Variety Effects in Mobile Advertising

Advertising as a Reminder: Evidence from the Dutch State Lottery

We show that advertising can act as a reminder for consumers who intend to buy a product. , Consumers who intend to buy a product may forget to do so because they suffer from limited attention. Therefore, they may value being reminded by an advertisement. This reminder effect of advertising could be important in many markets but is usually difficult to document. We study it in the context of buying a product that has existed for almost 300 years: a ticket for the Dutch State Lottery. This context is particularly suitable for our analysis because the product is simple, it is very well known, and there are multiple fixed and known purchase cycles per year. Moreover, radio and TV advertisements are designed explicitly to remind consumers to buy a lottery ticket before the draw. This can conveniently be done online. We develop an approach to distinguish reminder effects of advertising from other effects, such as conveying information about the size of the jackpot. The key idea is that reminder effects are short lived. We use minute-level advertising and online sales data and find that the reminder effect of advertising is strong. Reaching 1% of the population by a radio advertisement leads to an increase in online sales of 1.55% in the four hours after the advertisement is aired. For TV advertisements, the increase is 0.78%. We show that the effects generally last longer for radio advertisements. We also provide direct evidence that reminding consumers not only affects the timing of purchases but also leads to market expansion. Finally, we estimate a model of consumer behavior under limited attention to quantify the effect on total sales. We find that total sales would be 16.7% lower without the reminder effect of advertising and that shifting advertising to the week of the draw would lead to a 9.2% increase in sales. History: Puneet Manchanda served as the senior editor and Günter Hitsch served as associate editor for this article. Supplemental Material: A replication package with code and log files and an Online Appendix are available at https://doi.org/10.1287/mksc.2022.1405 .

How Targeting Affects Customer Search: A Field Experiment

It has become common practice for retailers to personalize direct marketing efforts based on customer transaction histories as a tactic to increase sales. Targeted email offers featuring products in the same category as a customer’s previous purchases generate higher purchase rates. However, a targeted offer emphasizing familiar products could result in curtailed search for unadvertised products, as a closely matched offer weakens a customer’s incentives to search beyond the targeted items. In a field experiment using email offers sent by an online wine retailer, targeted offers resulted in decreased search activity on the retailer’s website. This effect is driven by a lower rate of search by customers who visit the site, rather than a lower incidence of search. There are several ways this could potentially hurt retailers and consumers, such as reduced cross-selling and fewer opportunities for customers to explore new products. This paper was accepted by Pradeep Chintagunta, marketing.

Cognitive Load and Social Media Advertising

Social media engagement requires cognitive resources, which subsequently impact the advertisements consumers see while browsing. For the most part, however, advertising practitioners and scholars s...

Privacy vs. Profit: The Impact of Google's Manifest Version 3 (MV3) Update on Ad Blocker Effectiveness

Google's recent update to the manifest file for Chrome browser extensions, transitioning from manifest version 2 (MV2) to manifest version 3 (MV3), has raised concerns among users and ad blocker providers, who worry that the new restrictions, notably the shift from the powerful WebRequest API to the more restrictive DeclarativeNetRequest API, might reduce ad blocker effectiveness. Because ad blockers play a vital role for millions of users seeking a more private and ad-free browsing experience, this study empirically investigates how the MV3 update affects their ability to block ads and trackers. Through a browser-based experiment conducted across multiple samples of ad-supported websites, we compare the MV3 to MV2 instances of four widely used ad blockers. Our results reveal no statistically significant reduction in ad-blocking or anti-tracking effectiveness for MV3 ad blockers compared to their MV2 counterparts, and in some cases, MV3 instances even exhibit slight improvements in blocking trackers. These findings are reassuring for users, indicating that the MV3 instances of popular ad blockers continue to provide effective protection against intrusive ads and privacy-infringing trackers. While some uncertainties remain, ad blocker providers appear to have successfully navigated the MV3 update, finding solutions that maintain the core functionality of their extensions.

