Marketing academia stands at a pivotal moment. The work we do extends far beyond publishing research—it shapes how we mentor students, influence organizations, and lead within our institutions. Whether you’re preparing for your first faculty position, navigating the tenure process, refining your teaching methods, or stepping into administrative roles, your career path deserves dedicated attention and support. The 2026 Summer Academic Conference recognizes that professional growth happens across multiple dimensions, and every stage of your academic journey matters.

Join us in Denver, CO, to connect with colleagues who share your commitment to excellence in all aspects of academic life. Through curated workshops, panels, and collaborative sessions, you’ll gain practical strategies for advancing your scholarship, teaching effectiveness, and leadership capabilities. This conference creates space for doctoral students seeking job market guidance, clinical faculty exploring new pedagogical approaches, and experienced professors transitioning into administrative positions. Together, we’ll build the knowledge, relationships, and momentum that fuel meaningful careers in marketing academia.

In-Person and Virtual Options

During the 2026 AMA Summer Academic Conference in Denver, CO, July 24-26, we will explore how marketing academics can strengthen their impact across scholarship, teaching, and organizational leadership at every career stage.

If you cannot join us in Denver, select virtual programming will be available on Monday, July 20.

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Why Attend?

Receive valuable feedback on your research from leading scholars and get guidance to help prepare your work for publication.

Engage in dynamic conversations on the latest marketing innovations and build meaningful connections with researchers, reviewers, and editors—advancing your career and gaining fresh perspectives on the field.

Dive deeper into current events and innovative topics in marketing through insightful Expert and Intensive Workshops facilitated by prominent academics in the industry.

Celebrate the achievements of our community at the largest AMA Academic Awards Luncheon, where we’ll honor the AMA-EBSCO-RRBM Annual Award for Responsible Research in Marketing winners, the Robert J. Lavidge Global Marketing Research Award recipient, and more.

Conference Co-Chairs

Kay Peters

UC Davis & University of Hamburg

Kelly Hewett

Colorado State University

Kim Whitler

University of Virginia

Meet the 2026 Conference Track Chairs

Conference Tracks

AMA Event Policies

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Around the world, marketing scholars, policymakers, and societal decision-makers are grappling with complex, interdependent challenges—from climate change and health inequities to data privacy and financial inclusion. These issues cross national, disciplinary, and institutional borders, and addressing them requires a collective, boundary-spanning approach. Recognizing and challenging these boundaries is essential to fostering more inclusive, innovative, and impactful research. By deliberately looking beyond these boundaries—both literal and figurative—we can discover alternative approaches and draw on examples of research, collaborations, and policies that meaningfully improve the well-being of individuals, businesses, societies, and the planet.

The 2026 Marketing & Public Policy Conference convenes in Ottawa, Ontario, marking the first time this conference has been held outside the United States. As the national capital of a country known for its pluralism and progressive social policies, Ottawa offers a compelling context for engaging in global dialogue. Explore research that examines the scope of marketing & public policy scholarship and reflects a diverse range of voices, disciplines, contexts and methodologies to address shared challenges and drive impactful marketing and public policy solutions.

Join us in Ottawa, Ontario, on May 16-18, 2026, to amplify global voices, address shared challenges, and increase the impact of marketing and policy research.

Keynote Speaker

Straight from the Heart: A Fireside Chat with the Honourable Jean Chrétien
Former Prime Minister of Canada (1993-2003)

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Why Attend?

Be part of an intimate community focused on advancing marketing’s role in addressing critical policy issues.

Co-create research-driven solutions that promote resilience and social impact through collaboration with academic and industry leaders.

Explore emerging insights at the intersection of marketing, public policy, and innovation.

Build meaningful relationships with experts and peers who share your commitment to impactful, interdisciplinary work.

Junior Scholars Workshops

Jump-start your conference experience by arriving early to connect with fellow scholars and receive mentorship on your work.

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Conference Co-Chairs

Monica LaBarge

Queen’s University

Jacob Brower

Queen’s University

Michael Mulvey

University of Ottawa

AMA Event Policies

The post 2026 AMA Marketing and Public Policy Conference appeared first on American Marketing Association.

Join Marc Pritchard, the Chief Brand Officer of Procter & Gamble, and AMA’s CEO, Bennie F. Johnson, for a conversation to explore marketing strategies, practices, and learning that will shape the future of the profession. 

The AMA is bringing together industry leaders and academics to advance marketing education. Join us for a thoughtful dialogue that will:

• Advance marketing education by allowing academics to engage with executives who are shaping the profession.
• Nurture marketing theory and research by giving scholars access to first hand accounts of the challenges facing leading executives.
• Support industry thought leadership and community interplay by bringing together industry leaders and academics to build the future of the profession.

This webinar airs at 12 PM and will be available on-demand for six months after airing.

The post AMA Executive in Residence Program appeared first on American Marketing Association.

Conference Proceedings

Explore the latest research from the 2026 event.

Bridging at the Frontiers: Marketing for a World in Transition

As the AMA’s first conference outside the U.S., Bridging at the Frontiers highlights marketing’s unique role in connecting diverse ideas, geographies and priorities while exploring the boundaries of innovation and practice. In today’s world of transition—marked by geopolitical tensions, within-country polarization and ideological contests, environmental challenges and the transformative impact of AI—marketers must navigate complex trade-offs and seize opportunities to create meaningful value.

The 2026 theme emphasizes the dual challenge of bridging divides—local and global, technological and human, ideological and practical—while advancing the frontiers of what marketing can achieve. How can marketing leaders respond to shifting societal norms and skepticism toward policies of sustainability and inclusion while fostering trust across diverse audiences? What role can AI play in transforming customer journeys while driving ethical innovation and equitable growth, such as balancing personalization with privacy and fairness? How can firms manage the dynamic tension between global ambitions and local relevance, ensuring their strategies resonate across distinct cultural and economic contexts?

Join us to explore these pressing questions and opportunities, from rethinking how marketing helps to balance resilience and efficiency in rapidly evolving markets to leveraging emerging technologies to address societal challenges. Together, we’ll bridge the gap between academia and practice, connect global trends with local realities, and chart new frontiers for marketing in a dynamic and interconnected world in transition.

Why Attend?

Hear new perspectives from colleagues across the discipline at this premier marketing research event, where top-tier scholars will present their compelling research. 

Engage in conversations about the latest research topics with researchers, reviewers and editors using our inclusive community and contribute to a more comprehensive approach to marketing.

Connect with like-minded scholars through various learning and networking opportunities.

Celebrate milestones within the community as AMA honors the 2026 AMA Fellows cohort, the 2026 AMA-Irwin-McGraw-Hill Distinguished Marketing Educator Award recipient, and other distinguished winners.

Maximize Your Time Onsite

Conference Co-Chairs

Michael Haenlein

ESCP Business School

K. Sudhir

Yale University

Ela Veresiu

York University

Meet the 2026 Conference Track Chairs

AMA Event Policies

The post 2026 AMA Winter Academic Conference appeared first on American Marketing Association.

