Modern marketing increasingly requires managers to deploy new content at scale, often with limited opportunity for prior testing. As a result, decisions about what to launch become strategic managerial choices under uncertainty rather than purely creative exercises. While generative AI makes the creation of new content fast and highly scalable, it simultaneously expands the set of options managers must evaluate, making reliable content selection increasingly difficult. We develop a framework for causal prediction that enables managers to evaluate and deploy novel marketing content generated by AI. The framework uses pretrained large language models to represent previously deployed content and learn how its features causally relate to outcomes. Using a rejection-sampling procedure, the framework screens new content proposed by generative AI to avoid extrapolation beyond what historical data can reliably support. In a large-scale email marketing application (3.3 million observations across 34 campaigns), the framework improves out-of-sample prediction and real-world deployment performance relative to standard approaches, enabling outcome-guided generation of higher-performing AI-generated content. The framework establishes a threshold based on how closely new content resembles past campaigns, separating cases where causal prediction is reliable from cases where direct experimentation is warranted. The framework has important implications for marketing decision making in a rapidly evolving environment where generative AI is transforming content creation and deployment.
Consumers often behave opportunistically, taking more than what fair marketplace exchange warrants (e.g., taking excess samples, returning used items). While each individual transgression may be minor, cumulatively they can undermine firm profitability. Thus, addressing consumer opportunism is an important managerial concern. We identify a novel antecedent of consumer opportunism: consumers’ acceptance of societal hierarchy (i.e., power distance belief [PDB]), including firms’ higher position in it than consumers. Nine studies (plus two supplementary studies) employing archival, correlational, and experimental data provide converging evidence that, in marketplace interactions, this belief evokes a need to feel clever (i.e., to feel smart and knowledgeable vis-à-vis firms), which, in turn, promotes opportunistic behaviors. These behaviors exploit firm policy loopholes, allowing consumers to feel clever without explicitly defying firm authority. Higher-status consumers, who may be accustomed to a position of advantage and consequently less deferential, experience this need more, leading to greater opportunism. Reminders of surveillance reduce opportunism, but brand relationship reminders, paradoxically, license it. This research thus offers novel theoretical insights into consumer opportunism along with substantive managerial implications.
Sellers frequently make positive but vague claims about product quality—a practice known as “puffery” that typically enjoys legal protection. This research examines whether puffery influences consumer behavior in the context of Airbnb listings. The study exploits within-listing changes to claims made in hosts’ brief property descriptions and estimates their effects using a hazard model of booking timing. Adding a single puffed claim increases bookings by about .7 days per year (.2% above baseline); an exclamation point adds roughly 1.0 days per year. Puffery’s impact is statistically similar to objective claims (.5 days per year), though objective claims often duplicate information available through search filters, reducing their marginal impact; less redundant, weakly subjective claims add roughly 1.0 days per year. The study also evaluates whether puffery backfires by disappointing consumers. Results indicate limited backlash: adding a puffed claim reduces numerical ratings and review sentiment by no more than .03 standard deviations. Together, these findings question the legal assumption that consumers sufficiently discount puffed claims, but remain consistent with puffery’s continued legal protection by showing it does not reduce average consumer-reported satisfaction.
The authors estimate the heterogeneity of TV advertising effectiveness across store characteristics and advertising levels using a large-scale panel of 135 U.S. retail and restaurant brands, and then use these estimates to assess strategies for improving TV advertising performance. The findings show significant heterogeneity in TV advertising elasticity across characteristics for over 93% of brands, but also show that firms’ observed allocations generally fail to fully exploit this estimated heterogeneity and instead covary much more closely with simple heuristics. For example, firms tend to advertise in areas where they already have high revenue, rather than in the areas estimated to have the highest incremental revenue from advertising. In particular, brands tend to overinvest in dense, high-income markets and underinvest in markets with high concentrations of college-educated residents. The authors project that brands could improve ad lift by a median 2.35 percentage points (relative to <.5% median baseline ad lift) and earn tens of millions in additional revenue under identical-budget reallocations that better leverage this heterogeneity, and that such reallocations could increase the proportion of brands achieving positive return on investment from TV advertising by 14–16 percentage points.