Commercial Persuasion in AI-Mediated Conversations

As Large Language Models (LLMs) become a primary interface between users and the web, companies face growing economic incentives to embed commercial influence into AI-mediated conversations. We present two preregistered experiments (N = 2,012) in which participants selected a book to receive from a large eBook catalog using either a traditional search engine or a conversational LLM agent powered by one of five frontier models. Unbeknownst to participants, a fifth of all products were randomly designated as sponsored and promoted in different ways. We find that LLM-driven persuasion nearly triples the rate at which users select sponsored products compared to traditional search placement (61.2% vs. 22.4%), while the vast majority of participants fail to detect any promotional steering. Explicit "Sponsored" labels do not significantly reduce persuasion, and instructing the model to conceal its intent makes its influence nearly invisible (detection accuracy < 10%). Altogether, our results indicate that conversational AI can covertly redirect consumer choices at scale, and that existing transparency mechanisms may be insufficient to protect users.

Targeted Promotions on an E-Book Platform: Crowding Out, Heterogeneity, and Opportunity Costs

Targeted promotions based on individual purchase history can increase sales. However, the opportunity costs of targeting to optimize promoted product sales are poorly understood. A series of randomized field experiments with a large e-book platform shows that although targeted promotions increase promoted product sales and purchases of similar products, they can crowd out purchases of dissimilar products (i.e., e-books from nontargeted genres) by decreasing search activities of nontargeted goods on the same platform. The effects on total sales are heterogeneous, ranging from net decreases to insignificant drops, motivating a targeting exercise comparing strategies that optimize promoted product sales versus total sales. Targeting for promoted product sales tends to assign promotions to customers who purchased similar products, whereas targeting for total sales assigns promotions on the basis of other user characteristics. Targeting for promoted product sales generated incremental total sales that amounted to approximately 29% of the optimal incremental total sales when targeting for total sales (an opportunity cost of 71%). The optimal targeting exercise highlights how maximizing promotional lift can incur opportunity costs in terms of other forgone sales.

Accelerating dynamics of collective attention

With news pushed to smart phones in real time and social media reactions spreading across the globe in seconds, the public discussion can appear accelerated and temporally fragmented. In longitudinal datasets across various domains, covering multiple decades, we find increasing gradients and shortened periods in the trajectories of how cultural items receive collective attention. Is this the inevitable conclusion of the way information is disseminated and consumed? Our findings support this hypothesis. Using a simple mathematical model of topics competing for finite collective attention, we are able to explain the empirical data remarkably well. Our modeling suggests that the accelerating ups and downs of popular content are driven by increasing production and consumption of content, resulting in a more rapid exhaustion of limited attention resources. In the interplay with competition for novelty, this causes growing turnover rates and individual topics receiving shorter intervals of collective attention.

AIDA model | Marketing | Research Starters | EBSCO Research

<p>The AIDA model is a marketing framework that outlines the stages consumers typically go through when making a purchasing decision: Attention, Interest, Desire, and Action. Developed in the late 19th century by American marketer Elias St. Elmo Lewis and later refined by Edward Strong in the 1920s, this model aims to guide marketers in capturing potential buyers&#039; attention and leading them toward a purchase. </p> <p>In the Attention stage, marketers employ engaging strategies to attract consumers, often using eye-catching visuals or intriguing information. Next, the Interest stage focuses on maintaining that attention through memorable content or relatable messaging. The Desire stage demonstrates how a product or service fulfills the consumer&#039;s needs, often using persuasive techniques like testimonials or demonstrations. Finally, the Action stage prompts the consumer to make a purchase, providing clear instructions on how to proceed.</p> <p>While the AIDA model remains relevant, marketers today may adapt it to incorporate new elements, such as Retention or Satisfaction, reflecting the evolving digital landscape and consumer behavior. Overall, the AIDA model serves as a foundational tool for understanding consumer engagement and facilitating effective marketing strategies.</p>

