducted with software from Scientific Computing Associates (version 6.0; Oak Brook, IL).

Website Coupons

Eight of the 9 websites offered a coupon at the beginning of the observation period ( Table 1 ). A change in coupon represents the replacement of the coupon for either a recycled or new coupon (dollar value or coupon type). The change does not represent the removal of a coupon that was not replaced. Hence, a coupon appeared on the majority of snuff websites throughout the observation period. Premium brands like Skoal and Copenhagen infrequently changed the type or value of their online coupons. In contrast, websites for the discount brands like Longhorn, Red Man, and Timber Wolf frequently changed their coupons from week to week. However, Swedish Match, the company of the 3 discount brands, often recycled the coupons from prior weeks without having posted coupons with new values.

Some brands offered a straight discount coupon 12 such as a $1 off one can of Grizzly. Another type of coupon was the floor price which specified a certain dollar value for the product (eg, $1/can of General Snus). The third type of coupon, the buy-one-get-one-free coupon, was observed less frequently than the other coupon types. Some brands offered multiple coupons at a given time, such as 2 straight discount coupons that varied in value. The predominant marketing theme in the coupons was savings, exemplified by such phrases as “Bag a Buck or Two” and “Save some coin on your favorite can.”

Sweepstakes and Other Promotions

Te online promotions were a common form of direct marketing, as evidenced by 7 of 9 snuff brands that held at least one non-coupon promotion from 2011 through 2014; Red Seal and Longhorn were the exceptions. Emerging snuff brands like Camel Snus utilized this form of marketing more frequently than established brands like Copenhagen. The former and latter brands held promotions on 39 and 21 months, respectively, of the 48 months of observation. Most promotions lasted several months and sometimes overlapped for a given brand. They were often held in the summer, but did occur in the other seasons. As Table 2 illustrates, games and giveaways yielded nominal prizes and gifts (eg, bottle of hot sauce), in contrast to the substantial prizes awarded in contests and sweepstakes (eg, Timber Wolf’s $100,000 grand prize). The latter promotions highlighted themes or notable events, such as Skoal’s 80 Days of Saturdays celebrating the company’s 80 th birthday. Grizzly sponsored creative promotions centered on the theme of masculinity, exemplified by Writing the Man Rules Challenge Contest, which required contestants to write their “man rules” on each week’s theme. Like other promotions, this contest ran for multiple weeks with weekly grand prizes and instant-win prizes. Restrictions on online entry into the various promotions varied from one entry per day to one entry per promotion.

Examples of Online Promotions in the Form of Games, Contests, Sweepstakes, and Giveaways

A descriptive analysis of the marketing reach of the promotions revealed that the promotion dates coincided with the number of unique visitors to the snuff websites. This was evident for the 3 featured websites in Figure 2 , particularly Redman. com which experienced a substantial decline in Internet traffic following termination of its 2 promotions in 2011 and 2012. The dates for skoal.com and mygrizzly.com correspond to peak website traffic during the online promotions.

Temporal Relationship between Number of Unique Website Visitors and Promotion Dates (1/2011 – 12/2014)

Promotions, Website Traffic and Sales

For most products, time-series methods revealed seasonality in monthly website traffic as well as other effects (Appendices A through G). After removal of these patterns, website traffic increased substantially during months of active sweepstakes, contests or other promotions ( Table 3 ). This finding held for 5 of the 7 snuff brands that sponsored the online promotions. For instance, promotions for Skoal corresponded to an additional 88,695 “hits” above the level expected from the non-promotion periods, representing an increase of more than 2 standard deviations. By contrast, Camel Snus and General Snus exhibited no association between the promotions and website traffic.

Time-series Results over the 48 Time Points Examining Website Traffic and Snuff Sales as a Function of Sweepstakes, Contests and Other Promotions

Sales of several snuff brands also demonstrated a strong trend (eg, Skoal), thereby requiring us to render the trend mean-stationary. For the majority of snuff products (5 of 7), the association between promotions and sales was not statistically significant as demonstrated by the second column in Table 3 ; the exceptions were Red Man and Timber Wolf. The regression coefficients for Red Man (β = 46,301) and Timber Wolf (β = 44,072) indicate a substantial increase in snuff sales corresponding to a promotion period. As shown in the last column of Table 3 , website traffic in general was not associated with snuff sales.

These findings indicate that the brand websites are successful in attracting tobacco consumers during sweepstakes and other promotions. The success in reaching consumers was evident for the popular brands like Skoal and Grizzly, as well as the less popular brands like Red Man. The observation that coupons were a central feature of the websites provided rationale for testing associations among snuff sales, website traffic, and the promotions. The premise was that consumers would visit the brand websites during sweepstakes/contests, download the coupons, and redeem the coupons during promotion periods. The null association between promotions and snuff sales for 5 of 7 brands raises the question of whether direct marketing is primarily intended to enhance brand loyalty in the long term or boost sales in the short term. The latter is supported by the notion that sales promotions aim to stimulate behavior, as opposed to advertising which has a long-term effect from conditioning a consumer’s perception of a product. 23 Redmond reported that during the rapid expansion of sales promotions for cigarettes (1983–1992), expenditures for such promotions were highly correlated with the initiation of daily smoking among ninth graders. 23

The absence of a short-term impact of promotions on sales is not what we had expected. Tough, it is plausible that the substantial increase in website traffic during promotions could increase customer satisfaction and brand loyalty over time. 11 Without having measured these outcomes, it would be unfounded to reach such a conclusion. On the other hand, it would be premature to conclude that the promotions were a failure based on the null associations observed between promotions and sales. Unfortunately, any long-term effect of promotions would be undetected in the current study. It can be argued that momentary increases in sales may not occur because marketing tactics (ie, couponing) originate from the tobacco industry’s efforts to offset the decline in sales resulting from increases in cigarette taxes/prices. 24 Empirical evidence of the effect of direct marketing comes from a pre- and post-assessment of the 1998 Master Settlement Agreement (MSA). 25 Loomis et al reported that following the MSA, the proportion of promoted cigarette sales increased substantially and correlated with the imposition of state excise taxes. The overall decrease in cigarette consumption, resulting from the increase in cigarette prices, may have been partially offset by the rise in marketing expenditures that led up to the 1998 MSA. 26

Our assessment of the reach and impact of direct marketing was limited by a number of factors. First, the impact of promotions on sales, via the redemption of website coupons, could be assessed only indirectly through enumeration of website visits during the promotion periods.

The alternative of measuring coupon use directly through a proprietary marketing panel would have yielded few affirmative responses due to the low prevalence of snuff use. The second limitation was the lack of a metric for assessing brand loyalty and customer satisfaction. Without such a metric, we could only speculate that direct marketing through brand websites increases customer retention. The third limitation was the use of 2 online resources, online-sweepstakes.com and trinketsandtrash. org, for identifying sweepstakes and other promotions that predated our weekly tracking of brand websites. However, the chance of not identifying a major sweepstake was relatively small given the breadth of the 2 online resources. The fourth limitation was the inability to characterize the website visitors, such as demographics (eg, adolescent vs adult), history and extent of snuff use, and smoking status. The fifth limitation was the discrepancy in the reporting of sales data in 4-week periods versus the monthly reporting of website traffic. This discrepancy required us to exclude arbitrarily one 4-week period (ie, late December to mid-to-late January), which created a slight lag between website traffic and sales data for the earlier part of a given year. The sixth limitation was the potential for a type II error arising from analysis of fewer than 50 data points. 22 Increasing the sample size for greater statistical power was not feasible because sales and Internet traffic data were unattainable for the years prior to 2010, and exorbitantly expensive for the 12 months in 2010 (ie,12 data points). Lastly, the ARIMA models did not include variables that could have affected snuff sales, such as state excise taxes for cigarettes and snuff. Unlike us, Dave and Saffer were able to obtain and model state-level measures in estimating an elasticity of .06 for exposure to smokeless tobacco advertising. 6

Despite the limitations, we demonstrated that brand websites are a significant source of direct marketing for snuff manufacturers. Promotions such as sweepstakes and contests draw a large number of website visitors who are exposed to coupons and other forms of direct marketing that could strengthen consumers’ brand loyalty. Thus, it is important for tobacco control advocates to track the brand websites for identifying emerging practices in marketing that are being directed to snuff consumers.

IMPLICATIONS FOR TOBACCO REGULATION

Restricting tobacco marketing is one of 8 elements in the FDA’s public health framework for regulating tobacco. 15 Ashley et al note the importance of researching marketing via the emerging media channels, such as tobacco brand websites. 15 Our study demonstrates that consumers are drawn in large numbers to snuff websites during contests and sweepstakes. The 2009 Tobacco Control Act does not restrict the marketing content of adult-only tobacco websites in the same manner as other media channels. Yet, some data have challenged the notion that adult-only tobacco websites are impervious to adolescents who attempt to enter them, 16 suggesting that age-verification methods are fallible. Soneji et al report that 6% of 15-to-17-year-olds had visited a cigarette brand website, possibly through the creation of a bogus identity or the login information of an adult family member. 16 Either way, the finding indicates that adolescents are being exposed to online tobacco marketing. If the snuff websites are proven to be a threat to public health, notably adolescents, 15 then the websites could be regulated by the FDA as authorized by the 2009 Tobacco Control Act.

Acknowledgments

This work was supported by the National Institute of Drug Abuse and the Family Smoking Prevention and Tobacco Control Act (R03DA027950). We thank the reviewers for their helpful suggestions.

Full Time-Series Results, Including ARIMA Parameters, over the 48 Time Points Examining Website Traffic and Snuff Sales as a Function of Sweepstakes, Contests, and Other Promotions

Full Time-Series Results, Including ARIMA Parameters, over the 48 Time Points Examining Website Traffic and Snuff Sales as a Function Of Sweepstakes, Contests, and Other Promotions

Human Subjects Statement

This study did not entail the use of human subjects.

Conflict of Interest Statement

None declared.

Contributor Information

David S. Timberlake, Associate Professor, Program in Public Health, University of California, Irvine, CA.

Tim A. Bruckner, Associate Professor, Program in Public Health, University of California, Irvine, CA.

Vyvian Ngo, Program in Public Health, University of California, Irvine, CA.

Dmitriy Nikitin, Project Specialist, Program in Public Health, University of California, Irvine, CA.

Direct mail to prospects and email to current customers? Modeling and field-testing multichannel marketing

• Original Empirical Research
• Open access
• Published: 05 August 2023

Cite this article

You have full access to this open access article

• Albert Valenti   ORCID: orcid.org/0000-0003-2887-7712 1 ,
• Shuba Srinivasan 2 ,
• Gokhan Yildirim 3 &
• Koen Pauwels 4 , 5  

3067 Accesses

2 Altmetric

Explore all metrics

Multichannel retailers need to understand how to allocate marketing budgets to customer segments and online and offline sales channels. We propose an integrated methodological approach to assess how email and direct mail effectiveness vary by channel and customer value segment. We apply this approach to an international beauty retailer in six countries and to an apparel retailer in the United States. We estimate multi-equation hierarchical linear models and find that sales responsiveness to email and direct mail varies by customer value segment. Specifically, direct mail drives customer acquisition in the offline channel, while email drives sales for both online and offline channels for current customer segments. A randomized field experiment with the beauty retailer provides causal support for the findings. The proposed reallocation of marketing resources would yield a revenue lift of 13.5% for the beauty retailer and 9.3% for the apparel retailer, compared with the 6.5% actual increase in the field experiment.