Our research team analyzed how firms responded to the “China Shock,” a surge of imports that disrupted many U.S. industries between 2000 and 2019. We discovered that firms with influential marketing departments and well-established market-based assets—like differentiation and customer capital—were better able to weather these competitive pressures. Specifically, we found that:

1. Marketing Leadership is Crucial

Firms where marketing had a strong voice in strategic decisions outperformed their peers. By aligning cross-functional teams and advocating for customer-driven innovation, these firms launched initiatives that enhanced brand loyalty, improved product innovation, and strengthened competitive positioning.

2. Strategic Differentiation Matters

Differentiation also proved to be a powerful tool. Firms that emphasized unique product features, higher quality, or sustainability outperformed those competing solely on price. For example, branding efforts like “Made in America” or customization helped firms justify premium pricing and retain customers, even when faced with cheaper imports.

3. Customer Relationships Drive Resilience

Customer relationship capital rounded out the trio of success factors. Firms that invested in building long-term trust and loyalty with their customers faced less risk of losing market share. Strong customer ties created switching costs, making it harder for competitors to lure away buyers.

What Does this Mean for the C-Suite?

These insights have significant implications for executives. Many firms respond to financial pressures by cutting marketing budgets or sidelining marketing leaders from strategic discussions. However, our findings highlight the need to elevate marketing as a core function. Boards and CEOs can support marketing by increasing its decision-making authority and ensuring it is involved in board-level discussions.

Policymakers also have a role to play. While trade policies and tariffs are commonly used to protect domestic industries, our research suggests that empowering firms with marketing resources can offer a market-driven alternative to counter import competition. Public–private partnerships focused on branding, differentiation, and customer engagement could strengthen the competitiveness of domestic firms.

The need for marketing-driven strategies will only grow. Experts warn of a potential “China Shock 2.0,” which could flood global markets with low-cost imports in sectors like electric vehicles and solar panels. Firms must proactively strengthen their marketing leadership and differentiation efforts to withstand future competition.

For firms navigating a volatile global trade landscape, strong marketing capabilities can make the difference between thriving and folding.

Source: Nandini Ramani, “Can Marketing Enable Firms to Counter Import Competition? Evidence from the China Shock,” Journal of Marketing, 89 (5), 47–65.

Go to the Journal of Marketing

The post Struggling to Navigate Global Trade? Rely on the Power of Marketing appeared first on American Marketing Association.

In recent years, marketing scholars and practitioners have expressed growing concern about the diminishing influence of marketing departments. Against this backdrop, a Journal of Marketing Research study examines how governance networks may determine marketing department power (MDP). Drawing on data from over 6,000 publicly traded firms from 2007 to 2021, the researchers show that directors’ exposure through board service at other firms (i.e., board interlocks) affects MDP in the firms on whose boards they also serve (i.e., focal firms). More importantly, the strength of this effect hinges on three interlocking dimensions:

1. the reach of a firm’s board network,
2. the richness of marketing information within that network, and
3. the firm’s receptivity to information furnished by the board interlock network.

This work shifts the lens to upstream factors that shape MDP, suggesting that marketing’s influence is not built solely internally—it is also transmitted through board interlocks, making the board not only a governance body but also a conduit for influencing a firm’s MDP. For scholars of marketing’s organizational role, functional power, and network diffusion effects, this study offers a fresh vantage point and a reminder that if marketing wants to increase its power in firms, the conversation must extend beyond the CMO’s office into the boardroom.

For marketers, the core takeaway is clear: the board matters. Firms whose directors are connected to companies where marketing holds greater influence are more likely to elevate marketing’s importance within their own organization. These networks shape how leaders think about growth, which is a key priority for every board. While firms often call on marketers when facing serious challenges or major opportunities, marketing should not be reserved for exceptional circumstances. A key priority for marketers and CMOs is to educate their boards on how and why marketing drives firm growth, a shared goal across virtually all boards.

A key priority for marketers and CMOs is to educate their boards on how and why marketing drives firm growth, a shared goal across virtually all boards.

In short, governance structures are not just background context; they are powerful levers that can strengthen or diminish marketing’s strategic voice in the firm.

We recently had the opportunity to meet with all three authors of this research, who kindly offered additional insights into their motivations, managerial implications, and prospective avenues for future research.

Q: Your research shows that boards of directors, often overlooked in marketing, can shape marketing’s strategic importance. What led you to recognize the board as a missing piece in the marketing power puzzle, and how did this idea develop into the published study?

A: The idea grew from our earlier work on Chief Marketing Officers, where we found that firms that employ CMOs tend to perform better. But we also noticed that the presence of marketers on top management teams and the overall influence of marketing within firms has declined over time. That pattern made us think about what other forces might shape marketing’s standing in firms, beyond what happens inside the organization. The board of directors emerged as a natural next place to look, because directors serve on multiple boards and can bring with them ideas about what marketing should look like. When the Wharton Customer Analytics Initiative released a call for projects offering access to large-scale data on board linkages, it gave us a perfect opportunity to test this idea. That combination of prior research, the open question around marketing’s declining power, and the new data on board interlocks ultimately came together in this study.

Q: Do firms need a formal “Marketing Department” to have influence at the top, or is it enough to possess strong marketing capabilities and a deep understanding of what marketing brings to the organization?

A: That’s more of a philosophical question. While marketing today is highly cross-functional, a formal marketing function still matters. Having a defined department or leadership structure gives marketing visibility and accountability at the top. Without it, the customer perspective can easily get lost amid competing priorities. As we often say, “when something is everybody’s responsibility, it ends up being nobody’s responsibility.” A clear advocate, like a marketing department, helps ensure that the customer’s voice is represented in key decisions.

At the same time, the role of marketing looks very different across industries. In consumer goods and retail, marketing tends to have comprehensive control and plays a central strategic role. In banking, it often has a narrower focus on promotions or communications. In professional services like accounting, marketing is more standardized and peripheral. Unlike functions such as accounting, which look similar across most firms, marketing differs widely in scope, influence, and integration. That diversity makes it distinctive: its impact depends on how the organization chooses to empower it. Companies with a more comprehensive marketing approach tend to outperform those with limited marketing responsibilities. Ultimately, marketing power depends on balancing formal structure with shared responsibility.

Q: You show that marketing department power can diffuse across firms through board interlocks. In other areas, firms also learn through executive mobility, strategic alliances, shared consultants, or even investor influence. How does the kind of knowledge transfer you uncover through board ties differ from these other diffusion channels, and what kinds of marketing knowledge travels across boards?

A: Other knowledge transfer channels certainly exist, such as executive mobility or strategic alliances, and with the appropriate data, they could be modeled in a similar framework. Our study focuses specifically on board interlocks, and because we do not observe boardroom conversations directly, we can capture them only through proxies. Similar mechanisms may operate through other channels, but we cannot directly test them.