What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce

Online marketplaces will be transformed by autonomous AI agents acting on behalf of consumers. Rather than humans browsing and clicking, AI agents can parse webpages or leverage APIs to view, evaluate and choose products. We investigate the behavior of AI agents using ACES, a provider-agnostic framework for auditing agent decision-making. We reveal that agents can exhibit choice homogeneity, often concentrating demand on a few ``modal'' products while ignoring others entirely. Yet, these preferences are unstable: model updates can drastically reshuffle market shares. Furthermore, randomized trials show that while agents have improved over time on simple tasks with a clearly identified best choice, they exhibit strong position biases -- varying across providers and model versions, and persisting even in text-only "headless" interfaces -- undermining any universal notion of a ``top'' rank. Agents also consistently penalize sponsored tags while rewarding platform endorsements, and sensitivities to price, ratings, and reviews vary sharply across models. Finally, we demonstrate that sellers can respond: a seller-side agent making simple, query-conditional description tweaks can drive significant gains in market share. These findings reveal that agentic markets are volatile and fundamentally different from human-centric commerce, highlighting the need for continuous auditing and raising questions for platform design, seller strategy and regulation.

The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

Online platforms have a wealth of data, run countless experiments, and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is that incentives are misaligned: platforms are not optimizing for user happiness. We suggest the problem runs deeper, transcending the specific incentives of any particular platform, and instead stems from a mistaken foundational assumption. To understand what users want, platforms look at what users do. This is a kind of revealed-preference assumption that is ubiquitous in the way user models are built. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want. The behavioral economics and psychology literatures suggest, for example, that we can choose mindlessly or that we can be too myopic in our choices, behaviors that feel entirely familiar on online platforms. In this work, we develop a model of media consumption where users have inconsistent preferences. We consider a platform which wants to maximize user utility, but only observes behavioral data in the form of the user’s engagement. We show how our model of users’ preference inconsistencies produces phenomena that are familiar from everyday experience but difficult to capture in traditional user interaction models. These phenomena include users who have long sessions on a platform but derive very little utility from it, and platform changes that steadily raise user engagement before abruptly causing users to go “cold turkey” and quit. A key ingredient in our model is a formulation for how platforms determine what to show users: they optimize over a large set of potential content (the content manifold) parametrized by underlying features of the content. Whether improving engagement improves user welfare depends on the direction of movement in the content manifold: For certain directions of change, increasing engagement makes users less happy, whereas in other directions on the same manifold, increasing engagement makes users happier. We provide a characterization of the structure of content manifolds for which increasing engagement fails to increase user utility. By linking these effects to abstractions of platform design choices, our model thus creates a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by the Vannevar Bush Faculty Fellowship and Multidisciplinary University Research Initiative [Grant W911NF-19-0217]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.03683 .

In-Store Spending Dynamics: How Budgets Invert Relative-Spending Patterns

Abstract The authors conduct four controlled lab experiments and one field study in a brick-and-mortar grocery store to demonstrate that relative spending—the price of the purchased item relative to the mean price of the product category—evolves nonlinearly and distinctly for budget and nonbudget shoppers. While the relative spending of budget shoppers evolves in a concave manner, the relative spending of nonbudget shoppers evolves inversely in a convex manner. Thus, budget (nonbudget) shoppers spend relatively more (less) in the middle than at the beginning and toward the end of their shopping trip. Mediation analyses confirm that the pain of paying experienced while shopping drives price salience, which then drives relative spending. Moreover, manipulating shoppers’ pain of paying, by altering the opportunity costs associated with their spending or drawing shoppers’ attention to their spending via real-time spending feedback, is shown to influence these spending patterns. The research offers theoretical contributions to the in-store decision-making, budgeting, and pain-of-paying literature and has important implications for marketing and promotion strategies in retail and mobile technology environments, as it suggests when a shopper may be more sensitive to price-related factors.