Similar content being viewed by others

How is retargeting related to purchase incidence, channel choice, and purchase quantity?

Tanya Mark, Tirtha Dhar, … Katherine N. Lemon

May I have your attention, please? An investigation on opening effectiveness in e-mail marketing

Julián Chaparro-Peláez, Ángel Hernández-García & Ángel-José Lorente-Páramo

Positioning the Postal Service for the Next Generation: An Overview of Market Research

Avoid common mistakes on your manuscript.

Introduction

Multichannel retail is important for today’s marketers (Cui et al., 2021 ; Dekimpe, 2020 ), but it requires managers to allocate marketing budgets to channels and customer segments. Customer transactional data give retailers detailed information about existing customers’ purchase history, allowing them to prioritize customer segments by value. However, which segments are most responsive to which marketing action is unclear a priori (Zhang et al., 2014 ). For example, direct mail volume increased 46% between 2019 and 2022 (Gendusa, 2022 ), with growth in impressions (28%) outpacing digital ad impressions (10%) in 2021; yet direct mail is also very expensive and therefore often targeted to the highest-value segments (Sahni et al., 2017 ). Our survey of 351 marketing managers reveals that 46% believe that the most expensive marketing action should be targeted at the most valuable customers while 41% believe that all marketing should be sent to all customer types. Footnote 1 Consistent with these statements, most companies target emails to least valuable customer segments, including prospects, with its low cost as the primary reason (Levinson, 2019 ; Medlar, 2017 ), and target costly marketing actions to high-value customer segments. However, managers need to assess marketing response in multiple channels to understand which marketing actions produce the best returns and to make budgeting decisions on which actions to invest in and which customer segments to target.

Prior marketing research advises companies to allocate marketing actions to customer segments most receptive to them (Kamakura & Russell, 1989 ) and finds that customers’ past experience with the company’s offering does not necessarily mean greater responsiveness to marketing actions (Ascarza, 2018 ). First, some current customers may not peruse direct mail because they already know the firm and its offerings and prefer reminder emails. However, prospective customers (prospects hereinafter), for whom transactional data is not available, and light buyers might be more responsive to direct mail because of the rich information provided. How much these customers subsequently buy is an open question. Second, customers’ intrinsic preferences for email and direct mail may depend on the intensity of their interactions with the firm (i.e., email opening or direct mail browsing frequencies) differ among customers. Consistent with this argument, Return Path’s ( 2015 ) study suggests that email targeting should depend on customers’ engagement level. Third, recent legal developments such as the European Union’s General Data Protection Regulation (GDPR), are removing up to 75% of third-party data from analysis, Indeed, direct mail “has greatly benefited from GDPR because it does not require consent from recipients” (PostGrid, 2022 ) while the use of emails may be impacted, making it more crucial to identify and target marketing-responsive customers instead of maximizing impressions (e.g., by emailing all customers) (Morris, 2019 ; Snyder, 2018 ).

Our research objective is therefore to address an important marketing-mix resource allocation problem for multichannel retailers. That is, we propose an integrated methodological approach to allocate online and offline marketing actions (in our case, email and direct mail), given the different responses in channels and customer segments, including prospects and dormant customers (i.e., those who have not purchased for a long time). To this end, we develop a decision-support system based on a systematic empirical modeling approach. We begin by describing the value of customers using the recency–frequency–monetary value–clumpiness (RFMC) model, which allows us to classify customers into value segments (Zhang et al., 2014 ). We then estimate multi-equation hierarchical linear models (HLMs) to assess the online and offline sales responsiveness to email and direct mail and customer value segment levels. When the data are from multinational retailers, we perform a meta-analysis of the estimates across countries for comparison. We use HLMs for out-of-sample sales prediction and marketing resource reallocation. Finally, we perform a field experiment to obtain the predicted benefits of the reallocation in a causal setting.

Specifically, we perform three empirical analyses. The first involves an international beauty retailer with data on every purchase transaction and all marketing communications for a four-year period for almost 85,000 customers randomly sampled from its six main markets: United States, Great Britain, Germany, France, Spain, and Italy. The second analysis involves a US apparel retailer with transactional data, online and offline channels, and marketing actions. In the third analysis, to evaluate our model-based results and provide causal inference support, we design and implement a randomized field experiment for the beauty retailer with all its 120,000 customers in Italy.

From a substantive perspective, our research is grounded in marketing managers’ challenges in multichannel retail, addressed with the empirics-first approach advocated by Golder et al. ( 2023 ) instead of starting from theory often borrowed from the founding disciplines (Kohli & Haenlein, 2021 ). Our work combines “conceptualization, research design and research execution” with “deep socialization with practice” (Stremersch, 2021 , p. 13) and challenges the current wisdom held by many high-level decision makers (Kohli & Haenlein, 2021 ). Following Lehmann ( 2020 ), we adopt an integrative modeling approach that blends data and theory by combining prediction and explanation for two specific retailers in six developed countries. In terms of prescriptive implications, we assess potential revenue improvements (e.g., Lemmens & Gupta, 2020 ) following Mantrala et al. ( 1992 ), who argue that the biggest gains are to be realized not by optimizing the total budget, but by doing a reallocation of the current budget. Consistent with this argument, we find that a reallocation of the marketing budget over customer value groups yields substantial revenue improvement. While the specific results are particularly relevant to the marketing actions and companies studied, our findings may encourage scholarly thinking about how they generalize to other contexts (Stremersch et al., 2023 ).

Research on multichannel response to direct mail and email

Within the rich literature on multichannel marketing response, we focus our review on the two marketing actions, direct mail and emails, that capture offline and online marketing in our contexts and on how marketing responsiveness differs by customer segments. Table  1 shows our study’s contributions and positions it within related marketing literature.

Direct mail effectiveness

Despite the growth of online marketing, multichannel retailers rely on direct mail given its ease of processing by consumers, ability to generate greater brand recall, and higher response rates than digital marketing communication (e.g., email, paid search, online display, social media). Direct mail, which accounts for more than one-third of direct marketing expenditures in many countries (Direct Marketing Association, 2015 ), can arouse interest in a firm’s products and result in purchase through short-term rewards (Roberts & Berger, 1999 ; Rust & Verhoef, 2005 ).

Several studies have shown that direct mail significantly affects behavior (Hill et al., 2006 ; Verhoef, 2003 ) and adoption of a new technological product (Prins & Verhoef, 2007 ; Risselada et al., 2014 ). Naik and Peters ( 2009 ) provide empirical evidence that direct mail directly drives online visits to enable car configurations. Valentini et al. ( 2011 ) find that direct mail by a multichannel retailer can drive new customers’ choice of shopping in either online or offline channels (for a review of omnichannel retail, see Timoumi et al., 2022 ). In the context of direct mail for charity donations, Seenivasan et al. ( 2016 ) conduct a field experiment that varies the framing of the message and find that monthly framing of the donation, including a story of an in-group person, yields better outcomes. Verhoef et al. ( 2007 ) argue that direct mail has high ease of use, can result in channel lock-in, and exhibits cross-channel synergy between direct mail search and web purchase. Danaher and Dagger ( 2013 ) cite direct mail as an effective tool to reach unaware consumers. In their comparison of the relative effectiveness of multiple marketing tools, they identify direct mail as the second most effective tool when considering dollar sales as the focal outcome and the most effective when profit is the focal outcome. At the same time, contradictory evidence in the business-to-business sector suggests that direct mail is not effective in driving sales (Wiesel et al., 2011 ).

Direct mail response varies for customer groups and marketing interventions, which can be explained by customer characteristics and past purchase history (Rust & Verhoef, 2005 ). Research has found that marketing response can differ among customer groups depending on demographics or recency–frequency–monetary (RFM) value metrics (Wedel & Kamakura, 2002 ). For example, marketing actions such as promotions are more effective for prospects (Van Heerde & Bijmolt, 2005 ) but do little for acquired customers and could even have negative effects (Anderson & Simester, 2004 ). Rust and Verhoef ( 2005 ) find that loyal customers might have reached their full value in the service relationship in terms of the number of financial services purchased and might be less likely to purchase additional services, despite receiving direct mail with a call for action. Mark et al. ( 2019 ) develop a dynamic segmentation model of channel choice and purchase frequency to assess the responsiveness of segments to direct mail and email. They find that direct mail is an effective tool at influencing purchases in both offline and online channels. However, none of these studies consider how response to direct mail might vary for prospects versus acquired customers over time, in online and offline channels, and assessed with consumer transactional data in an econometrics analysis or a randomized field experiment (see Table 1 ).

Email effectiveness

Emails are effective in driving sales response for several reasons. First, they enable marketers to reach their customers at a low cost. Chittenden and Rettie ( 2003 ) report that the total cost per 5000 customers for email campaigns is $26,500 versus $69,600 for direct mail, so email costs about 38% of direct mail. Second, emails provide information that motivates customers to visit the physical store (Tezinde et al., 2002 ). Emails drive sales (Danaher & Dagger, 2013 ), average spending (Kumar et al., 2014 ), and customer retention (Drèze & Bonfrer, 2008 ). Third, emails may generate faster responses and create an opportunity for interactive communication with customers; customers can respond to an email the moment they receive it on their computer or mobile device.

As to cross-channel effects, emails make it more convenient for customers to use the online (vs. offline) channel because they can land on the firm’s web page by clicking on the email links. Ansari et al. ( 2008 ) find that emails have a positive effect on online sales but a negative effect on offline sales. Sahni et al. ( 2017 ) conduct a post hoc analysis of experiments and show the aggregate-level effects of emails on expenditure. Similarly, Zhang et al. ( 2017 ) capture the average effect of a customer’s response to emails on purchase.

Finally, several meta-analyses find that marketing effectiveness varies across countries and that country effects moderate the elasticity of advertising (Sethuraman et al., 2011 ) and promotions (Kremer et al., 2008 ). Importantly, this evidence comes mostly from a comparison between mature and emerging markets, whereas our data are from mature markets. In addition, as Table 1 shows, these studies do not consider cross-channel effects of marketing actions, except for a few single-country works (Pauwels & Neslin, 2015 ; Valentini et al., 2011 ).

Direct mail and email comparison in consumer segment response

How do direct mail and email compare in consumer responses? In surveys, 70% of Americans find direct mail more personal than email (Direct Marketing Association, 2020 ). Consumers view direct mail as more believable, formal, and important and email as quicker, more informal, and spontaneous (Niblock, 2017 ). While 56% of consumers note that direct mail makes them feel valued, only 40% indicate such about email (Niblock, 2017 ). When delving deeper into why this is so, consumers report that direct mail is tangible and real (Bozeman, 2019 ) and, “as a physical object, provides the space and time needed to appreciate what the company sends” (Medlar, 2017 ).