A key idea here is that boards prioritize growth. When a director sees marketing contributing to growth in one firm, that perspective may diffuse through the interlock to another board. What travels may be high-level mental models about how marketing contributes to performance, or, in some cases, even specific examples shared by directors. Still, the exact mechanism remains a conjecture because we do not observe the discussions themselves; we only observe their downstream effects.

Operationally, even though we cannot measure every variable directly, our use of instrumental-variable methods helps mitigate omitted-variable concerns in this observational setting. We also know from broader research that top management buy-in is essential. That is what makes boards distinctive: because the CEO reports directly to them, any shift in board-level thinking carries disproportionate weight. These mechanisms remain hypotheses that could be examined in more depth when richer data become available.

Q: When boards are interlocked within the same industry, marketing power may spread more easily across firms. Could that connectivity also create unintended consequences? For example, could firms converge on similar, potentially less differentiated strategies?

A: As board members generally cannot serve on the boards of direct competitors, true competitor‐to‐competitor interlocks are uncommon. However, if firms are not direct competitors but are in related industries, shared information could lead them to become more similar, potentially reducing differentiation and creating herding effects. This relates to some of the network measures we used, such as degree and brokerage. Degree centrality suggests that more connections may lead firms to behave more similarly. In contrast, in brokerage, a board member links otherwise unconnected parts of the network and can introduce more diverse and innovative ideas. So, the risk depends on the structure of the interlock network.

Technically speaking, more substantial board interlock effects may mean that firms are more likely to follow their existing connections. If boards increasingly form interlocks with boards they are already connected to, then the likelihood of convergence increases. Studying this convergence would require looking at network dynamics, how these connections form and evolve over time, presenting an interesting future direction. So, the risk depends on whether board networks become more tightly clustered. If that clustering does occur, the risk of strategic convergence increases.

Q: As marketing becomes linked to broader corporate priorities like DEI and ESG initiatives, does this interconnectedness strengthen marketing’s strategic influence or risk diluting its focus?

A: Any initiative that customers value is worth pursuing, whether it’s DEI, ESG, or something else. If diversity, equity, and inclusion lead to broader thinking and help the company better serve customers, they naturally add to both customer and corporate value. The key is to have a clear understanding of how these initiatives benefit customers. For example, Unilever’s Project Shakti in India empowered women entrepreneurs while also expanding distribution in rural markets. This is an example where a social initiative directly supported business goals. If firms can articulate how these priorities connect to customer value, then marketing’s role becomes more pronounced. But if the link isn’t clear, there’s a risk that marketing’s focus becomes scattered. Many companies still treat DEI and ESG as compliance initiatives rather than customer-driven ones, so marketing often isn’t leading those efforts. If marketing leads them and grounds them in what matters to customers, that can actually elevate marketing’s strategic influence rather than diluting it.

Q: If you were to extend this research further, which context or mechanism would you most like to explore to deepen our understanding of how governance structures shape marketing’s strategic importance?

A: From a technical perspective, an important next step would be to examine how board connections form and evolve. Some drivers are endogenous; for example, boards that share indirect connections are more likely to become directly connected, much like “friends of friends” becoming friends. Understanding those processes would be valuable, particularly when marketing-affiliated directors drive the connection. If a marketing-driven tie disappears and later reappears, is it due to a marketing-affiliated person? Examining these processes could deepen our understanding of marketing’s strategic influence.

More broadly, another valuable direction is to examine marketing’s organizational role and influence within firms. Some work, including this practitioner-oriented special issue, builds on the idea that marketing’s influence within firms has been declining and thus asks: how can marketers regain strategic influence? As the focus increasingly shifts to marketing activities and the creation of customer value, not merely the marketing department, future research should prioritize these value-creating functions rather than focusing solely on the department. In addition, management research suggests that board interlock effects have been weakening or disappearing. We do not see that in our data; the effect remains stable over time. That leads to an interesting question about what’s actually happening: is the board interlock effect still active?

References:

Frank Germann (2025), “Beyond the 4 Ps: Marketing’s Strategic Comeback [Special issue], NIM Marketing Intelligence Review, https://www.nim.org/en/publications/nim-marketing-intelligence-review/detail-issue/beyond-the-4-ps.

Frank Germann, Peter Ebbes, and Rajdeep Grewal (2015), “The Chief Marketing Officer Matters!” Journal of Marketing, 79 (3), 1–22. https://doi.org/10.1509/jm.14.0244.

Unilever (2024), “Harnessing the Potential of India’s Growing Workforce,” (July 23), https://www.unilever.com/news/news-search/2024/harnessing-the-potential-of-indias-growing-workforce/.

Source: Peter Ebbes, Frank Germann, and Rajdeep Grewal (2024), “Getting the Board on Board: Marketing Department Power and Board Interlocks,” Journal of Marketing Research, 62 (1), 1−21. doi:10.1177/00222437241272180

Go to the Journal of Marketing Research

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As a result, the ability to systematically analyze visual content has become essential for both managers and academic researchers. Image analytics enables firms to move beyond subjective evaluations of creative assets. By quantifying visual characteristics at scale, firms can evaluate, optimize, and personalize visual communication strategies across markets and customer segments. For researchers, image analytics provides a way to incorporate visual data into empirical analysis. By transforming images into structured, analyzable variables, researchers can investigate how visual elements shape consumer behavior and market outcomes across contexts, thereby advancing theory in domains where visual design plays a central role.

However, unlike structured data, images do not come with predefined variables. Researchers and managers must first decide which aspects of visual content matter for a given outcome and how to extract those variables from unstructured images in a reliable and scalable way.

Image variables can be broadly organized into three categories based on the level of visual meaning:

1. Low-level features capture visual properties derived directly from pixel values, such as color, brightness, and composition.
2. Mid-level features capture what is present in the image, such as objects, people, logos, or scenes.
3. High-level features capture how images are interpreted or evaluated, such as perceived emotion, aesthetics, or brand personality.

From a managerial perspective, these levels can be viewed as part of a broader decision process that links business outcomes, feature selection, measurement methods, and validation, as shown in Figure 1. In practice, the appropriate level depends on the objective of the image analytics task, as summarized in Table 1.

This article provides a structured framework to guide feature selection and measurement, synthesizing recent research from Journal of Marketing Research and related marketing and information systems journals to show how image analytics can inform theory and managerial actions.

Figure 1: Feature Selection and Measurement Framework for Image Analytics

Table 1: Choosing the Right Level of Visual Meaning

Low-Level Features: Color, Composition, and Basic Visual Properties

Objective image attributes such as file size, resolution, orientation (portrait vs. landscape), and foundational visual features drawn from photography research, including color, composition, and figure–ground relationships, provide a natural starting point for image analytics. These attributes are either directly observable or easy to extract, follow standardized definitions, and yield consistent values for the same image, enabling systematic comparison at scale.