The paper explores peer effects in user churn within a digital socially-connected platform and investigates how firms can leverage peer effects and network structure for customer relationship management (CRM). The data come from a massively-multiplayer online game, where gamer social ties are endogenous. We build a two-stage structural model to capture network formation and peer effects in churn. Unobserved gamer heterogeneity inferred from network formation corrects for endogeneity bias in peer effects. The main findings are: (i) Peer effects substantially amplify intervention effectiveness, with an average social multiplier of 2.48. Critically, the full model with unobserved gamer heterogeneity reveals substantial cross-network variation in social multipliers that the restricted model largely masks. (ii) Users vary far more in how they influence peers' retention than in their own likelihood of retention. (iii) User's network value is driven primarily by network centrality measures, unlike user's direct value which correlates more with individual characteristics.
Firms use privacy-sensitive data to make targeting decisions, which can inadvertently reveal the underlying information driving those decisions—a risk the authors term targeting privacy risk . The authors use differential privacy to quantify and control this risk. Although firms increasingly adopt differential privacy, policymakers offer limited guidance on its implementation in a targeting framework. The crucial question, therefore, becomes: where should firms implement differential privacy? The authors find that profitability critically depends on where differential privacy is implemented within a typical targeting workflow. This insight stems from two novel targeting strategies: one implements differential privacy during model training, the other at the targeting-decision stage. Using a large-scale field experiment involving 747,975 customers and extensive simulations, the authors show that both strategies remain profitable under strong privacy protection. Notably, the decision-stage strategy yields, on average, fivefold higher profits than alternative implementations. To generalize this finding, the authors derive the expected profit for a given privacy risk level and a privacy elasticity of targeting profits. Collectively, the results offer guidelines for firms and policymakers to ensure privacy protection and profitability.
Online content platforms monetize user engagement through advertising and share ad revenue with content creators to incentivize content provision. A central design decision for these platforms is the choice of ad intensity policy, which governs how advertising load is determined and shapes creator incentives, content quality, consumer consumption behaviors, and platform profitability. The authors analyze a model with one platform and two competing content creators to study three ad intensity policies: differentiated advertising (DA), uniform advertising (UA), and creator-set advertising. With symmetric creators and quality-independent marginal ad revenue, UA intensifies quality-based competition among creators and leads to higher revenue-sharing rate, content quality, advertising intensity, and platform profit than DA. Creators benefit more from UA than from DA when creator substitutability is low, but may prefer DA when substitutability is high. Compared with DA, creator-set advertising weakens incentives for content investment, resulting in lower content quality, lower ad intensity, and reduced platform profit; its effects on creators and consumers depend on the degree of creator substitutability. Extensions show that relaxing the benchmark assumptions—such as allowing for creator asymmetry or quality-dependent marginal ad revenue—can overturn UA's advantage and make DA more profitable, highlighting that no single ad intensity policy is universally optimal.
Brand logos are key visual elements that shape consumers’ perceptions and behaviors. This research advances the logo literature by systematically examining an understudied design feature: three-dimensionality. Three-dimensional (3D) logos incorporate visual depth cues such as shading, perspective, and occlusion, which can make them appear more like tangible objects than two-dimensional (2D) logos. Across multiple field, lab, and online studies, the authors demonstrate that, relative to their 2D counterparts, 3D logos can increase brand purchase likelihood. This effect arises because depth cues in 3D logos evoke mental representations of tangible, physical forms, thereby increasing perceived logo realism and, in turn, enhancing brand trust. Importantly, these benefits are not universal but conditional. The positive effects of 3D logos emerge when logos appear in visually simple environments or when offerings are perceived as more concrete (i.e., higher in tangible dominance). Conversely, in visually complex environments or for abstract offerings with limited physical contact, 3D logos provide little or no advantage over 2D designs. By identifying when depth cues improve consumer responses, this research offers a nuanced theoretical account of logo three-dimensionality and actionable guidance for managers considering 3D logo adoption.