Regarding differences among consumer segments, direct mail’s trustworthiness and ability to evoke feelings of being valued might be more important for prospects than for current customers. Only 44% of consumers could recall the brand right after seeing a digital ad, while 75% could recall it after receiving direct mail (Niblock, 2017 ). Consumers prefer to receive direct mail for brochures and catalogs (63% vs 21%) and welcome packs (62% vs 23%) but prefer emails for news and updates (62% vs 17%) and confirmation or follow-up messages (57% vs 21%) (Niblock, 2017 ). While direct mail “appeals to … prospects in a very different way – a more emotional way” (Medlar, 2017 ), email is read while at work or relaxing at home and “doesn’t feel the same … as opening a piece of direct mail does” (Bozeman, 2019 ). Moreover, the physicality of direct mail versus email provides the space to communicate more creatively (Levinson, 2019 ), which might be more appealing for prospects who know less about the company offering.

Contributions

This research makes substantive and methodological contributions to the marketing literature on multichannel resource allocation (see Table 1 ). First, from a substantive standpoint, it tackles an important marketing mix resource allocation problem facing multichannel retailers—namely, how to allocate online and offline marketing actions given the different responses in channels and customer segments, including prospect and dormant customers. This research is the first to show that sending direct mail—the most expensive marketing action—to the highest-value customers results in lower performance. Our model-based results in several countries and across retailers, confirmed by a field experiment, show that retailers should allocate direct mail for customer acquisition. From a practice perspective, our decision-support system is embedded in a beauty retailer’s decision processes (Lilien, 2011 ).

Second, from a methodological perspective, we adopt an integrated approach to assess the effectiveness of email and direct mail, per channel and segment. Inspired by the iterative model-experiment decision-making procedure (Hanssens & Pauwels, 2016 ), we also assess our model-based findings in a field experiment. Fischer et al. ( 2011 ) similarly propose a decision model to guide marketing resource allocation in a business-to-business health care setting by determining near-optimal marketing budgets at the country–product–marketing activity level in an Excel-supported environment. Our approach differs from theirs in three ways. First, they do not obtain insights into direct mail and email effectiveness for customer segments, which are of academic and managerial interest. Second, their approach lacks an experimental field test, which is helpful for normative implications that prescriptively guide marketing resource allocation. Third, they analyze their budget allocation estimations under the assumption of the specific response function that best represents the data in their study. Instead, we use more flexible econometric estimation techniques.

Methodological approach

Modeling requirements.

Our research objectives impose several methodological requirements. First, the modeling approach should allow for customer heterogeneity. An important decision is whether customer heterogeneity should be captured at the individual or segment level. We refer to aggregate segment-level models for three reasons: (1) we compare current customers with prospects and dormants, for whom historical purchase data are not available; (2) our objective is to support strategic decision-making on marketing resource allocation, and therefore we follow the literature on such models, which are typically at the aggregate level (e.g., Fischer et al., 2011 ; Hanssens et al., 2014 ; Srinivasan et al., 2016 ); and (3) targeting-related privacy concerns loom large when using consumer-level data, and scholars in the RFM tradition have advocated for summarizing consumer purchase histories and using data-compressed variables for modeling (e.g., Zhang et al., 2014 ).

Second, when confronted with email and direct mail campaigns, customer segments may exhibit different purchase behavior because of differences in overall consumption levels (i.e., intercept heterogeneity) and variations in their responses to email and direct mail campaigns (i.e., slope heterogeneity). These sources of variation are referred to as unobserved heterogeneity (Jain et al., 1994 ). Thus, our model should be flexible in accommodating unobserved heterogeneity among customer segments.

Third, we require a model that involves online and offline channels simultaneously and allows for cross-channel correlation. This enables us to account for channel variation in marketing responsiveness of each customer segment and consider the dependence between online and offline channels. These requirements lead us to estimate a multi-equation HLM (Leckie & Charlton, 2013 ) with two levels, with time-series observations nested within customer segments. Finally, because consumer segments could differ by country, we estimate our model separately for each country.

Thus, we develop and implement a multimethod modeling approach plus a field experiment to address retailers’ marketing problem. Table  2 outlines this approach, which combines customer value segmentation and cluster analysis (descriptive), econometric analyses through multi-equation HLMs (predictive), reallocation of marketing resources (prescriptive), and a field experimental study (causal).

Empirical methodology

Quantify customer value.

We quantify customer value with the RFMC approach because it only requires customer purchase history and can be readily implemented by managers (Zhang et al., 2014 ). Footnote 2 The RFMC approach is an extension of the traditional RFM, which is widely used for customer valuation (Gupta et al., 2006 ), and adds the clumpiness metric. Clumpiness is the degree of nonconformity to identical spacing in purchasing, and its addition helps achieve improved customer valuation and predictive accuracy (Zhang et al., 2014 ). We operationalize clumpiness using the entropy measure. Footnote 3

Create customer segments

We create customer segments according to the standardized RFMC metrics in each country using k-means cluster analysis, an approach preferred for large data sets (James et al., 2013 ). Footnote 4 We use the Euclidean distance as the dissimilarity measure (Gordon, 1999 ). As a starting point for the clusters’ centroids, we use the quantiles of the standardized RFMC values because we want to obtain clusters that reflect a customer value continuum. For example, for a four-cluster solution, the starting points are the 20%–40%–60%–80% values of each standardized RFMC metric. In consultation with the beauty retailer, we opted for a static segmentation to ensure managerial tractability and ease of implementation, given the firm’s annual marketing budget allocation. Importantly, we consider two additional customer segments, prospects and dormants, for which RFMC values cannot be computed because data are not existent or not available because they have not purchased for a long time.

Evaluate responsiveness to emails and direct mail

We estimate multi-equation HLMs to assess online and offline sales responsiveness to emails and direct mail by customer value segment (Leckie & Charlton, 2013 ). Specifically, for each country, we use a two-level structure in which time-series observations are nested within customer value segments (Auer & Papies, 2020 ; Rabe-Hesketh & Skrondal, 2008 ; Raudenbush & Bryk, 2002 ). Similar to Steenkamp and Geyskens ( 2014 ), we develop our model formulation for each level to arrive at the equation we estimate.

We include variables that vary with time as predictors in the level 1 formulation. Equations ( 1 ) and ( 2 ) include all the time-varying predictors (subscripts t and i denote time index and customer value segment index, respectively). Because we deal with time-series data, we specify a k th –order autoregressive terms to account for the autocorrelations in the residuals. Footnote 5 Thus, for both offline and online equations, we formulate level 1 as follows:

Across time within a customer value segment

where the superscripts off and on indicate that the coefficient is for the offline and online equation, respectively, OFF_SALES and ON_SALES stand for offline and online sales, and EMAIL and DIRECT_MAIL stand for email and direct mail. Moreover, DISC is the discount variable that controls for the applied promotions, and HOLIDAY is a categorical variable that captures the effect of major holidays. Footnote 6 The error terms, \({\varepsilon}_{ti}^{off}\) and \({\varepsilon}_{ti}^{on}\) , follow a bivariate normal distribution with zero mean and time-invariant variance–covariance matrix, \(\Omega =\left[\begin{array}{cc}{\sigma}_{off}^2& {\sigma}_{off, on}\\ {}{\sigma}_{on, off}& {\sigma}_{on}^2\end{array}\right]\) . Thus, Ω is nondiagonal; that is, the errors of the two equations are correlated (Leckie & Charlton, 2013 ).

The level 1 equations suggest that the intercepts and slopes of the email and direct mail variables vary across customer value segments. Level 2 includes these varying parameters from level 1 as dependent variables:

Across customer value segments

Equation ( 3 ) shows that overall offline sales in segment i are a function of a baseline ( \({\alpha}_{00}^{off}\Big)\) and a segment-specific intercept ( \({\alpha}_{0i}^{off}\) ). Similarly, Eq. ( 4 ) shows that overall online sales in segment j are a function of a baseline ( \({\alpha}_{00}^{on}\Big)\) and a segment-specific intercept ( \({\alpha}_{0i}^{on}\) ). Equations ( 5 )–( 8 ) specify the slopes of the email and direct mail variables as fixed across time and varying across segments. For example, the slope for email in the offline sales equation is a function of the overall effect ( \({\delta}_{00}^{off}\) ) and a segment-specific effect ( \({\delta}_{0i}^{off}\) ). Footnote 7

We combine the two levels in a single formulation, as shown in Eqs. ( 9 ) and ( 10 ):

Model Estimation

We estimate the model simultaneously using maximum likelihood for each country, allowing the errors of the equations to be correlated (Leckie & Charlton, 2013 ). We focus on the combined significance of the parameter estimates across countries using the meta-analytical test of added Z’s (Rosenthal, 1984 ), because our main interest is in the overall effects of online and offline marketing actions (ter Braak et al., 2014 ). This test allows us to derive more generalizable insights because it combines evidence of the six countries in our data. The effect size of the parameters are the weighted mean response elasticity parameters across countries. The weight is the inverse of the estimate’s standard error, normalized to 1. Thus, weighted coefficients can be interpreted as a reliability-weighted mean, with estimates with higher reliability (lower standard error) obtaining a higher weight (ter Braak et al., 2014 ).

Endogeneity

Our explanatory variables may not be strictly exogenous. For example, managers may set email and direct mail levels according to certain customer responsiveness. This type of endogeneity can be overcome by using exclusion restrictions. In the “Robustness checks” section, we derive these exclusion restrictions and explain how we use the control function (CF) approach to account for this source of endogeneity exploiting multi-country data (Papies et al., 2017 ; Wooldridge, 2015 ). In addition, we conducted a field experiment that assesses the causal effects.

Obtain out-of-sample predictions

We compare the forecast accuracy of the proposed HLMs with several benchmarks. We re-estimate the model parameters holding out 15% of the estimation period to evaluate prediction accuracy. We use three benchmarks commonly used by managers: random walk (i.e., the value in the previous period), last value in the estimation period, and mean of the country’s customer segment in the estimation period. We also use two machine learning models as benchmarks: random forests and support vector machines (Hennig-Thurau et al., 2015 ; Zhang & Chang, 2021 ). We evaluate the forecast accuracy with two measures: mean absolute error (MAE) and mean absolute percentage error (MAPE).