Among intrinsic attributes, color has received the most sustained attention in marketing research (see Labrecque [2020] for a comprehensive review). Color is commonly characterized along three dimensions: hue (e.g., red, blue), saturation (intensity or richness), and value (lightness versus darkness), together often referred to as the hue–saturation–value (HSV) space. Consistent with prior reviews of color research in marketing (Labrecque 2020), much of the foundational evidence on color effects comes from controlled laboratory experiments that offer strong internal validity but limited scalability. Nowadays, image analytics enable researchers to extend color research to large-scale field datasets by measuring color properties directly from images. Image processing packages such as Python Pillow allow researchers to extract pixel-level color values, preferably in HSV space to facilitate interpretation, and construct theory-consistent color variables. For example, dominant colors are often identified using k-means clustering to group pixels with similar color characteristics and represent each cluster by its centroid (Labrecque et al. 2025). Other studies operationalize image clarity as the proportion of pixels exceeding a brightness threshold (Zhang et al. 2022) or measure colorfulness as one minus the combined pixel share of the three most dominant colors (Dang et al. 2026; Li and Xie 2020). Although specific parameter choices vary across studies, results are generally robust to reasonable alternative specifications. Major commercial vision platforms such as Google Vision AI, Amazon Web Services (AWS) Rekognition, and Microsoft Azure offer similar color detection capabilities.

Once constructed, color-related attributes can enter regression or machine learning models directly to explain or predict outcomes. For example, Li and Xie (2020) find that colorfulness affects user engagement on social media in a category-contingent manner, while Zhang et al. (2022) show that Airbnb photos with warmer hues, greater image clarity, and more balanced color properties associate with higher demand. Using gradient-boosted regression trees, Dzyabura et al. (2023) demonstrate that nuanced color composition clusters exert substantial predictive power for product returns, indicating that subtle shade differences can meaningfully affect consumer behavior. However, Zhang and Luo (2023) find that photographic attributes such as composition and brightness are less useful in predicting restaurant survival compared to the content of the photos.

Beyond their direct effects, color-related attributes also serve as building blocks or explanatory factors for higher-level perceptual constructs. Hou, Zhang, and Zhang (2023) show that warmer hues, higher saturation, and greater brightness evoke more positive emotions in charity crowdfunding images, while Zhang et al. (2022) find that professionally verified Airbnb photos achieve higher perceived quality in part due to more appealing color properties. Yu et al. (2026) further demonstrate that saturation, brightness, brightness contrast, and image clarity increase picture-evoked arousal but not valence in restaurant reviews. Complementing these findings, Labrecque et al. (2025) show that marketers systematically pair highly saturated product images with language emphasizing potency and efficacy.

Taken together, this body of work highlights the dual role of color-related attributes in image analytics. Color properties directly explain variation in consumer responses and market outcomes, while also serving as foundational visual factors that must be accounted for when studying higher-level creative strategies. In practice, failing to control for color can lead managers and researchers to misattribute performance differences to creative content or messaging when those differences instead reflect underlying visual properties (Labrecque 2020; Li and Xie 2020; Zhang et al. 2022). This risk is particularly pronounced when images vary substantially in brightness or colorfulness, when creative elements such as people presence or emotional expressions systematically co-occur with specific color patterns, or when analyses span product categories with distinct color conventions.

Mid-Level Features: Objects and Human Faces

Mid-level visual features capture what appears in the image rather than only how it looks at the pixel level. Common examples include detected objects (e.g., product, food, car, logo, sports equipment) and the presence and characteristics of human faces (e.g., face presence, number of faces, facial expressions). These features matter because they map directly onto content elements that consumers notice and interpret, and they often proxy for managerial choices about what to show, such as products versus lifestyle contexts or people versus objects.

Researchers can extract object- and face-based features using pretrained computer vision systems that return labels and localized regions. For example, Google Vision can identify multiple objects and faces and provide bounding boxes and facial attributes, including emotion likelihoods. Similar outputs are available from platforms such as AWS Rekognition, Microsoft Azure, and Clarifai, enabling straightforward feature construction ranging from simple presence or count measures to composition-based measures that rely on object size and location.

When standard outputs are insufficient, researchers can fine-tune pretrained deep learning models for task-specific classification. For example, Hartmann et al. (2021) train a Visual Geometry Group (VGG-16) convolutional neural network (CNN) model to distinguish consumer selfies from brand selfies, and Li et al. (2022) use transfer learning with a Residual Network (ResNet-50) architecture to classify room types in Airbnb photos. Table 2 summarizes the key conceptual steps in fine-tuning pretrained CNNs for such tasks. In practice, fine-tuning is most commonly used for mid-level (e.g., object or content detection) and high-level (e.g., perception or evaluation prediction) image analytics tasks, whereas low-level visual properties such as color or brightness are typically extracted using standard image processing tools.

Table 2: Conceptual overview of fine-tuning a pretrained Convolutional Neural Networks (CNN)

In marketing contexts, including people in images is a common and highly consequential creative decision. However, evidence across settings shows that the effects of human presence are highly context dependent. Li and Xie (2020) find that images with human faces increase attention and engagement on Twitter but not on Instagram. Lu, Jung, and Peck (2024) show that in identity-relevant contexts such as vacations or weddings, including another person can reduce liking and preference by triggering psychological ownership concerns. In social media branding, Hartmann et al. (2021) document a similar trade-off: Consumer selfies generate more likes and comments, whereas product-focused brand selfies elicit stronger brand engagement and purchase intentions. In online reviews, Guan et al. (2023) find that reviewer face disclosure increases subsequent product ratings by reducing uncertainty about product fit. Together, these findings indicate that human presence does not uniformly enhance image effectiveness; its impact depends on platform norms, consumption goals, and the role the image plays in the decision process. Beyond direct effects, human and object presence also shape image effectiveness indirectly by influencing emotional responses. In charity crowdfunding, Hou, Zhang, and Zhang (2023) show that images featuring people heighten excitement while suppressing awe and selectively amplify or reduce negative emotions, whereas images with animals evoke a distinct emotional profile. Taken together, this work underscores the importance of explicitly modeling human and object presence in image analytics, both as direct predictors and as drivers of emotional and evaluative mechanisms that influence downstream outcomes.

High-Level Features: Emotion, Quality, Aesthetics, and beyond

High-level visual features capture how people respond to and evaluate an image or an object within an image. These features reflect subjective interpretations, such as the emotions an image evokes (Hou, Zhang, and Zhang 2023), the quality or aesthetic appeal it conveys (Zhang et al. 2022, Guan et al. 2023) and person-related attributes such as celebrity potential (Feng et al. 2025) or attractiveness (Malik, Singh, and Srinivasan 2023).

Prior research demonstrates that these perceptual constructs play an important role across a wide range of contexts. For example, images that evoke specific emotions influence engagement and donation behavior in charity crowdfunding (Hou, Zhang, and Zhang 2023). Perceived visual quality and aesthetic appeal shape evaluations in hospitality and online review settings (Guan et al. 2023; Zhang et al. 2022). Face-related attributes inferred from images, such as celebrity potential or attractiveness, affect influencer selection, hiring decisions, and long-term career outcomes (Feng et al. 2025; Malik, Singh, and Srinivasan 2023; Troncoso and Luo 2023). Together, these findings show that high-level visual features capture meaningful variation in how images shape evaluations and decisions, even though their effects often depend on context and task.