Overproduction and overconsumption represent key issues in the fight against resource waste and environmental degradation. Whereas the scientific debate has mainly focused on how to increase product durability by improving tangible aspects (e.g., technical characteristics, materials used, production processes), this article focuses on a consumer-based view of durability, examining how long consumers plan to use a product for, a construct labeled length of product usage (LPU). The core thesis is that LPU might depend not only on tangible product characteristics but also on intangible product characteristics, such as creativity, defined in terms of high novelty and adequate appropriateness. In advancing this creativity-based LPU account, the authors argue that higher product creativity leads to higher LPU by strengthening consumers’ emotional attachment to products. Seven preregistered studies, employing different products, populations, and creativity manipulations, empirically support the proposed framework. The results also show that the effect of creativity on LPU, via emotional attachment, is especially pronounced for nonowners versus owners and for mass-market versus luxury products. By emphasizing that creativity can be key to fostering sustainable consumption, this research advances the literature on the antecedents of LPU and product durability, offers implications for companies, and provides avenues for future research.
Consumers rely heavily on product reviews in their purchasing and consumption decisions, and how reviews influence product sales is well-documented. This research asks whether reviews might have an afterlife beyond purchase—that is, whether they can affect the product experience itself. Across five studies involving real consumer-generated reviews and product interaction, this research demonstrates that consumers who read reviews before consumption have different experiences with a product than consumers who experience the same product without reading reviews. Specifically, consumers who are exposed to negative reviews before they consume a product experience the reviewed product more negatively than consumers who have not read any reviews. Reading positive reviews, however, does not significantly affect the consumption experience. This asymmetrical assimilative effect arises because negative reviews increase the salience of negative attributes of the reviewed product during consumption, making those attributes more influential during product experience. The effect is robust across various contexts, although forewarning consumers and prompting them to form independent opinions can reduce it. These findings open the door for investigation into the broader influence of reviews on consumption, beyond the point of purchase, and have important practical implications for marketers seeking to navigate this influence.
User-generated photos play an increasingly important role in online reviews, yet little is known about the process by which they are initially created. This paper develops a theory of image creation arguing that reviewers invest more creative effort in photographing products/services they feel positively (vs. negatively) about. This greater effort results in higher-quality photos that observers find to be more helpful, both because they are easier to visually process and because they engender greater trust in the reviewer. Results across six studies—including controlled experiments and an observational laboratory study (N = 4,218) as well as field analyses of Amazon and Yelp reviews (N = 669,937)—lend support to these predictions. By identifying valence as a driver of creative effort in user-generated photography, the findings advance knowledge on how visual content is produced in online marketplaces and how such content is evaluated by consumers.
The ability to quickly capture and adapt to customer preferences is central for firms seeking to offer personalized products and improve retention. This objective becomes challenging when individual-level data on customer interactions are limited, as is often the case for new customers or short consumption sessions. To this end, we propose meta-temporal processes (MetaTP), a meta-learning framework that enables scalable personalization from a small number of individual observations. MetaTP is trained across a large collection of session-based tasks, allowing it to improve data efficiency and transfer shared structure across customers. To model customer interactions over time, MetaTP integrates a Transformer-based architecture that captures sequential consumption patterns within sessions. This design uncovers dynamic preference heterogeneity and enables accurate predictions. We illustrate MetaTP through an application on customer sequential consumption of digital products, focusing on the lukewarm stage of the customer journey, a transition period characterized by limited individual observations. Empirically, MetaTP outperforms a comprehensive set of benchmark methods in few-shot prediction and reveals meaningful patterns of preference evolution through its interpretable parameters. Managerially, we demonstrate how firms can leverage MetaTP to optimize personalized recommendations with limited individual data, including product sequencing decisions and both open-loop and closed-loop session completion strategies.