Reallocate optimally emails and direct mail

We (re)allocate emails and direct mail using the HLM estimates that incorporate customer value segments as drivers of marketing effectiveness, under the constraint of management’s maximum total number of emails and direct mail to avoid consumer fatigue and backlash (see Web Appendix A instead for a reallocation setup under the constraint of a total monetary budget). In doing so, we obtain the online and offline contributions in terms of sales increase per unit of email and direct mail per customer segment in each country (Dinner et al., 2014 ). For a given customer segment, we define the constrained resource allocation decision as

where Π is a differentiable profit function, m is the contribution margin (%), OFF  _  SALES is offline sales, ON  _  SALES is online sales, c em is the unit cost of emails (€), and c dm is the unit cost of direct mail (€). We note that the feasible region is compact by Eq. ( 11 ), and therefore Π attains a local maximum on the feasible region according to the Weierstrass theorem (Sundaram, 1996 ). Thus, the solution of this problem is characterized by the Karush–Kuhn–Tucker (KKT) conditions, which we develop in detail in Web Appendix B . Since the beauty retailer had upper bounds for both email and direct mail campaigns, the net returns of email ( NR em ) become:

Assuming c em is constant, we define  NR em  =  FC em  −  c em , where FC em denotes the financial contribution of emails. Then, as the short-term elasticities of offline and online sales with respect to number of emails are \({\eta}_{off, em}=\frac{\partial OFF\_ SALES}{\partial EMAIL}\ \frac{EMAIL}{OFF\_ SALES}\) and \({\eta}_{on, em}=\frac{\partial ON\_ SALES}{\partial EMAIL}\ \frac{EMAIL}{ON\_ SALES}\) , we can define FC em  as

Similarly, the financial contribution of direct mail is

International beauty retailer data

We obtained data from L’Occitane en Provence, an international natural and organic ingredient-based beauty and wellness products retailer. Its product portfolio includes skin care, hair care, fragrance, and body and bath offerings, and stores exclusively sell their own products. In addition to the brick-and-mortar stores, the company sells online through an e-commerce website for each country. These websites do not differ across countries, beyond the different languages.

The purchase transaction data, which cover four years between 2011 and 2014, include both online and offline transactions and discounts at purchase at the individual customer level for 84,110 customers. We randomly sampled customers from the firm’s six main countries: United States, Great Britain, Germany, France, Spain, and Italy. The data comprise prospect, dormant, and active customers.

The marketing communication data, which cover the years 2013 and 2014, contain all the online and offline communications from the retailer. The only online communication the retailer uses is email, and the data include whether and when the email was received, opened, and clicked. The only offline communication is direct mail, and the data include the start and end dates of the direct mail campaigns. According to the retailer’s management, the content is typically the same for both marketing actions; we employed two independent coders to confirm that this is the case for a sample of 385 emails and direct mail pieces from the United States and Great Britain (both were in English, the native language of the coders).

Beyond emails and direct mail, the company offers discounts, which we treat as a control variable in our model (Srinivasan et al., 2010 ). During the analysis period, the prices were the same in both the online and offline channels in each country. The firm has email and postal addresses for 42% and 65% of its customers, respectively. Footnote 8 In addition, the firm has the contact information of multiple prospects, who have shown interest in the brand at the point of sale or website but have not yet purchased from the firm.

Data operationalization

For the operationalization, we specify emails as an email sent, instead of “opened,” because emails sent represent a firm marketing decision. We operationalize the direct mail variable as 1 divided by the length of the campaign for each week of the campaign, because we do not know the exact day customers received the direct mail and thus must assume a constant impact throughout the campaign. We measure discount as the value amount of the discount (Wiesel et al., 2011 ). Finally, we test for seasonality by considering all periods, as in Srinivasan et al. ( 2004 ), but we find that seasonality occurs only for the Christmas period. We therefore create a dummy variable that takes the value of 1 between weeks 47 and 52 around the Christmas holiday. Table  3 provides descriptive statistics of the variables by country.

We aggregate the data at the weekly level to obtain a panel of customer transactions and marketing actions. We used 96 weeks of data for the calibration period to compute RFMC metrics and to create customer value segments. For the estimation of the HLMs, we used between 51 and 60 weeks, depending on data availability per country. These HLMs use log-transformed data to reduce skewness in the variables, to facilitate interpretation of the coefficients directly as elasticities, and to make comparisons among marketing actions, segments, and countries feasible; the estimated elasticities are the basis of the recommended effective marketing resource (re)allocation.

US apparel retailer data

We obtained data for a second retailer on all purchases and marketing communications for 23,891 randomly selected customers in the United States from 2010 to 2012. The retailer’s products, apparel and accessories targeted at women, are sold exclusively through company-owned brick-and-mortar stores or through the retailer’s own website. Similar to the beauty retailer, the retailer’s only online (offline) communication channel is email (direct mail). Moreover, this retailer has a different marketing approach than the beauty retailer; it allocates a larger proportion of emails to the medium- and high-value segments, while direct mail allocation is proportional to the size of the segments. Web Appendix C provides descriptive statistics.

Model-free evidence

We first explore the relationship between both direct mail and email and sales. We do so without imposing any structure in the data by examining the correlations at the individual customer level for the three predefined customer groups: prospects, dormants, and current customers.

As shown on Table  4 , the correlations between direct mail and sales are larger for prospects than for both dormants and current customers in four of the six countries (US, Great Britain, Germany, and Spain), while the correlation for prospects is of similar magnitude as for current customers in two countries (France and Italy). In contrast, for emails, the correlations with sales are mostly negative across these three customer groups and six countries and without a clear pattern of correlation magnitude. These results suggest that direct mail might be more effective for prospects.

Econometric analysis results

We begin with the results of the econometric analysis of the historical transaction data for both retailers. Then, we present several robustness checks where we: (1) specify a three-level cross-random-effects (CRE) model to evaluate sales variation drivers, (2) estimate a three-level HLM that combines all six countries, (3) estimate the HLM model with a Bayesian approach, and (4) assess endogeneity with the CF approach. We describe the field experiment design and results in the subsequent section.

International beauty retailer results

Customer value segments.

We compute customer value in terms of the RFMC metrics for each customer. With these metrics, we then create customer value segments using cluster analysis. Footnote 9 From the comparison of different cluster solutions (see Web Appendix D ), we obtain seven segments in the United States and Italy and six segments in Great Britain, Germany, France, and Spain. In each country, two segments consist of prospects and dormants, that is, customers who have never purchased from the retailer and customers who did not purchase during the two-year calibration period but have purchased before from the retailer. We label the other segments (i.e., consumers who made purchases during the calibration period) as nonrecent low value, recent low value, medium value, high value, and very high value. Table  5 reports the results of the cluster analysis. The table shows the breakdown of the customer value segments by country and the means and standard deviations of the RFMC metrics.

Prospects and dormants in combination represent at least half the customer base in all countries. However, there are notable country-specific differences: the United States has a larger proportion of prospects (38%) and a lower proportion of dormants (26%), while Great Britain has a larger proportion of dormants (40%) and a lower proportion of prospects (10%). The two low-value segments have similar levels of frequency, monetary value, and clumpiness but differ on the recency dimension. The recent low-value segment (nonrecent low-value segment) purchased, on average, eight weeks (one and half years) before the end of the calibration period. The United States (13%) and France (14%) have a lower proportion of these two segments. The medium-value segment mirrors the population average for the four metrics, while the high-value segments have large values of both frequency and monetary value. All countries are fairly similar in terms of the proportion of medium- and high-value customers, ranging from 25% to 30%, except the United States, which has a slightly lower representation of these customers (22%).

Effectiveness of direct mail and email

We estimate the HLMs with maximum likelihood estimation. All variables are stationary according to the augmented Dickey–Fuller and Levin–Lin–Chu panel unit-root tests (see Web Appendix E ). We check for homoskedasticity of the residuals (see Web Appendix F ). To determine the number of autoregressive terms, we test for residual autocorrelation, adding lags until the autocorrelation has been purged from the residuals; this resulted in two lags for the autoregressive terms. Our empirical findings suggest that random-effects components are not statistically significant in any of the six countries, and therefore a fixed-coefficients specification should be employed. Thus, in our models, we capture segment-level customer heterogeneity through the fixed-coefficients specification.

Table  6 presents the main results on the offline and online sales elasticities of email and direct mail for value segments consolidated across countries (ter Braak et al., 2014 ). Direct mail has positive and significant offline sales effects for prospects (.164, Z = 3.940, p  < .05). The magnitude of the estimated direct mail elasticity is in line with expectations from previous research: Danaher and Dagger ( 2013 ) report .104 as an average direct mail elasticity. and Danaher et al. ( 2020 ) find catalog elasticities of .02 (online) to .03 (in-store). Email, by contrast, has positive and significant online sales effects for medium- and high-value segments (.432, Z = 1.793 and .478, Z = 1.764, both p  < .1). We present the HLM estimation results for each country in Web Appendices H and I and the long-term elasticities in Web Appendix J .

In summary, we find important differences in the effectiveness of email and direct mail for channels and value segments. First, email has sales effects on medium- and high-value segments, while direct mail works only for prospects. Second, email has online sales effects, while direct mail has offline sales effects.

Out-of-sample forecasts

We compare the conditional forecast results for the last 15% of observations, for which the brand’s marketing-mix decisions are known. We obtain the forecasts from three traditional benchmarks (i.e., mean of customer value segment per country in the estimation period, the last period value in the estimation period, and a random walk) and from two machine learning models (i.e., random forest and support vector machines). As Table  7 shows, the best forecast accuracy comes from the HLM, given that it exploits the cross-sectional, time-series, and hierarchical structure of the data. Footnote 10

US apparel retailer results

For the second retailer, we compute the RFMC metrics for each individual customer at the weekly level for a calibration period of one year. We then segment the customer base according to the RFMC metrics into six segments to facilitate comparisons with the beauty retailer analysis. Table  8 shows that the proportion of customers in each segment is prospects (14%), dormants (7%), nonrecent low value (26%), recent low value (16%), medium value (34%), and high value (3%). We then evaluate the responsiveness to emails and direct mail in the estimation period consisting of 52 weeks.

Our results shown in Table  9 confirm the findings of the main analysis that own- and cross-channel effects of emails and direct mail vary by customer value segment. Specifically, direct mail has both offline and online effects for dormants (.02, p  < .05; .05 p  < 0.05, respectively), while email only has offline effects for both prospects and dormants (.12, p  < .01; .14 p  < 0.01, respectively). Notably, email shows only offline effects, and direct mail shows both offline and online effects.

Robustness checks

We test whether the results are robust to capturing country heterogeneity in a single main model, instead of having a separate model per each country. For the beauty retailer, first, we estimate three-level CRE models to evaluate the extent to which sales variation is explained by each possible level: time, customer value segment, and country. Second, we estimate a three-level HLM to incorporate country as a third level. We also present the robustness of our results to a Bayesian estimation and a CF approach.

Assessment of sales variation drivers

Similar to Hanssens et al. ( 2014 ), we estimate CRE models to examine the sales variation drivers. The CRE models show that for offline sales, customer value accounts for 90% of the explained variance, and country effects and time effects account for 8% and 2%, respectively. However, we find important differences for online sales—country effects explain as much as 50% of the explained variance, while customer value and time effects account for 42% and 8%, respectively. Thus, both country effects and customer value are essential to understand online sales variation, while customer value explains the majority of offline sales variation. All in all, the CRE results provide further empirical support for using multichannel marketing for customer value segments.

Analysis of countries jointly

Our model is flexible to allow resource allocation for customer segments at the global corporate level. That is, instead of six two-level HLMs, we estimate a three-level model, in which we constraint the number of segments to be the same in each country. This approach may be preferred by multinational retailers whose decisions for within-country allocations of expenditures between emails and direct mail are centralized. The number of optimal segments per country is six. The results are similar to the main results in both signs and significance. Specifically, the main finding that the most expensive marketing action, direct mail, is effective in driving customer acquisition of prospects in the offline channel holds (see Web Appendices K and L for details).