High-level visual features offer three key advantages for image analytics:

1. They align measurement with how marketing theory conceptualizes decision making. Many theories emphasize perceptions and judgments as the link between marketing stimuli and outcomes.
2. They provide a compact way to summarize complex visual information, improving stability and making comparisons easier across platforms, categories, and context.
3. They improve interpretability for both researchers and managers by translating visual variation into psychologically meaningful constructs that are easier to explain and act on.

Researchers typically construct high-level visual features using the same CNN-based framework applied to other task-specific image analytics, consistent with the workflow summarized in Table 2. The main difference lies in task definition and labeling: Instead of predicting objects or content categories, models infer perceptual judgments or attributes based on human evaluations or validated proxies. The resulting predictions then serve as quantitative measures that can be incorporated directly into empirical models.

Advertising and consumer behavior research has long emphasized that visual and verbal elements are processed jointly and that their congruence shapes consumer responses (Heckler and Childers 1992). Advances in image analytics now allow researchers and managers to measure these relationships directly and at scale. Methodologically, this stream of research uses deep learning models to generate representations for images and text and then constructs measures that capture how visual and verbal content relates across modalities. These measures can be validated against human judgments and incorporated into empirical models to study how visual and verbal cues jointly shape perceptions and decisions.

A central insight from this literature is that consumer responses depend critically on how image and text content relate to one another. Shin et al. (2020) show that image–text similarity substantially improves prediction of social media content popularity and consumer engagement. Li and Xie (2020) find that stronger image–text fit increases user engagement on Twitter but not on Instagram, underscoring platform-specific processing differences. In online reviews, Ceylan, Diehl, and Proserpio (2024) and Yu et al. (2026) show that alignment between photos and text in both content and emotional valence and arousal improves review helpfulness by enhancing processing fluency. Extending beyond reinforcement, Cao, Li, and Zhang (2025) uncover a U-shaped effect of image–text congruence in product representations, showing that both high congruence driven by relevance and deliberate incongruence driven by surprise can enhance consumer preference. Together, this steam of work highlights the importance of coordinating visual and verbal cues rather than optimizing them in isolation.

Putting the Framework into Practice: A Multilevel View of a Marketing Image

Figure 2: Example of Multilevel Image Measurement in a Social Media Post

To illustrate how image analytics can support managerial decision making, consider the Nike social media post shown in Figure 2. The same image can be analyzed at multiple levels depending on the business objective. Rather than extracting every possible visual feature, the goal is to select image variables and measurements that match the decision being supported.

At the low level, managers can measure visual style and properties such as color distribution, brightness, contrast, and background uniformity using standard image-processing tools (e.g., Python image libraries, vision APIs). These measures help ensure visual consistency within and across campaigns and help isolate the effects of higher-level creative decisions. In this example, the dark background and strong contrast visually isolate the product and increase visual salience. More broadly, by quantifying background tone and contrast across posts, Nike can test when high-contrast, minimalist imagery enhances engagement or conversion relative to visuals featuring brighter or more visually complex backgrounds. These insights allow managers to tailor visual style across platforms, product categories, and campaign objectives.

At the mid-level, managers can measure what is present in the image. Object detection or classification models can identify the product, logo, product components, and the presence or absence of human. These measures support decisions about product-focused versus lifestyle-centered creative strategy, brand visibility, and content tagging. In this example, the absence of people and the sole visual focus on the shoe signal a produce-centric creative strategy that emphasizes technology and performance. Prior research shows that such content choices have meaningful consequences: Hartmann et al. (2021) find that consumer selfies generate more likes and comments, whereas product-focused brand selfies elicit stronger brand engagement and purchase intentions. By quantifying whether images feature products alone or include people, Nike can align its creative strategy with campaign objectives. Product-centric imagery can be emphasized when the goal is to strengthen purchase intent and brand evaluation. In contrast, incorporating human elements may be more effective when the objective is to increase social interaction.

At the high level, managers can gauge how consumers interpret the image. Custom models or human-assisted coding can be used to measure perceived excitement, performance intensity, or innovation cues. These constructs are directly linked to engagement, purchase intent, and brand perception (Guan et al. 2023; Hou, Zhang, and Zhang 2023; Zhang et al. 2022). In the Nike example, the dark background, strong contrast, and focused presentation of the shoe collectively convey a high-performance and technologically advanced impression. Such measures allow managers to evaluate whether an image communicates the intended brand meaning or emotional tone before deployment at scale.

When text is present, managers can also evaluate image–text alignment. Here, the performance-focused copy aligns closely with the high-energy technical visual, reinforcing the product message. This alignment strengthens the overall consumer interpretation. Prior research demonstrates that alignment between visual and verbal cues can enhance engagement and processing fluency, while strategic incongruence may also increase attention in some contexts (Cao, Li, and Zhang 2025; Ceylan, Diehl, and Proserpio 2024; Shin et al. 2020; Li and Xie 2020; Yu et al. 2026). Extending this approach across posts allows Nike to evaluate when alignment between images and text enhances engagement and conversion outcomes and when alternative strategies may be more effective.

Together, these levels provide complementary insights, moving from visual style, to content strategy, to consumer interpretation and market outcomes.

Summary

This article offers a practical framework for making sense of visual content in digital marketing. It shows how images can be analyzed at three levels—basic visual properties, content elements such as products and people, and higher-level perceptions such as emotion and quality—and explains when each level is most useful for understanding performance. The article also highlights the growing importance of analyzing images together with text, since consumers often interpret visual and verbal cues jointly. By synthesizing recent research and outlining scalable analytic approaches, the framework helps managers and researchers choose the right visual features, avoid misleading conclusions, and design images that communicate more effectively across platforms and contexts.