Consumers often resist marketing information about death-related products and services (DRPS), let alone purchase them. Yet, there is a massive market and universal necessity for DRPS offerings like life insurance and funeral services. Proactively considering DRPS in advance allows consumers to better prepare for death and its aftermath. The current research builds on terror management theory and the literature on language use and psychological distance to propose a language-based sales communication strategy for DRPS. The findings from eight studies, including two field experiments and an eye-tracking study, indicate that DRPS sales messages appear in consumers’ second (vs. native) language (e.g., using muerte , Spanish for death, when talking to a native English speaker whose second language is Spanish) decreases consumers’ fear of death by creating greater psychological distance to death, which in turn induces more consumption (e.g., actual purchases). However, this effect becomes attenuated when consumers perceive high control over death or when the DRPS feature transcendence after death. In contrast, the effect is amplified for DRPS that require an intermediate level of customer participation. These findings offer novel insights into how marketers and policymakers can motivate consumers to consider DRPS earlier and engage in more proactive decision-making.
Mobile applications in the personal development sector increasingly integrate goal-enabling technology features (GETFs), which allow users to define a service-related end goal, set implementation strategies through subgoals, and monitor progress. Little is known, however, about how the difficulty of goals chosen during GETF adoption affects subsequent app behaviors. This study examines whether customers who set more versus less difficult end goals and subgoals show greater engagement and retention, and whether firms can nudge customers toward goal-difficulty levels conducive to sustained engagement. Using behavioral data from an investment app that introduced GETFs, the authors employ hierarchical modeling with staggered synthetic control and instrumental variable regression to address self-selection and endogeneity. Results reveal substantial heterogeneity: Many adopters show no or negative engagement changes, whereas those selecting moderately challenging goals and subgoals significantly increase in-app investment actions, though not sign-ins. Higher engagement postadoption predicts improved retention after one year. A field experiment confirms that subgoal difficulty causally drives in-app actions. These findings suggest that personalized guidance during GETF adoption can enhance sustained engagement. Marketing managers are advised to tailor goal-setting features to individual needs, providing expert-like support. This research provides novel empirical evidence on goal-difficulty levels that most effectively promote app engagement.
Figure-ground reversal (FGR) transcends visual conventions by reversing the roles of figure and ground in brand logo designs. In this research, the authors study how FGR logos affect consumers’ brand attitudes. Using traditional self-reported measures as well as biometric technology, they illuminate the unique nature of FGR’s underlying mechanism and identify moderators to shed additional light on that process. Specifically, they find that the positive effect of FGR logos on brand attitude is mediated by engagement and aesthetic appeal, and moderated by the visual identification and semantic interpretability of FGR objects. Across a multi-method investigation that includes live bidding, incentive-compatible willingness-to-pay, eye-tracking, and multiple boundary condition experiments, the authors provide empirical support for these effects and reveal the underlying mechanism. They conclude by discussing the contributions of the research to the literature on visual marketing phenomena and the implications of the findings for better visual branding in the marketplace.
This research examines whether functional magnetic resonance imaging (fMRI) data add predictive value beyond traditional market and survey data in forecasting two critical outcomes: (1) store manager adoption and (2) consumer sales of consumer packaged goods. Using data from a large retail chain, this study combines observable market variables, survey-based attitudes from a large representative consumer sample, and fMRI signals from a smaller convenience sample. Applying decision tree and least absolute shrinkage and selection operator (LASSO) regression approaches, the authors find that fMRI data enhance sales forecasts—particularly for more innovative products—while survey measures better predict store manager adoption. The research also quantifies the economic value of these improvements relative to data-acquisition costs, providing a framework for evaluating the return on investment of neuroforecasting tools. These findings clarify when neural measures add the most value over conventional analytics — particularly for innovative products at the consumer sales stage — with implications for product launch strategies and data investment decisions.