Bayesian estimation

To confirm that the results are not driven by the estimation procedure, we estimate the HLMs with a Bayesian approach for the main models and an alternative model with random intercepts, instead of fixed intercepts as in the main model. The results are similar to the main estimations in both signs and significance (see Web Appendix M ).

CF approach

The marketing communication variables might be correlated with the error term. Such endogeneity can be overcome using exclusion restrictions. We explore the possible estimate bias with a CF approach (Papies et al., 2017 ; Wooldridge, 2015 ), which is equivalent to the two-stage least squares approach for linear models but uses fitted values of the first stage as additional regressors in the second stage. To construct instruments for each country, we use the level of marketing in the other countries (Kuebler et al., 2018 ). The assumption is that country managers do not consider the sales levels of other countries when determining the marketing actions for a focal country (exclusion restriction). That is, managers set marketing actions levels expecting a response on the consumers they impact, i.e., customers in their country of responsibility and not in other countries. At the same time, managers follow similar strategies per segment across countries, and therefore marketing actions in the same segment may be correlated across countries (relevance condition). Indeed, the correlations between the instruments and the endogenous variables fall in the ranges of .88 and .94 for direct mail and .72 and .88 for email, supporting our assumption on the relevance condition.

The CF analysis largely confirms the main analysis results. The CF estimates coincide in terms of direction and significance with those of the main analysis, except for the effect of email on online sales for high-value customers in the United States (see Web Appendix N ). The instruments for the offline sales model are not significant (−.010, p  > .1 and .064, p  > .05, for direct mail and email, respectively), suggesting that the estimates of the offline sales model in the main analysis are not biased. However, the instruments for the online sales model are positive and significant (.112, p  < .05 and .161, p  < .05, for direct mail and email, respectively). When we account for this positive bias, the online equation results in a nonsignificant effect of emails for high-value customers in the United States. Moreover, the magnitude of the effect of email on online sales for high value customers in Italy and France is reduced but remains significant. All other results remain the same.

• Field experiment

Field experiment design

The main goal of the field experiment is to test the model-based findings on the differential effects of emails and direct mail by customer value segment in a controlled causal setting. We designed and implemented the experiment together with the marketing team of the beauty retailer between July and November 2017 in Italy. The four experimental cells are (1) control (no marketing), (2) only emails, (3) only direct mail, and (4) both emails and direct mail. To ensure a balanced proportion in each cell, we stratified each cell in the six customer value segments. To create the six segments, we obtained individual-customer purchase data spanning two years before the experiment. The field experiment took into account customers’ expressed preferences not to be contacted by certain channels and therefore was run on a sample of customers contactable by both channels, to avoid self-selection, to compare email and direct mail responsiveness in online and offline channels. Although a pure random assignment should result in each segment being equally represented in the four experimental cells in theory, proportionate stratification ensures that all segments are equally represented in each cell in practice (Duflo et al., 2007 ). This stratification is especially important because the total amount of direct mail was constrained for budgetary reasons to 33,000 pieces, and we wanted to ensure that high-value customers, who are a small fraction of the overall population, are proportionally represented in cells 3 and 4. The total sample consists of 122,394 customers (Table  10 ).

To evaluate the differential effects of the treatment groups, we specify a random-effects regression for customers in the prospect and dormant segments, because the treatment is exogenous (Chintagunta et al., 1991 ). For customers in the other four value segments, we specify a difference-in-differences regression, because the treatment is exogenous and customers in these segments purchased within the two-year period before the experiment. We run a separate regression per each segment, in which customer sales ( SALES ) vary per customer (index i ) and week (index t ). Equation ( 14 ) presents the random-effects regression and considers only the campaign period because prospects and dormants did not purchase before the experiment. Equation ( 15 ) presents the difference-in-differences regression and considers the campaign period and the two years prior.

where α i represents the customer-level intercept; CELL2 , CELL3 , and CELL4 capture whether the customer belongs to cells 2, 3, and 4, respectively; 𝛾 t represents time fixed effects; CAMPAIGN is a dummy variable that takes the value of 1 if the period belongs to the campaign and 0 otherwise; and ε it is the residual error. The coefficients of interest are β 2 , β 3 , and β 4 for Eq. ( 14 ) and β 5 , β 6 , and β 7 for Eq. ( 15 ).

Field experiment results

Figure  1 shows the results of the field experiment on the differential effectiveness of email and direct mail for different consumer value segments. First, we confirm that direct mail is only effective for prospects, with an elasticity of .132 ( p  < .05), compared with the .164 estimate in the main analysis. Second, email is not effective for any of the segments, while it was significantly effective for medium- and high-value segments in the main analysis. Third, direct mail and email in combination (interaction effects) are effective for the medium-value segment (.011, p  < .05), while the two marketing actions did not interact significantly in the main analysis; this effect, though significant, is small.

Email and direct mail effectiveness from field experiment for beauty retailer. Notes: Confidence level of error bars at p < .05.

Managerial implications

We calculate revenue lifts from (1) the econometric analysis of the beauty retailer data, (2) the econometric analysis of the apparel retailer, and (3) the field experiment. To calculate the financial contribution of emails and direct mail (Eqs. ( 12 ) and ( 13 )), we take the elasticity estimates from the empirical models and the mean levels of sales, emails, and direct mail per customer segment from the data. According to the beauty retailer’s annual report (L’Occitane, 2015 ), the cost of goods sold is 18%, and therefore we infer that the profit margin is 82%. Keeping the total number of emails and direct mail constrained in each country and holding the budget constant, Footnote 11 we assess how much the reallocation of marketing resources would improve the financial contribution.

Figure  2 compares the current allocation of marketing resources with the proposed reallocation and reports the sizes of the customer value segments (see Web Appendix O for details by country). For emails, the current allocation is proportional to the size of the customer value segments (i.e., “bigger gets more”; Corstjens & Merrihue, 2003 , p. 118); our reallocation proposes to reduce emails for prospects, dormants, and recent low-value segment and to increase them for nonrecent low-, medium-, and high-value segments, based on their response elasticities and segment sizes. For direct mail, the current allocation disproportionally considers the medium- and high-value segments and disregards prospects (i.e., the most expensive action for the most valuable customers); our reallocation suggests shifting direct mail to prospects. We evaluate the incremental revenue from the proposed reallocation by multiplying the financial contribution of the segment by the difference between the model-based proposed number of emails and direct mail and the actual number sent by the retailer based on the HLM.

Effective reallocation of emails and direct mail for beauty retailer

Our reallocation of marketing actions would yield a sales lift of €340,000, 33% due to better allocation of emails and 67% due to better allocation of direct mail, which represents a 13.5% total revenue increase. Given the beauty retailer’s size, the global implementation of the proposed reallocations would amount to hundreds of millions of euros in incremental revenues. Footnote 12 For the apparel retailer, the effective reallocation of marketing actions would yield a sales lift of $26,000, 84% due to better allocation of emails and 16% due to better allocation of direct mail, which represents a 9.3% revenue increase.

Finally, we quantify the revenue lift potential with a marketing resource allocation that considers the field experiment estimates. We compute the revenue lift with respect to the status quo of the typical marketing allocation used by the retailer, as this condition is not present in our experimental cells ( CELL1 in the experiment receives no marketing, which is not business-as-usual). Collectively, the marketing resource reallocation from the field experiment findings lifts revenue by 6.5% with respect to business-as-usual, holding marketing costs constant. The business-as-usual allocation has a revenue impact lift of 1.6% with respect to the control group of no marketing actions. Thus, we expect that a chainwide implementation of these recommendations will result in a lift of between 6.5% (from the field experiment) and 13.5% (using HLMs) in revenue for the beauty retailer.

Communication of these insights to the beauty retailer helped management adopt data-driven analytical tools and blend quantitative approaches with managerial intuition (Roberts, 2000 ). As one member of the marketing team noted: “The different effectiveness of direct mail and email depending on the customer type was surprising to us. Rethinking about this finding, we have a deep and increasing interest in investing in direct mail activities for customer acquisition and inactive customers.” The model-based recommendations helped the retailer embrace scientific decision-support systems and provided an opportunity to use marketing analytical dashboards with hands-on practice. In the words of Delphine Fournier, customer relationship management manager of L’Occitane: “The combination of marketing science tools with experimentation gives us a new perspective in understanding marketing effectiveness and helps us improve our resource allocation tremendously” (ISMS Practice Prize, 2018 ). L’Occitane has since implemented this model-based decision-making procedure consisting of iterative, model experiment, phases (Hanssens & Pauwels, 2016 ), and embedded marketing science models into its decision processes (Kumar & Petersen, 2005 ; Lilien, 2011 ).

Conclusions

Understanding online and offline sales responsiveness to email and direct mail for multichannel retailers is essential for academics and practitioners. Accordingly, we propose a systematic approach to quantify how email and direct mail influence online and offline sales for different customer value segments across countries. We conduct an empirical analysis using data from a beauty retailer with 84,110 customers from six countries and run a field experiment with 122,000 customers in one country for the retailer. We replicate the econometric analysis for an apparel retailer. In addition, we conduct several robustness checks to assess the validity of our findings.

This research provides four key insights. First, direct mail drives customer acquisition in the offline channel, while email drives both online and offline sales across different customer segments. Second, the model performs considerably better than benchmarks (up to 50%) in forecasting sales for channels and countries. Third, a reallocation of the marketing budget for customer value groups shows substantial revenue improvement of 13.5% for the HLM-based analysis and a revenue lift of 6.5% in the field experiment. Our model can be readily applied to other settings, as indicated by the 9.3% calculated revenue improvement for the apparel retailer. Moreover, the results of the field experiment in one country provide causal support for our empirical model findings.

Our findings challenge common wisdom, though they are consistent with surveys on different consumer experiences with direct mail versus emails. Receiving an expensive direct mail is more likely than an email to attract the attention of customers who have never purchased or stopped purchasing a while ago (dormants). This interpretation fits the broader consumer behavior theory that affective reactions are critical (e.g., Hoch & Loewenstein, 1991 ; Shiv & Fedorikhin, 1999 ); we would also add that emotional appeals are especially important to attract the attention of and gain new customers, while current customers do not need them to the same degree.

We offer several important insights for retail managers operating in a multichannel context. To allocate marketing resources effectively, managers should pay close attention to the different responsiveness of customer value segments to emails and direct mail. Both customer value and country effects are relevant to understand the online sales variation, even among the similar Western countries we analyzed. Our methodology can help retailers forecast future sales and optimally allocate marketing resources. Several of our insights may inspire companies to reassess how they run their email and direct mail campaigns. First, a customer’s “high-value” status with the company does not mean greater responsiveness to marketing actions. In our analysis, we find that such customers are less responsive to the (very expensive) direct mail. Second, as newly penetrated countries typically have a higher share of prospective customers and light buyers, direct mail resources might best be allocated to such countries. Finally, customer privacy issues have become even more important with recent legal developments, such as the GDPR, raising the stakes for companies to identify and target responsive customers.