References

Cao, Jingcun, Xiaolin Li, and Lingling Zhang (2025), “Is Relevancy Everything? A Deep-Learning Approach to Understand the Effect of Image-Text Congruence,” Management Science, 71 (12), 10579–10602. https://doi.org/10.1287/mnsc.2022.01896

Ceylan, Gizem, Kristin Diehl, and Davide Proserpio (2024), “Words Meet Photos: When and Why Photos Increase Review Helpfulness,” Journal of Marketing Research, 61 (1), 5–26. https://doi.org/10.1177/00222437231169711

Dang, Ivy Chu, Canice M.C. Kwan, Jayson S. Jia, and Yang Shi (2026), “When Words Meet Visuals: How Content Composition Drives Social Media Engagement for Marketer-Generated Content,” Journal of Marketing Research, 63 (1), 167–90. https://doi.org/10.1177/00222437251373042

Dzyabura, Daria, Siham El Kihal, John R. Hauser, and Marat Ibragimov (2023), “Leveraging the Power of Images in Managing Product Return Rates,” Marketing Science, 42 (6), 1125–42. https://doi.org/10.1287/mksc.2023.1451

Feng, Xiaohang, Shunyuan Zhang, Xiao Liu, Kannan Srinivasan, and Cait Lamberton (2025), “An AI Method to Score Celebrity Visual Potential,” Journal of Marketing Research, 62 (5), 757–75. https://doi.org/10.1177/00222437251323238

Guan, Yue, Yong Tan, Qiang Wei, and Guoqing Chen (2023), “When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics,” Information Systems Research, 34 (4), 1641–63. https://doi.org/10.1287/isre.2023.1201

Hartmann, Jochen, Mark Heitmann, Christina Schamp, and Oded Netzer (2021), “The Power of Brand Selfies,” Journal of Marketing Research, 58 (6), 1159–77. https://doi.org/10.1177/00222437211037258

Heckler, Susan E. and Terry L. Childers (1992), “The Role of Expectancy and Relevancy in Memory for Verbal and Visual Information: What Is Incongruency?” Journal of Consumer Research, 18 (4), 475–92. https://doi.org/10.1086/209275

Hou, Jian-Ren, Jie Zhang, and Kunpeng Zhang (2023), “Pictures That Are Worth a Thousand Donations: How Emotions in Project Images Drive the Success of Online Charity Fundraising Campaigns? An Image Design Perspective,” MIS Quarterly, 47 (2), 535–84. https://doi.org/10.25300/MISQ/2022/17164

Labrecque, Lauren (2020), “Color Research in Marketing: Theoretical and Technical Considerations for Conducting Rigorous and Impactful Color Research,” Psychology & Marketing, 37 (7), 855–63. https://doi.org/10.1002/mar.21359

Li, Hanwei, David Simchi-Levi, Michelle Xiao Wu, and Weiming Zhu (2022), “Estimating and Exploiting the Impact of Photo Layout: A Structural Approach,” Management Science, 69 (9), 5209–33. https://doi.org/10.1287/mnsc.2022.4616

Li, Yiyi and Ying Xie (2020), “Is a Picture Worth a Thousand Words? An Empirical Study of Image Content and Social Media Engagement,” Journal of Marketing Research, 57 (1), 1–19. https://doi.org/10.1177/0022243719881113

Lu, Zoe Y., Suyeon Jung, and Joann Peck (2024), “It Looks Like ‘Theirs’: When and Why Human Presence in the Photo Lowers Viewers’ Liking and Preference for an Experience Venue,” Journal of Consumer Research, 51 (2), 321–41. https://doi.org/10.1093/jcr/ucad059

Malik, Nikhil, Param Vir Singh, and Kannan Srinivasan (2023), “When Does Beauty Pay? A Large-Scale Image-Based Appearance Analysis on Career Transitions,” Information Systems Research, 35 (4), 1524–45. https://doi.org/10.1287/isre.2021.0559

Shin, Donghyuk, Shu He, Gene Moo Lee, Andrew B. Whinston, Suleyman Centintas, and Kuang-Chih Lee (2020), “Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach,” MIS Quarterly, 44 (4), 1459–92. https://doi.org/10.25300/MISQ/2020/14870

Troncoso, Isamar and Lan Luo (2022), “Look the Part? The Role of Profile Pictures in Online Labor Markets,” Marketing Science, 42 (6), 1080–1100. https://doi.org/10.1287/mksc.2022.1425

Yu, Yifan, Xinyao Wang, Jinghua Huang, and Yong Tang (2026), “The Pleasant Visual Path to Review Helpfulness: Picture-Evoked Emotional Valence and Picture-Text Alignment,” MIS Quarterly, 50 (1), 243–68. https://doi.org/10.25300/MISQ/2025/17965

Zhang, Mengxia and Lan Luo (2022), “Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp,” Management Science, 69 (1), 25–50. https://doi.org/10.1287/mnsc.2022.4359

Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, and Kannan Srinivasan (2022), “What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features,” Management Science, 68 (8), 5644–66. https://doi.org/10.1287/mnsc.2021.4175

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Marketers have long recognized that customers acquired through referrals tend to be more loyal and valuable. What has remained underappreciated, however, is the additional value these customers may generate through their future referral behavior. In their recent Journal of Marketing Research article, Rachel Gershon (University of California, Berkeley) and Zhenling Jiang (University of Pennsylvania) uncover a “referral contagion” and show that referred customers are not just more profitable but also more likely to refer others, setting off a multiplier effect that many firms have overlooked so far.

Beyond Acquisition: The Hidden Downstream Value of Referrals

Referral programs are ubiquitous, from ride-sharing and food delivery apps to fintech platforms and online retailers. Typically, marketers have evaluated these referral programs by counting how many new customers they bring in and how much revenue those customers generate. Gershon and Jiang argue that this approach severely underestimates the true value of referral programs.

Across multiple field data sets, they show that referred customers make between 31% and 57% more referrals than those acquired through other channels. When these secondary referrals are ignored, firms end up undervaluing the total worth of a referral by 20% to 36%. The authors demonstrate this referral contagion across a wide range of industries, including finance, software, and retail.

Why Referrals Spread: The Role of Social Appropriateness

Gershon and Jiang show that referred customers are more likely to refer others because the act of referring feels more socially appropriate to them. Drawing on insights from social psychology, they find that when people see someone else refer, they interpret the behavior as socially acceptable, reducing the fear of seeming too pushy or self-interested.

In several controlled experiments, participants who imagined joining an app through a friend’s referral rated the act of referring as more appropriate, felt lower psychological discomfort, and were significantly more likely to make referrals themselves compared to those who imagined joining through an ad. This effect was stronger when the referrer was a friend rather than an influencer, emphasizing that personal recommendations drive the norm of appropriateness more than celebrity endorsements.

The Power of a Simple Nudge: “You Were Referred In – Now Refer Your Friends!”

To translate their insights into practice, the authors conducted a large-scale field experiment with over 10 million referred customers. A simple tweak made all the difference: instead of a generic “Refer your friends!” message, half the customers received a reminder tied to their own experience: “You were referred in – now refer your friends!” The message activated the existing social norm, made referring feel more appropriate, and ultimately boosted referrals by more than 20%.

A simple tweak made all the difference: instead of a generic “Refer your friends!” message, half the customers received a reminder tied to their own experience: “You were referred in – now refer your friends!” The message activated the existing social norm, made referring feel more appropriate, and ultimately boosted referrals by more than 20%.

The study illustrates the value of industry–research collaborations. Companies gain evidence-based insights that go beyond intuition, while researchers gain access to real-world data and the opportunity to test ideas at scale. We reached out to the authors to learn more about the inspiration behind their work and what their results mean for managers. In the conversation below, Gershon and Jiang share their perspective on how referral contagion works, how firms can capture its full value, and where future opportunities lie for practitioners.

Q: What first sparked your interest in exploring the “referral contagion”?