Risk absorption, where one party assumes risk to support a partner, is common in business-to-business (B2B) relationships but remains underexplored both as a form of relationship marketing investment and in its economic consequences. This study investigates risk absorption in the indirect car loan market, where third-party lenders approve loans for high-risk consumers, enabling auto dealers to close sales that might otherwise fall through. Using a three-year dataset from a loan supplier working with 1,550 dealers, the authors examine both the direct costs of risk absorption (e.g., delinquency payments) and the behavioral responses of dealers. They find that dealers often reciprocate by referring more loans after receiving risk absorption, but these referrals tend to carry higher risk, suggesting opportunistic behavior. Dealer responses are heterogeneous: Those with higher operational risk exhibit both stronger reciprocity and exploitation. Moreover, reciprocal behavior grows stronger early in the relationship and fades over time, whereas exploitative tendencies do not vary with relationship length. These findings provide new insight into how risk absorption unfolds across varying dealer profiles in B2B contexts.
Despite the ubiquity of ingredient quantity information in the marketplace, prior literature has yet to examine whether ingredient quantity shapes consumer choice. This research presents and tests a novel framework that charts when, why, and how this pervasive ingredient quantity information influences consumers’ food decisions. The findings from two preregistered pilot studies, seven preregistered experiments, and ten supplementary experiments in the Web Appendix indicate that consumers are often more interested in food products framed as containing few (vs. many) ingredients, even when the same ingredient list is displayed across products. This preference stems from the perception that fewer ingredients indicate less processing, especially when a product’s processing history is unavailable. As a result, a product with fewer ingredients is perceived as more natural and is thus preferred. Further, the studies also show that although consumers commonly pursue the goal to consume natural products, when other consumption goals (e.g., the goal to seek indulgent or unique products) rise in importance, a product framed as containing more ingredients can become more preferred. This work uncovers how ingredient quantity information biases consumers’ perceptions and daily food product decisions, and it provides easily implementable guidance for marketers seeking to increase consumers’ purchase likelihood.
Communication is a key aspect of the joint decision-making process, yet the field lacks an understanding of how people talk to each other while making joint decisions. In this article, the authors analyzed nearly 200 joint decision conversations from shop-along observations. They found that joint decision conversations are composed of four distinct communication patterns, which characterize how partners talk to each other: (1) coordination (including inquiry and disclosure), (2) contrast (including persuasion and devil's advocate), (3) build, and (4) one-sided. The authors then used these communication patterns as the building blocks of joint decision conversations to quantitatively model how they dynamically flow as partners shop together, finding that decision partners navigate the decision life cycle nonlinearly and communication pattern usage affects immediate satisfaction outcomes. The findings enable connections to be drawn across the splintered literatures on dyadic communication. The authors develop a taxonomy that reflects an integrated, cross-disciplinary phenomenological understanding of each communication pattern to facilitate interdisciplinary research. Theoretical advancements and practical implications are discussed, as are areas for future research.
A large body of research shows that even when information is accessible, consumers often fail to attend to it. To what extent and under what conditions can firms profit from such consumer inattention? The authors study this question theoretically and empirically in the used car market, focusing on the widely documented left-digit bias. Theoretically, firms can profit from targeting the most inattentive consumers even when there is an active decentralized market for used goods trading. Leveraging a detailed dataset of millions of automobile transactions from a seven-year period, the authors find that consumers exhibit inattention in the form of left-digit bias to the odometer, and such inattention is estimated to be significantly more for consumers who buy from firms. Compared with private sellers, car dealerships transact with ex post significantly more left-digit-biased consumers. Dealerships sell more vehicles with odometer readings below round numbers, sell them faster, and extract higher margins from these vehicles. The results imply that intermediaries can "skim" consumers with specific behavioral biases and extract meaningful surplus from selling to them.