This research has limitations that suggest directions for future research. First, we did not examine the order of emails and direct mail; thus, future research could test the ideal sequencing of email and direct mail, as “email makes for the perfect follow up to a direct mail campaign” (Bozeman, 2019 ) and companies should “create a lasting first impression with direct mail [and] reinforce it with email marketing” (Niblock, 2017 ). Future research could also explore a continuous (discrete) time dynamic optimization model through which Hamiltonian (Bellman) equations would be specified. Second, our data do not include competitors’ marketing actions. However, for both retail data sets used, the own-brand products are sold exclusively by the companies in question, rendering competition only indirect. Furthermore, future research could quantify marketing’s power to build long-term brand equity or to upgrade customers to higher-value segments. Our methodology can also be applied beyond the studied developed Western markets (e.g., developing countries), the analyzed product categories, and the studied channels (e.g., mobile) or marketing actions (e.g., phone calls, text notifications). In this study, we propose and implement a generalizable methodology for marketing resource allocation, which can be applied by any multichannel (multinational) retailer, whether they sell products or services, and can accommodate any number of countries, sales and communication channels. Finally, we call for future research to examine other regions to determine whether the findings generalize to non-Western countries.

Survey with N = 351, average experience = 3.8 years. The remainder (13%) agreed with the statement, “The most expensive communication should be sent to the least valuable customers.”

Other data (e.g., demographics, preferences, needs, attitudes) were not available for our partner companies.

Zhang et al. ( 2014 ) propose four measures (entropy, second moment, log utility, and sum of three largest components) and show that entropy is the most robust with the best performance.

Marketers have a long history of working with both a priori segmentation and latent response segments (e.g., Kamakura & Russell, 1989 ). The latter requires observing marketing response and leaves explaining the observed response differences to other analyses (e.g., comparing a priori customer characteristics to make the latent segments addressable). By contrast, a priori segmentation uses variables the company can observe (e.g., demographics) and then shows how marketing responses differ between these segments. A priori segmentation has evolved from demographics to customer purchase histories such as RFMC, which drive marketing response and are actionable for the company (e.g., Zhang et al., 2014 ). The evolving convention in the RFMC literature and our discussion with managers led us to choose this segmentation.

We estimated a model with heterogenous autoregressive coefficients across segments to assess whether they varied by segment. The likelihood ratio test results suggested the homogenous autoregressive coefficients across segments for all countries, except France. For France, we based our decision on the information criteria (AIC and BIC) result (see Web Appendix G), which favored the homogenous autoregressive coefficients across segments.

We estimated a model with interaction terms between the marketing actions to test whether they showed synergistic effects. Since we did not find significant synergistic effects, we choose to keep a more parsimonious specification in the model specification.

For the segment-specific intercepts and slopes, we use the fixed-effects formulation. An alternative approach is to use a random-effects specification that treats parameters as realizations of random variables following a probability distribution. To determine which specification to follow, we estimate the model with random-effects and test the significance of the random components. Our results favor the use of fixed-effects specification. We discuss this finding in the results section. This choice is also consistent with the recommendation that the fixed-effects approach should be used when the data have a small number of groups (i.e., fewer than 10) (Snijders & Bosker, 2011 ; Steenkamp & Geyskens, 2014 ).

We randomly selected the data for the econometric analysis in each country from the full customer base. Therefore, some sampled customers might not be contactable. The field experiment addresses the potential self-selection issue: we only include in the experiment the 120,000-plus customers who are contactable by email and direct mail to assess their responsiveness to both channels.

To select the number of cluster solutions, we take into account model requirement constraints, statistical criteria, and managerial considerations. We examine the reduction of variance in the RFMC metrics explained by the different number of clusters in each country. To this end, we use the comparison criteria of within-sum-of-squares, proportional reduction variance (eta coefficient), and proportional reduction error (Makles, 2012 ).

We focus on over-time forecasting validation because the HLM exploits the time-series structure as well as the cross-sectional and hierarchical structure of the data. However, we also perform a k-fold cross-validation. The fivefold validation uses 80% of the customers in a segment to predict the other 20%, rotating this approach through the full sample five times. The results based on the fivefold specification indicate an MAE and a MAPE of .732 and .265 for the offline sales equation, respectively, and .670 and .606 for the online sales equation, respectively.

The reason companies do not totally skew toward emails is twofold: (1) the optimal allocation depends on the ratio of elasticities (e.g., Dorfman & Steiner, 1954 ; Wright, 2009 ), and (2) companies want to avoid losing consumer goodwill by exceeding an unknown annoyance level of emails. We worked with the client, which set a maximum of three emails per week (seven in the United States). If we had not worked with a limit, some segments would have received two emails a day (i.e., 14 in one week), which seemed excessive to the managers.

Following Fischer et al. ( 2011 ), we also evaluate the reallocation considering the growth potential per segment and country applying a segment size multiplier. We obtain the multiplier from the growth observed in each segment. The reallocation results that consider this growth remain practically the same.

Anderson, E. T., & Simester, D. I. (2004). Long-run effects of promotion depth on new versus established customers: Three field studies. Marketing Science, 23 (1), 4–20.

Article   Google Scholar  

Ansari, A., Mela, C. F., & Neslin, S. A. (2008). Customer channel migration. Journal of Marketing Research, 45 (1), 60–76.

Ascarza, E. (2018). Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research, 55 (1), 80–98.

Auer, J., & Papies, D. (2020). Cross-price elasticities and their determinants: A meta-analysis and new empirical generalizations. Journal of the Academy of Marketing Science, 48 (3), 584–605.

Bozeman, R. (2019). Direct mail vs email – Optimize both through the customer journey. Retrieved April 6, 2021 from https://www.postalytics.com/blog/direct-mail-vs-email/ .

Chintagunta, P. K., Jain, C. D., & Vilcassim, N. J. (1991). Investigating heterogeneity in brand preferences in logit models for panel data. Journal of Marketing Research, 28 (4), 417–428.

Chittenden, L., & Rettie, R. (2003). An evaluation of e-mail marketing and factors affecting response. Journal of Targeting, Measurement and Analysis for Marketing, 11 (3), 203–217.

Corstjens, M., & Merrihue, J. (2003). Optimal marketing. Harvard Business Review, 81 (10), 114–121.

Google Scholar  

Cui, T. H., Ghose, A., Halaburda, H., Iyengar, R., Pauwels, K., Sriram, S., Tucker, C., & Venkataraman, S. (2021). Informational challenges in omnichannel marketing: Remedies and future research. Journal of Marketing, 85 (1), 103–120.

Danaher, P. J., & Dagger, T. S. (2013). Comparing the relative effectiveness of advertising channels: A case study of a multimedia blitz campaign. Journal of Marketing Research, 50 (4), 517–534.

Danaher, P. J., Danaher, T. S., Smith, M. S., & Loaiza-Maya, R. (2020). Advertising effectiveness for multiple retailer-brands in a multimedia and multichannel environment. Journal of Marketing Research, 57 (3), 445–467.

Dekimpe, M. G. (2020). Retailing and retailing research in the age of big data analytics. International Journal of Research in Marketing, 37 (1), 3–14.

Dinner, I. M., Van Heerde, H. J., & Neslin, S. A. (2014). Driving online and offline sales: The cross-channel effects of traditional online display, and paid search advertising. Journal of Marketing Research, 51 (5), 527–545.

Direct Marketing Association (2015). National client email 2015. Research report. Retrieved April 6, 2021 from https://www.emailmonday.com/wp content/uploads/2015/04/National-client-email-2015-DMA.pdf .

Direct Marketing Association (UK) (2020). Marketer email tracker 2020. Research report. Retrieved April 6, 2021 from https://dma.org.uk/uploads/misc/marketer-email-tracker-2020.pdf .

Dorfman, R., & Steiner, P. O. (1954). Optimal advertising and optimal quality. American Economic Review, 44 (5), 826–836.

Drèze, X., & Bonfrer, A. (2008). Empirical investigation of the impact of communication timing on customer equity. Journal of Interactive Marketing, 22 (1), 36–50.

Duflo, E., Glennerster, R., & Kremer, M. (2007). Using randomization in development economics research: A toolkit. In T. P. Schultz & J. A. Strauss (Eds.), Handbook of development economics (Vol. 4, pp. 3895–3962). Elsevier.

Fischer, M., Albers, S., Wagner, N., & Frie, M. (2011). Practice prize winner. Dynamic marketing budget allocation across countries, products, and marketing activities. Marketing Science, 30 (4), 568–585.

Gendusa, J. (2022). 82 direct mail statistics you should know about in 2022, PostcardMania , Retrieved August 4, 2022 from https://www.postcardmania.com/blog/direct-mail-statistics-2022 .

Golder, P. N., Dekimpe, M. G., An, J. T., van Heerde, H. J., Kim, D. S., & Alba, J. W. (2023). Learning from data: An empirics-first approach to relevant knowledge generation. Journal of Marketing, 87 (3), 319–336.

Gordon, A. D. (1999). Classification . Chapman and Hall.

Book   Google Scholar  

Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., Ravishanker, N., & Sriram, S. (2006). Modeling customer lifetime value. Journal of Service Research, 9 (2), 139–155.

Hanssens, D. M., & Pauwels, K. H. (2016). Demonstrating the value of marketing. Journal of Marketing, 80 (6), 173–190.

Hanssens, D. M., Pauwels, K. H., Srinivasan, S., Vanhuele, M., & Yildirim, G. (2014). Consumer attitude metrics for guiding marketing mix decisions. Marketing Science, 33 (4), 534–550.

Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015). Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies. Journal of the Academy of Marketing Science, 43 (3), 375–394.

Hill, S., Provost, F., & Volinsky, C. (2006). Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 21 (2), 256–276.

Hoch, S. J., & Loewenstein, G. F. (1991). Time-inconsistent preferences and consumer self-control. Journal of Consumer Research, 17 (4), 492–507.

ISMS Practice Prize (2018). Gary L. Lilien ISMS-MSI Practice Prize Videos. Retrieved April 6, 2021 from https://lilienpracticeprizevideos.org/category/2018/ .

Jain, D. C., Vilcassim, N. J., & Chintagunta., P.K. (1994). A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data. Journal of Business and Economic Statistics, 12 (3), 317–328.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning . Springer.

Kamakura, W. A., & Russell, G. J. (1989). A probabilistic choice model for market segmentation and elasticity structure. Journal of Marketing Research, 26 (4), 379–390.

Kohli, A. K., & Haenlein, M. (2021). Factors affecting the study of important marketing issues: Implications and recommendations. International Journal of Research in Marketing, 38 (1), 1–11.

Kremer, S., Bijmolt, T., Leeflang, P., & Wieringa, J. (2008). Generalizations on the effectiveness of pharmaceutical promotional expenditures. International Journal of Research in Marketing, 25 (4), 234–246.

Kuebler, R., Pauwels, K., Yildirim, G., & Fandrich, T. (2018). App popularity: Where in the world are consumers most sensitive to price and user ratings. Journal of Marketing, 82 (5), 20–44.

Kumar, V., & Petersen, J. A. (2005). Using a customer-level marketing strategy to enhance firm performance: A review of theoretical and empirical evidence. Journal of the Academy of Marketing Science, 33 (4), 504–519.

Kumar, V., Zhang, X., & Luo, A. (2014). Modeling customer opt-in and opt-out in a permission-based marketing context. Journal of Marketing Research, 51 (4), 403–419.