A: The idea emerged from our observation of a robust pattern in our dataset: referred customers were substantially more likely to refer others. We found this pattern both intriguing and theoretically meaningful. When reviewing the literature, we saw that prior research had largely overlooked this downstream consequence of referral behavior, which inspired us to systematically investigate what we later termed “referral contagion.”

Q: Your research shows that referred customers don’t just buy more, they also refer more. Based on this, how should managers rethink how they measure the total value of their referral programs? 

A: While prior studies have examined the direct benefits of referred customers (such as higher loyalty and spending), they have largely overlooked their indirect impact through subsequent referrals. Managers should incorporate these downstream effects into how they assess the value of referral programs, including it in metrics like customer lifetime value (CLV) and the effective ROI of referral incentives.

Q: A simple reminder to referred customers can boost referrals by about 21%. Where might this nudge stop working, and how could marketers adapt it in practice?

A: Reminding customers that they were once referred signals that referring is appropriate. Making this social norm salient increases referral behavior. We expect this nudge to be effective in scenarios where psychological barriers prevent customers from making referrals, likely extending across different product categories, tie strengths, and incentive types. Exploring how these factors shape the effectiveness of the reminder presents an interesting direction for future research.

Q: Referral contagion seems relevant beyond business, such as in public health. How might your findings inform policymakers?

A: The idea of referral contagion naturally extends beyond business contexts. For policymakers in public health systems, this means that investments in referral-based outreach could have a multiplier effect, as those who are referred become more likely to refer others. Programs could be strengthened by highlighting that referring others is common and appropriate.

Q: Your research relies on large-scale field data and company collaboration. What challenges did you face in building these partnerships and collecting real-world data at this scale?

A: We began by reaching out to a wide range of potential field partners. It’s a numbers game: we cast a wide net and had many conversations until we found organizations whose interests and data aligned with our research goals. We were fortunate to identify three enthusiastic and collaborative partners.

Q: What do marketing managers gain from working with academic researchers, and vice versa?

A: Collaborations are most valuable when both sides view them as a valuable exchange. For managers, they offer a chance to go beyond intuition and understand what drives customer behavior, grounded in careful experimentation and analysis. For researchers, they provide access to rich data and the opportunity to test ideas in real-world settings. Collaborations reveal the challenges of translating theoretical insights into practice and how organizational constraints, competing priorities, and practical considerations shape what’s possible. Working with firms often challenges our assumptions and helps us refine our theories to be more relevant and impactful.

Source: Rachel Gershon and Zhenling Jiang (2024), “Referral Contagion: Downstream Benefits of Customer Referrals.” Journal of Marketing Research, 62 (1), 97–116. doi:https://doi.org/10.1177/00222437241257886.

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Our research team defines “immersive services” as those in which consumers are deeply embedded for a period of time, with their experiences largely constructed by the service. This includes industries like healthcare, education, hospitality, and eldercare. We identify four key characteristics of these services that can challenge consumer agency:

1. Encapsulation: Consumers are deeply immersed in the service, often separated from other parts of their lives.
2. Positionality: Hierarchies and power dynamics create stark differences between consumers and service providers.
3. Protocolization: Rigid routines and protocols dictate consumer behavior.
4. Multivocality: Multiple voices and perspectives within the service influence how consumers are expected to act.

These characteristics can make it difficult for consumers to act freely, thus affecting their well-being. For instance, consider healthcare settings where patients are required to follow strict protocols, or eldercare facilities where residents may feel constrained by rigid schedules. As polarization and AI-driven decision making become more common, these challenges are becoming even more pressing.

We discover, however, that consumers are not passive participants in immersive services. Instead, they actively work to regain their sense of agency through “improvisations”—creative strategies that allow them to navigate the constraints of the service. Specifically, consumers use five pathways to reclaim agency:

1. Expanding the figured world: Shaping their experience on their own terms by exerting control over time and space.
2. Voicing: Speaking out to challenge rules or advocate for changes in how they are treated.
3. Seeking task responsibility: Taking on meaningful tasks to assert independence and purpose.
4. Challenging protocols: Pushing back against rigid processes to co-create a service experience that better fits their needs.
5. Playing and imagining: Using creativity and imagination to reframe their experience and celebrate life.

For service managers, these findings offer clear strategies to empower consumers while maintaining necessary structure. Two key managerial approaches stand out:

1. Leverage technology to expand consumer freedom: Virtual tools and personalized digital platforms can help consumers navigate encapsulation and protocolization by providing more choices and flexibility.
2. Develop empathy-driven relationships: By fostering stronger interpersonal connections, service providers can address positionality and multivocality, helping consumers feel valued and heard.

We recommend a two-pronged approach to assess and address gaps in consumer agency. First, managers should analyze how the four structural characteristics—encapsulation, positionality, multivocality, and protocolization—impact consumers. Second, they should evaluate how effectively their services support the five pathways consumers use to regain agency.

Immersive services are critical to modern life, but they must evolve to meet the needs of consumers. By empowering consumers to reclaim their agency, service providers can enhance customer satisfaction, foster loyalty, and improve overall wellbeing.

Source: Laurel Anderson, Catharina Von Koshull, Martin Mende, and Johanna Gummerus, “Immersive Service: Characteristics, Challenges, and Pathways to Consumer Agency,” Journal of Marketing.

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Picture your great¹⁰⁰⁰ grandma crouched by a fire pit 25,000 years ago, deciding whether to eat unfamiliar berries or face starvation. She braves the bitterness, survives, and passes her taste-sensing genes through generations, eventually reaching you. Fast forward to today: you’re ordering an extra-dark roast at Starbucks while your friend frowns over your “bitter” choice. Little do they know, your ancient ancestor might still be calling the shots.

Now here’s the twist: major genetic testing companies have collected DNA from 30+ million people, including data that reveals the ancestors’ taste legacy in unprecedented detail. Companies can potentially benefit from this genetic treasure. But should they? When does betting on ancient taste make business sense? How can marketers decipher these ancient ties and utilize them in their decision making?

In a recent Journal of Marketing Research study, authors Remi Daviet and Gideon Nave analyzed genetic and survey data from 182,212 UK adults, examining 1.5 million genetic variations across seven taste dimensions (bitter, fatty, salty, savory, sour, spicy, and sweet). Their study provides the first large-scale empirical assessment of how genetic information performs against traditional demographic, behavioral, and consumption data in real-world marketing applications.

The results from Daviet and Nave’s study are remarkably promising: genetic data can predict 10.9% to 12.5% of taste preferences, which is meaningful for business decisions. Genetic data shines brightest for uncommon tastes that don’t appear in consumption data, delivering 97% to 233% improvements over traditional methods for flavors like spicy, sour, and bitter. Even familiar tastes saw gains ranging from 28% to 68%.

Genetic data boosts the prediction accuracy of what customers will crave before they know it themselves, giving companies a first-mover advantage in untapped preferences.

Implications in Different Contexts

• Food/Beverage Companies: Target customers before they discover niche tastes, especially for products with uncommon flavor profiles.
  