Demand characteristics in human–computer experiments

Demand characteristics refer to cues that can inform participants in experiments about the hypothesis and influence their behavior. They lead researchers to erroneously infer non-existing effects, undermining the experimental integrity of empirical studies. Despite a widespread acknowledgment of their confounding influence in experimental psychology, experiments involving humans and computers to a lesser extent consider effects of demand characteristics, as computerized protocols are thought to be immune to some experimenter biases. Furthermore, demand characteristics are considered to mainly effect subjective measures. As a result, demand characteristics often remain uncontrolled in studies involving computers, and in particular for objective measures such as performance. In this paper, we present two experiments that underline the importance of demand characteristics in human–computer interaction experiments. In a text-entry study, we made participants believe they were evaluating a research-based keyboard. This belief led to increased performance and self-reported user experience. In a second study, we conducted a thought experiment on the illusion of body ownership in virtual reality, where the experimental design indicated the study hypothesis. We found hypothesis-compliant responses from participants, even when they did not experience the illusion. We conclude that demand characteristics pose a significant challenge to the interpretation and validity of human–computer experiments, even when they are fully automated. We discuss the implications and offer guidelines to mitigate effects of demand characteristics.

“You Will:” A Macroeconomic Analysis of Digital Advertising

Abstract. An information-based model is developed where traditional and digital advertising finance the provision of free media goods and affect price comp

Save More Today or Tomorrow: The Role of Urgency in Precommitment Design

To encourage farsighted behaviors, previous research suggests that marketers should invite consumers to precommit to adopting these behaviors “later.” However, the authors propose that people will draw different inferences from different types of precommitment offers, and that these inferences can help explain when precommitment is (and is not) effective at increasing adoption of farsighted behaviors. Specifically, the authors theorize that simultaneously offering consumers the opportunity to adopt a farsighted behavior now or later (i.e., offering “simultaneous precommitment”) may signal that the behavior is not urgently recommended; however, offering consumers the opportunity to adopt that behavior immediately and then, only if they decline, inviting them to adopt it later (i.e., offering “sequential precommitment”) may signal just the opposite. In a multisite field experiment (N = 5,196), the authors find that simultaneously giving consumers the chance to increase their savings now or later reduced retirement savings. Two preregistered lab studies (N = 5,080) show that simultaneous precommitment leads people to infer that taking action is not urgently recommended, and such inferences predict less adoption of recommended behaviors. Importantly, offering sequential precommitment increases inferred urgency, predicting greater adoption. Together, this research advances knowledge about the limits and potential of precommitment.

What's Advertising Content Worth? Evidence from a Consumer Credit Marketing Field Experiment^*

Abstract. Firms spend billions of dollars developing advertising content, yet there is little field evidence on how much or how it affects demand. We analy

Are Large Language Models Sensitive to the Motives Behind Communication?

Human communication is $\textit{motivated}$: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs) and AI agents process is inherently framed by humans' intentions and incentives. People are adept at navigating such nuanced information: we routinely identify benevolent or self-serving motives in order to decide what statements to trust. For LLMs to be effective in the real world, they too must critically evaluate content by factoring in the motivations of the source---for instance, weighing the credibility of claims made in a sales pitch. In this paper, we undertake a comprehensive study of whether LLMs have this capacity for $\textit{motivational vigilance}$. We first employ controlled experiments from cognitive science to verify that LLMs' behavior is consistent with rational models of learning from motivated testimony, and find they successfully discount information from biased sources in a human-like manner. We then extend our evaluation to sponsored online adverts, a more naturalistic reflection of LLM agents' information ecosystems. In these settings, we find that LLMs' inferences do not track the rational models' predictions nearly as closely---partly due to additional information that distracts them from vigilance-relevant considerations. However, a simple steering intervention that boosts the salience of intentions and incentives substantially increases the correspondence between LLMs and the rational model. These results suggest that LLMs possess a basic sensitivity to the motivations of others, but generalizing to novel real-world settings will require further improvements to these models.