L’Occitane (2015). 2015 Annual results announcement. Retrieved April 6, 2021 from https://group.loccitane.com/investors/financial-information .

Leckie, G., & Charlton, C. (2013). Runmlwin: A program to run the MlwiN multilevel modeling software from within Stata. Journal of Statistical Software, 52 , 1–40.

Lehmann, D. R. (2020). The evolving world of research in marketing and the blending of theory and data. International Journal of Research in Marketing, 37 (1), 27–42.

Lemmens, A., & Gupta, S. (2020). Managing churn to maximize profits. Marketing Science, 39 (5), 956–973.

Levinson, I. (2019). E-mail versus direct mail: Which works better? Retrieved April 6, 2021 from https://www.businessknowhow.com/directmail/ideas/compare.html .

Lilien, G. L. (2011). Bridging the academic–practitioner divide in marketing decision models. Journal of Marketing, 75 (4), 196–210.

Makles, A. (2012). Stata tip 110: How to get the optimal k-means cluster solution. The Stata Journal, 12 (2), 347–351.

Mantrala, M. K., Sinha, P., & Zoltners, A. A. (1992). Impact of resource allocation rules on marketing investment-level decisions and profitability. Journal of Marketing Research, 29 (2), 162–175.

Mark, T., Bulla, J., Niraj, R., Bulla, I., & Schwarzwäller, W. (2019). Catalogue as a tool for reinforcing habits: Empirical evidence from a multichannel retailer. International Journal of Research in Marketing, 36 (4), 528–541.

Medlar, A. (2017). Direct mail vs. email: If you can only use one, which wins? Retrieved April 6, 2021 from https://www.enthusem.com/blog/direct-mail-vs.-email-if-you-can-only-use-one-which-wins .

Morris, G. (2019). Which EU countries accept B2B emails post-GDPR. Retrieved April 6, 2021 from https://www.leadiro.com/blog/gdpr-mapped .

Naik, P. A., & Peters, K. (2009). A hierarchical marketing communications model of online and offline media synergies. Journal of Interactive Marketing, 23 (4), 288–299.

Niblock, R. (2017). Infographic: Direct mail vs email. Retrieved April 6, 2021 from https://www.digitaldoughnut.com/articles/2017/february/infographic-direct-mail-vs-email .

Papies, D., Ebbes, P., & Van Heerde, H. J. (2017). Addressing endogeneity in marketing models. In P. Leeflang, J. Wieringa, T. Bijmolt, & K. Pauwels (Eds.), Advanced methods for modeling markets (pp. 581–627). Springer.

Chapter   Google Scholar  

Pauwels, K., & Neslin, S. A. (2015). Building with bricks and mortar: The revenue impact of opening physical stores in a multichannel environment. Journal of Retailing, 91 (2), 182–197.

PostGrid (2022). Everything you need to know about direct mail and GDPR. Retrieved April 6, 2021 from https://www.postgrid.com/gdpr-compliance-for-direct-mail/ .

Prins, R., & Verhoef, P. C. (2007). Marketing communication drivers of adoption timing of a new e-service among existing customers. Journal of Marketing, 71 (2), 169–183.

Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and longitudinal modeling using Stata . Stata P.

Raudenbush, S., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods . Sage.

Return Path (2015). Frequency matters: The keys to optimizing email send frequency. Research report. Retrieved April 6, 2021 from http://returnpath.com/wp-content/uploads/2015/06/RP-Frequency-Report-FINAL.pdf .

Risselada, H., Verhoef, P. C., & Bijmolt, T. (2014). Dynamic effects of social influence and direct marketing on the adoption of high-technology products. Journal of Marketing, 78 (2), 52–68.

Roberts, J. H. (2000). The intersection of modelling potential and practice. International Journal of Research in Marketing, 17 (2/3), 127–134.

Roberts, M. L., & Berger, P. D. (1999). Direct marketing management . Prentice Hall International.

Rosenthal, R. (1984). Meta-analytic procedures for social science research . Sage.

Rossi, P. E. (2014). Even the rich can make themselves poor: A critical examination of IV methods in marketing applications. Marketing Science, 33 (5), 655–672.

Rust, R., & Verhoef, P. C. (2005). Optimizing the marketing interventions mix in intermediate-term CRM. Marketing Science, 24 (3), 477–489.

Sahni, N., Zou, D., & Chintagunta, P. K. (2017). Do targeted discount offers serve as advertising? Evidence from 70 field experiments. Management Science, 63 (8), 2397–2771.

Seenivasan, S., Sudhir, K., & Talukdar, D. (2016). Do store brands aid store loyalty. Management Science, 62 (3), 802–816.

Sethuraman, R., Tellis, G. J., & Briesch, R. (2011). How well does advertising work? Generalizations from meta-analysis of brand advertising elasticities. Journal of Marketing Research, 48 (3), 457–471.

Shiv, B., & Fedorikhin, A. (1999). Heart and mind in conflict: The interplay of affect and cognition in consumer decision making. Journal of Consumer Research, 26 (3), 278–292.

Snijders, T. A., & Bosker, R. J. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling . Sage.

Snyder, J. (2018). Post GDPR, clients will own data and agencies must get creative. Retrieved April 6, 2021 from https://adexchanger.com/data-driven-thinking/post-gdpr-clients-will-own-data-and-agencies-must-get-creative/ .

Srinivasan, S., Pauwels, K., Hanssens, D. M., & Dekimpe, M. G. (2004). Do promotions benefit manufacturers, retailers, or both? Management Science, 50 (5), 617–629.

Srinivasan, S., Rutz, O. J., & Pauwels, K. (2016). Paths to and off purchase: Quantifying the impact of traditional marketing and online consumer activity. Journal of the Academy of Marketing Science, 44 (4), 440–453.

Srinivasan, S., Vanhuele, M., & Pauwels, K. (2010). Mind-set metrics in market response models: An integrative approach. Journal of Marketing Research, 47 (4), 672–684.

Steenkamp, J.-B. E. M., & Geyskens, I. (2014). Manufacturer and retailer strategies to impact store brand share: Global integration, local adaptation, and worldwide learning. Marketing Science, 33 (1), 6–26.

Stremersch, S. (2021). The study of important marketing issues: Reflections. International Journal of Research in Marketing, 38 (1), 12–17.

Stremersch, S., Gonzalez, J., Valenti, A., & Villanueva, J. (2023). The value of context-specific studies for marketing. Journal of the Academy of Marketing Science, 51 (1), 50–65.

Sundaram, R. K. (1996). A first course in optimization theory . Cambridge University Press.

ter Braak, A., Geyskens, I., & Dekimpe, M. G. (2014). Taking private labels upmarket: Empirical generalizations on category drivers of premium private label introductions. Journal of Retailing, 90 (2), 125–140.

Tezinde, T., Smith, B., & Murphy, J. (2002). Getting permission: Exploring factors affecting permission marketing. Journal of Interactive Marketing, 16 (4), 28–39.

Timoumi, A., Gangwar, M., & Mantrala, M. K. (2022). Cross-channel effects of omnichannel retail marketing strategies: A review of extant data-driven research. Journal of Retailing, 98 (1), 133–151.

Valentini, S., Montaguti, E., & Neslin, S. A. (2011). Decision process evolution in customer channel choice. Journal of Marketing, 75 (6), 72–86.

Van Heerde, H. J., & Bijmolt, T. (2005). Decomposing the promotional revenue bump for loyalty program members versus nonmembers. Journal of Marketing Research, 42 (4), 443–457.

Verhoef, P. C. (2003). Understanding the effect of customer relationship management efforts on customer retention and customer share development. Journal of Marketing, 67 (4), 30–45.

Verhoef, P. C., Neslin, S. A., & Vroomen, B. (2007). Multichannel customer management: Understanding the research-shopper phenomenon. International Journal of Research in Marketing, 24 (2), 129–148.

Wedel, M., & Kamakura, W. A. (2002). Introduction to the special issue on market segmentation. International Journal of Research in Marketing, 19 (3), 181–183.

Wiesel, T., Pauwels, K., & Arts, J. (2011). Marketing's profit impact: Quantifying online and offline funnel progression. Marketing Science, 30 (4), 604–611.

Wooldridge, J. M. (2015). Control function methods in applied econometrics. Journal of Human Resources, 50 (2), 420–445.

Wright, M. (2009). A new theorem for optimizing the advertising budget. Journal of Advertising Research, 49 (2), 164–169.

Zhang, J. Z., & Chang, C. W. (2021). Consumer dynamics: Theories, methods, and emerging directions. Journal of the Academy of Marketing Science, 49 (1), 166–196.

Zhang, X., Kumar, V., & Cosguner, K. (2017). Dynamically managing a profitable email marketing program. Journal of Marketing Research, 54 (6), 851–866.

Zhang, Y., Bradlow, E. T., & Small, D. S. (2014). Predicting customer value using clumpiness: From RFM to RFMC. Marketing Science, 34 (2), 195–208.

Download references

Acknowledgements

We are grateful to the Wharton Customer Analytics Initiative for providing the data sets used in this study and the management of L’Occitane en Provence for their collaboration in this project and for their feedback. We thank Eric Bradlow, Peter Fader, Dominique Hanssens, Gary Lilien, John Roberts, and Christian Schulze for their useful suggestions on this work. The first author is grateful for Boston University’s Questrom School of Business doctoral funding support.

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

Author information

Authors and affiliations.

IESE Business School, University of Navarra, 08034, Barcelona, Spain

Albert Valenti

Questrom School of Business, Boston University, Boston, MA, 02215, USA

Shuba Srinivasan

Imperial College of London, London, SW7 2AZ, UK

Gokhan Yildirim

D’Amore-McKim School of Business, Northeastern University, Boston, MA, 02115, USA

Koen Pauwels

BI Norwegian Business School, Oslo, Norway

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Albert Valenti .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Marnik Dekimpe served as Area Editor for this article.