  
• Healthcare/Pharma: Develop better-tasting formulations for genetically bitter-averse patients to improve medication adherence.
  
  
• Meal Kit Services: Use genetic screening to curate boxes that match individual taste predispositions, reducing returns and waste.
  
  
• Government Agencies: Design nutrition programs that align with genetic predispositions rather than fighting against them.

To explore the real-world implications of this research, we interviewed both authors about the practical questions their findings raise. Our conversation moved from research motivations and surprising discoveries to business cases and implementation strategies, before examining broader industry opportunities and future evolution.

Q: Was there any specific moment, observation, or personal experience that made you think, “we need to research this?” Was doing a genetic test the inspiration?

Dr. Nave: It was just the right time for this. There is a lot of genetic data that was never available before, and research from twin studies shows many behaviors are heritable and genetics should be informative of them. Although there were a few commercial applications, it’s unclear when managers should use this data. We wanted to look for the most basic input to this process, which is how predictive genetic data is relative to other variables. Lastly, as academic researchers, we chose nutrition and diet as our focus because this research can potentially improve people’s lives and contribute to social benefit.

Dr. Daviet: I did genetic testing because I was curious. I actually did it in Europe because they have better consumer protection for genetic data. We examined food taste because it’s one of the characteristics that is heavily heritable and relevant. We know that taste preferences are a very strong predictor of consumption. That was a good case study, demonstrating that genetics has an effect and is relevant to predicting consumption.

Q: Were there any surprising or unexpected findings in your study that challenged your initial assumptions? How did the research evolve from the surprising findings?

Dr. Daviet: We know from past research that genetics is predictive of most behavior to some extent. We were unsure whether genetics would offer predictive value beyond other factors, such as sociodemographic background or consumption patterns. My prediction was that it would add some predictive power to a bit of everything, but that was not the case. There are others where it adds a lot of predictive power, such as tastes like bitter, spicy, or sour, which are not often consumed in the local British diet.

Dr. Nave: For many tastes, we know that there are genetically programmed sensitivities because of known genes. For example, there is a receptor in the tongue that senses spiciness. To our surprise, genes that are known to be related to sensitivity to these tastes do not have a strong effect on preferences. Most of the genetic variants that are predictive cannot be directly linked to a known biological mechanism.

Dr. Daviet: There is a specific gene that can predict how people are sensitive to sourness, whether they can detect sourness in a sample. We were expecting that this would predict well if people like sour or not, but actually not. Instead, the liking is a lot of tiny effects across the genome that accumulate to create the overall taste preference, which is very complex.

Q: Suppose you were advising a Fortune 500 CEO who’s skeptical about investing in genetic marketing. What would be your elevator pitch to convince them this isn’t just academic curiosity but a real business opportunity?

Dr. Daviet: They don’t have to invest in marketing, and they can just let the competitor do it and gain a competitive advantage if they prefer. One of the strong advantages of genetics is its ability to identify patterns not revealed in past purchase data. This can help you identify new markets where there is no data, as they are unexplored and lack existing products. It can help you personalize based on different segments, something that traditional data might miss because either there is no data about it or it’s at an aggregate level.

Dr. Nave: Imagine you know what a consumer will need or will love before they even buy it—before they realize it themselves. One example is male balding patterns. This tendency is genetic so that you can predict it from birth. Knowing this allows you to build your brand image among potential customers before they become actual customers. Often, we only reveal certain traits after a while, and having first access is a significant competitive advantage.

Q: What are the most realistic applications of your findings? For example, if I’m launching a new energy drink, can you walk us through a simple, nontechnical roadmap? What’s the step-by-step genetic marketing playbook?

Dr. Nave: Imagine your energy drink has several flavors: bitter coffee, sweet strawberry, and sour lemon. These tastes are determined by people’s genetic profile, not just demographics. You could partner with a company like Ancestry.com to market to people with a certain flavor preference, without needing to collect the genetic data yourself. The key factor is that the data is very sensitive, and people may react very negatively to its use without their consent. The playbook will be used as carefully as possible, serving as a tool for segmentation and targeting.

Dr. Daviet: Let’s say you want to do a personalized drink, and you can identify key genetic traits such as caffeine metabolism, taste preference, health consciousness, and lifestyle without even having access to the data. You can see how these traits correlate in the genetic data and then tailor your product offering to different profiles and ask genetic companies to do personalized recommendations.

Dr. Nave: Some conditions, like having allergies to certain things or not being able to metabolize certain things, do have a strong genetic signal. Specific products, like lactose-free or alcohol-free versions, sometimes address these needs. There could be small segments that reveal these needs through genetic data.

Q: Beyond taste preferences, what other consumer behaviors have strong genetic components that non-food/health industries should pay attention to? Which industry do you think is missing the biggest genetic marketing opportunity right now?

Dr. Daviet: Behavioral genetics predicts everything to some extent. We can consider experiential services, as well as cultural services such as travel and entertainment. If you know someone’s ancestry background, you can tailor your marketing efforts to explore their cultural heritage. Based on genetics, someone might discover they have Latin American ancestry they didn’t know of and start exploring that. You can extend to pretty much anything—lifestyle, work.

Dr. Nave: Basically, everything is heritable except for the language you speak and the religion you practice. Even aspects such as your likelihood of divorce can be genetically influenced to some extent, as they correlate with specific genetic traits. There could be helpful signals everywhere. The question is when it’s stronger, when it’s not predictable from other data. That’s where genetics comes into play. Beauty and educational attainment have potential, but they’re not limited to these.

Q: Please paint us a picture: How do you see genetic marketing evolving over the next 10 years?

Dr. Daviet: Epigenetics might be easier. Epigenetics looks at how molecules attach to DNA and change gene expression, which evolves throughout life and provides a lot of additional information. Without the need for sampling one million people, and because it evolves over life, it’s more accurate. Currently, some companies are working in that field, and what’s trendy is biological age. Maybe you’re 25, but biologically, are you closer to 30 or 20? I could see an opportunity there because it’s more accessible, informative, and growing. Since it’s less complicated to gain insight from, it might be more sustainable on the business side, too.

Our conversation revealed that genetics work best for “hidden” preferences not shown in purchase data, and surprisingly, the authors noted that “basically everything is heritable except language and religion,” which opens up endless possibilities.

However, this raises critical questions: If genetic data can have such promising predictive power, where do we draw the ethical lines? For a comprehensive framework on the promise and perils of genetic marketing, read Dr. Daviet, Dr. Nave, and Dr. Wind’s essential guide, “Genetic Data: Potential Uses and Misuses in Marketing.”

References

Remi Daviet, Gideon Nave, and Jerry Wind (2021), “Genetic Data: Potential Uses and Misuses in Marketing,” Journal of Marketing, 86 (1), 7–26.

Remi Daviet and Gideon Nave (2024), “The Value of Genetic Data in Predicting Preferences: A Study of Food Taste,” Journal of Marketing Research, 61 (6), 1116–31.

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