Supplementary Information

(DOCX 5562 kb)

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Valenti, A., Srinivasan, S., Yildirim, G. et al. Direct mail to prospects and email to current customers? Modeling and field-testing multichannel marketing. J. of the Acad. Mark. Sci. (2023). https://doi.org/10.1007/s11747-023-00962-2

Download citation

Received : 22 September 2021

Accepted : 28 June 2023

Published : 05 August 2023

DOI : https://doi.org/10.1007/s11747-023-00962-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Marketing mix effectiveness
• Multichannel retailing
• Direct mail
• Hierarchical linear model
• Find a journal
• Publish with us
• Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 08 April 2024

Tumor-selective activity of RAS-GTP inhibition in pancreatic cancer

• Urszula N. Wasko 1 , 2   na1 ,
• Jingjing Jiang 3   na1 ,
• Tanner C. Dalton 1 , 2 ,
• Alvaro Curiel-Garcia   ORCID: orcid.org/0000-0001-6249-3267 1 , 2 ,
• A. Cole Edwards 4 ,
• Yingyun Wang 3 ,
• Bianca Lee 3 ,
• Margo Orlen   ORCID: orcid.org/0000-0002-9834-6282 5 ,
• Sha Tian 6 ,
• Clint A. Stalnecker   ORCID: orcid.org/0000-0002-0570-4416 7 , 8 ,
• Kristina Drizyte-Miller 7 ,
• Marie Menard 3 ,
• Julien Dilly   ORCID: orcid.org/0000-0002-4006-5285 9 , 10 ,
• Stephen A. Sastra 1 , 2 ,
• Carmine F. Palermo 1 , 2 ,
• Marie C. Hasselluhn   ORCID: orcid.org/0000-0001-9765-4075 1 , 2 ,
• Amanda R. Decker-Farrell 1 , 2 ,
• Stephanie Chang   ORCID: orcid.org/0009-0000-2026-5215 3 ,
• Lingyan Jiang 3 ,
• Xing Wei 3 ,
• Yu C. Yang 3 ,
• Ciara Helland 3 ,
• Haley Courtney 3 ,
• Yevgeniy Gindin 3 ,
• Karl Muonio 3 ,
• Ruiping Zhao 3 ,
• Samantha B. Kemp 5 ,
• Cynthia Clendenin   ORCID: orcid.org/0000-0003-4535-2088 11 ,
• Rina Sor   ORCID: orcid.org/0000-0003-2042-5746 11 ,
• William P. Vostrejs   ORCID: orcid.org/0000-0002-1659-0186 5 ,
• Priya S. Hibshman 4 ,
• Amber M. Amparo   ORCID: orcid.org/0000-0003-3805-746X 7 ,
• Connor Hennessey 9 , 10 ,
• Matthew G. Rees   ORCID: orcid.org/0000-0002-2987-7581 12 ,
• Melissa M. Ronan   ORCID: orcid.org/0000-0003-4269-1404 12 ,
• Jennifer A. Roth   ORCID: orcid.org/0000-0002-5117-5586 12 ,
• Jens Brodbeck 3 ,
• Lorenzo Tomassoni 2 , 13 ,
• Basil Bakir 1 , 2 ,
• Nicholas D. Socci 14 ,
• Laura E. Herring   ORCID: orcid.org/0000-0003-4496-7312 15 ,
• Natalie K. Barker 15 ,
• Junning Wang 9 , 10 ,
• James M. Cleary 9 , 10 ,
• Brian M. Wolpin   ORCID: orcid.org/0000-0002-0455-1032 9 , 10 ,
• John A. Chabot 16 ,
• Michael D. Kluger 16 ,
• Gulam A. Manji 1 , 2 ,
• Kenneth Y. Tsai   ORCID: orcid.org/0000-0001-5325-212X 17 ,
• Miroslav Sekulic 18 ,
• Stephen M. Lagana 18 ,
• Andrea Califano 1 , 2 , 13 , 19 , 20 , 21 , 22 , 23 ,
• Elsa Quintana 3 ,
• Zhengping Wang 3 ,
• Jacqueline A. M. Smith   ORCID: orcid.org/0000-0001-5028-8725 3 ,
• Matthew Holderfield 3 ,
• David Wildes   ORCID: orcid.org/0009-0009-3855-7270 3 ,
• Scott W. Lowe   ORCID: orcid.org/0000-0002-5284-9650 6 , 24 ,
• Michael A. Badgley 1 , 2 ,
• Andrew J. Aguirre   ORCID: orcid.org/0000-0002-0701-6203 9 , 10 , 12 , 25 ,
• Robert H. Vonderheide   ORCID: orcid.org/0000-0002-7252-954X 5 , 11 , 26 ,
• Ben Z. Stanger   ORCID: orcid.org/0000-0003-0410-4037 5 , 11 ,
• Timour Baslan 27 ,
• Channing J. Der   ORCID: orcid.org/0000-0002-7751-2747 7 , 8 ,
• Mallika Singh 3 &
• Kenneth P. Olive   ORCID: orcid.org/0000-0002-3392-8994 1 , 2  

Nature ( 2024 ) Cite this article

6477 Accesses

226 Altmetric

Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

• Pancreatic cancer
• Pharmacodynamics

Broad-spectrum RAS inhibition holds the potential to benefit roughly a quarter of human cancer patients whose tumors are driven by RAS mutations 1,2 . RMC-7977 is a highly selective inhibitor of the active GTP-bound forms of KRAS, HRAS, and NRAS, with affinity for both mutant and wild type (WT) variants (RAS(ON) multi-selective) 3 . As >90% of human pancreatic ductal adenocarcinoma (PDAC) cases are driven by activating mutations in KRAS 4 , we assessed the therapeutic potential of the RAS(ON) multi-selective inhibitor RMC-7977 in a comprehensive range of PDAC models. We observed broad and pronounced anti-tumor activity across models following direct RAS inhibition at exposures that were well-tolerated in vivo . Pharmacological analyses revealed divergent responses to RMC-7977 in tumor versus normal tissues. Treated tumors exhibited waves of apoptosis along with sustained proliferative arrest whereas normal tissues underwent only transient decreases in proliferation, with no evidence of apoptosis. In the autochthonous KPC model, RMC-7977 treatment resulted in a profound extension of survival followed by on-treatment relapse. Analysis of relapsed tumors identified Myc copy number gain as a prevalent candidate resistance mechanism, which could be overcome by combinatorial TEAD inhibition in vitro . Together, these data establish a strong preclinical rationale for the use of broad-spectrum RAS-GTP inhibition in the setting of PDAC and identify a promising candidate combination therapeutic regimen to overcome monotherapy resistance.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Concurrent inhibition of oncogenic and wild-type RAS-GTP for cancer therapy

Matthew Holderfield, Bianca J. Lee, … Mallika Singh

Direct and selective pharmacological disruption of the YAP–TEAD interface by IAG933 inhibits Hippo-dependent and RAS–MAPK-altered cancers

Emilie A. Chapeau, Laurent Sansregret, … Tobias Schmelzle

Evolutionary trajectories of small cell lung cancer under therapy

Julie George, Lukas Maas, … Roman K. Thomas

Author information

These authors contributed equally: Urszula N. Wasko, Jingjing Jiang

Authors and Affiliations

Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA

Urszula N. Wasko, Tanner C. Dalton, Alvaro Curiel-Garcia, Stephen A. Sastra, Carmine F. Palermo, Marie C. Hasselluhn, Amanda R. Decker-Farrell, Basil Bakir, Gulam A. Manji, Andrea Califano, Michael A. Badgley & Kenneth P. Olive

Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA

Urszula N. Wasko, Tanner C. Dalton, Alvaro Curiel-Garcia, Stephen A. Sastra, Carmine F. Palermo, Marie C. Hasselluhn, Amanda R. Decker-Farrell, Lorenzo Tomassoni, Basil Bakir, Gulam A. Manji, Andrea Califano, Michael A. Badgley & Kenneth P. Olive

Revolution Medicines, Inc., Redwood City, CA, USA

Jingjing Jiang, Yingyun Wang, Bianca Lee, Marie Menard, Stephanie Chang, Lingyan Jiang, Xing Wei, Yu C. Yang, Ciara Helland, Haley Courtney, Yevgeniy Gindin, Karl Muonio, Ruiping Zhao, Jens Brodbeck, Elsa Quintana, Zhengping Wang, Jacqueline A. M. Smith, Matthew Holderfield, David Wildes & Mallika Singh

Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

A. Cole Edwards & Priya S. Hibshman

University of Pennsylvania Perelman School of Medicine, Department of Medicine, Philadelphia, PA, USA

Margo Orlen, Samantha B. Kemp, William P. Vostrejs, Robert H. Vonderheide & Ben Z. Stanger

Cancer Biology & Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Sha Tian & Scott W. Lowe

Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Clint A. Stalnecker, Kristina Drizyte-Miller, Amber M. Amparo & Channing J. Der

Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Clint A. Stalnecker & Channing J. Der

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

Julien Dilly, Connor Hennessey, Junning Wang, James M. Cleary, Brian M. Wolpin & Andrew J. Aguirre

Harvard Medical School, Boston, MA, USA

University of Pennsylvania Perelman School of Medicine, Abramson Cancer Center, Philadelphia, PA, USA

Cynthia Clendenin, Rina Sor, Robert H. Vonderheide & Ben Z. Stanger

The Broad Institute of Harvard and MIT, Cambridge, MA, USA

Matthew G. Rees, Melissa M. Ronan, Jennifer A. Roth & Andrew J. Aguirre

Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA

Lorenzo Tomassoni & Andrea Califano

Bioinformatics Core, Memorial Sloan Kettering Cancer Center, New York, NY, USA

Nicholas D. Socci

UNC Michael Hooker Proteomics Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Laura E. Herring & Natalie K. Barker

Department of Surgery, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA

John A. Chabot & Michael D. Kluger

Departments of Pathology, Tumor Microenvironment and Metastasis; H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

Kenneth Y. Tsai

Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA

Miroslav Sekulic & Stephen M. Lagana

Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

Andrea Califano

J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA

Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA

Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA

Chan Zuckerberg Biohub New York, New York, NY, USA

Howard Hughes Medical Institute, Chevy Chase, MD, USA

Scott W. Lowe

Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

Andrew J. Aguirre

Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA

Robert H. Vonderheide

Department of Biomedical Sciences, School of Veterinary Medicine, The University of Pennsylvania, Philadelphia, PA, USA

Timour Baslan

You can also search for this author in PubMed   Google Scholar

Corresponding authors

Correspondence to Mallika Singh or Kenneth P. Olive .

Supplementary information

Supplementary figure 1.

uncropped Western Blot images with marked areas of interest, and target molecular weight.

Reporting Summary

Supplementary tables.

This file contains Supplementary Tables 1-10.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Wasko, U.N., Jiang, J., Dalton, T.C. et al. Tumor-selective activity of RAS-GTP inhibition in pancreatic cancer. Nature (2024). https://doi.org/10.1038/s41586-024-07379-z

Download citation

Received : 18 July 2023

Accepted : 02 April 2024

Published : 08 April 2024

DOI : https://doi.org/10.1038/s41586-024-07379-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Help | Advanced Search

Computer Science > Machine Learning

Title: mixture-of-depths: dynamically allocating compute in transformer-based language models.

Abstract: Transformer-based language models spread FLOPs uniformly across input sequences. In this work we demonstrate that transformers can instead learn to dynamically allocate FLOPs (or compute) to specific positions in a sequence, optimising the allocation along the sequence for different layers across the model depth. Our method enforces a total compute budget by capping the number of tokens ($k$) that can participate in the self-attention and MLP computations at a given layer. The tokens to be processed are determined by the network using a top-$k$ routing mechanism. Since $k$ is defined a priori, this simple procedure uses a static computation graph with known tensor sizes, unlike other conditional computation techniques. Nevertheless, since the identities of the $k$ tokens are fluid, this method can expend FLOPs non-uniformly across the time and model depth dimensions. Thus, compute expenditure is entirely predictable in sum total, but dynamic and context-sensitive at the token-level. Not only do models trained in this way learn to dynamically allocate compute, they do so efficiently. These models match baseline performance for equivalent FLOPS and wall-clock times to train, but require a fraction of the FLOPs per forward pass, and can be upwards of 50\% faster to step during post-training sampling.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .