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

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.

Variety Effects in Mobile Advertising

Mobile app users are often exposed to a sequence of short-lived marketing interventions (e.g., ads) within each usage session. This study examines how an increase in the variety of ads shown in a session affects a user's response to the next ad. The authors leverage the quasi-experimental variation in ad assignment in their data and propose an empirical framework that accounts for different types of confounding to isolate the effects of a unit increase in variety. Across a series of models, the authors consistently show that an increase in ad variety in a session results in a higher response rate to the next ad: holding all else fixed, a unit increase in variety of the prior sequence of ads can increase the click-through rate on the next ad by approximately 13%. The authors then explore the underlying mechanism and document empirical evidence for an attention-based account. The article offers important managerial implications by identifying a source of interdependence across ad exposures that is often ignored in the design of advertising auctions. Furthermore, the attention-based mechanism suggests that platforms can incorporate real-time attention measures to help advertisers with targeting dynamics.

Double Mental Discounting: When a Single Price Promotion Feels Twice as Nice

This research finds that when a single gain has strong associations with multiple costs, consumers often mentally deduct that gain from perceived costs multiple times. For example, with some price promotions (e.g., spend $200 now and receive a $50 gift card to spend in the future), consumers mentally deduct the value of the price promotion from the cost of the first purchase when they receive the promotion, as well as from the cost of the second purchase when they use the promotion. Multiple mental deductions based on a single gain result in consumers' perceptions that their costs are lower than they actually are, which can trigger higher expenditures. This mental accounting phenomenon, referred to as “double mental discounting,” is driven by the extent to which gains feel associated, or coupled, with multiple purchases. This article also documents methods to decouple promotional gains from purchases, thus mitigating double mental discounting.

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.

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.

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 .

Small Probabilistic Discounts Stimulate Spending: Pain of Paying in Price Promotions

AbstractWe find that small probabilistic price promotions effectively stimulate demand, even more so than comparable fixed price promotions (e.g., “1% chance it’s free” vs. “1% off,” respectively), because they more effectively reduce the pain of paying. In three field experiments at a grocer, we exogenously and endogenously manipulated the salience of pain of paying via elicitation timing (e.g., at entrance or checkout) and payment method (i.e., cash/debit cards or credit cards). This modulated the attractiveness of probabilistic discounts and their ability to stimulate spending. Shoppers paying with cash or debit cards, for example, spent 54% more if they received a 1% probabilistic discount than a 1% fixed discount (experiment 2). A fourth experiment showed that consumers’ sensitivity to pain of paying modulates the greater comparative efficacy of small probabilistic than fixed discounts. More broadly, the results elucidate a novel affective route through which price promotions stimulate demand––pain of paying.

Buy wisely

Whenever I buy things I try to prioritize cost per use. Sometimes I consider other priorities such as cost per smile, cost per thrill, cost per externality, ...

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>

The Interplay among Category Characteristics, Customer Characteristics, and Customer Activities on in-Store Decision Making

The authors explore product category and customer characteristics that affect consumers’ likelihood of engaging in unplanned purchases. In addition, they examine consumer activities that can exacerbate or limit these effects. The authors employ a hierarchical modeling approach to test their hypotheses using a data set of in-store intercept interviews conducted with 2300 consumers across 28 stores. The results show that category characteristics, such as purchase frequency and displays, and customer characteristics, such as household size and gender, affect in-store decision making. Moreover, although the analysis reveals that the baseline probability of an unplanned purchase is 46%, the contextual factors can drive this probability as high as 93%. The results support the predictions that list use, more frequent trips, limiting the aisles visited, limiting time spent in the store, and paying by cash are effective strategies for decreasing the likelihood of making unplanned purchases.

Prospect Theory, Mental Accounting, and Differences in Aggregated and Segregated Evaluation of Lottery Portfolios

If individuals have to evaluate a sequence of lotteries, their judgment is influenced by the presentation mode. Experimental studies have found significantly higher acceptance rates for a sequence of lotteries if the overall distribution was displayed instead of the set of lotteries itself. Mental accounting and loss aversion provide an easy and intuitive explanation for this phenomenon. In this paper we offer an explanation that incorporates further evaluation concepts of Prospect Theory. Our formal analysis of the difference in aggregated and segregated portfolio evaluation demonstrates that the higher attractiveness of the aggregated presentation mode is not a general phenomenon (as suggested in the literature) but depends on specific parameters of the lotteries. The theoretical findings are supported by an experimental study. In contrast to the existing evidence and in line with our theoretical results, we find for specific types of lotteries an even lower acceptance rate if the overall distribution is displayed.

The Exception Is the Rule: Underestimating and Overspending on Exceptional Expenses

Abstract Purchases fall along a continuum from ordinary (common or frequent) to exceptional (unusual or infrequent), with many of the largest expenses (e.g., electronics, celebrations) being the most exceptional. Across seven studies, we show that, while people are fairly adept at budgeting and predicting how much they will spend on ordinary items, they both underestimate their spending on exceptional purchases overall and overspend on each individual purchase. Based on the principles of mental accounting and choice bracketing, we show that this discrepancy arises in part because consumers categorize exceptional expenses too narrowly, construing each as a unique occurrence and consequently overspending across a series of discretely exceptional expenses. We conclude by proposing an intervention that diminishes this tendency by helping consumers consider their spending on exceptional items as part of a larger set of purchases.

Mental accounting of product returns

Abstract Product returns incur a substantial financial loss for retailers. We demonstrate how, when, and why cross‐selling during the product returns process can reduce this loss in revenue. We find consumers more readily spend money refunded from product returns than unspent money. We theorize that this refund effect occurs because consumers psychologically realize the loss of money when purchasing products and earmark that money for spending. Thus, consumers feel a smaller psychological loss when spending refunded money than unspent money on a subsequent purchase. In six experiments, we find consumers spend refunded money more freely than unspent money, even more than windfall gains like lottery winnings, on products in similar and different product categories (e.g., groceries vs. apparel). However, the refund effect only holds when consumers do not expect to return products at the point of purchase and before refunded money is commingled with money in other accounts. Our findings identify a new fungibility violation due to mental accounting (i.e., a new source effect), and illustrate its value for generating, validating, and explaining revenue retention strategies.

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.

Is Google Getting Worse? A Longitudinal Investigation of SEO Spam in Search Engines

Many users of web search engines have been complaining in recent years about the supposedly decreasing quality of search results. This is often attributed to an increasing amount of search-engine-optimized but low-quality content. Evidence for this has always been anecdotal, yet it’s not unreasonable to think that popular online marketing strategies such as affiliate marketing incentivize the mass production of such content to maximize clicks. Since neither this complaint nor affiliate marketing as such have received much attention from the IR community, we hereby lay the groundwork by conducting an in-depth exploratory study of how affiliate content affects today’s search engines. We monitored Google, Bing and DuckDuckGo for a year on 7,392 product review queries. Our findings suggest that all search engines have significant problems with highly optimized (affiliate) content—more than is representative for the entire web according to a baseline retrieval system on the ClueWeb22. Focussing on the product review genre, we find that only a small portion of product reviews on the web uses affiliate marketing, but the majority of all search results do. Of all affiliate networks, Amazon Associates is by far the most popular. We further observe an inverse relationship between affiliate marketing use and content complexity, and that all search engines fall victim to large-scale affiliate link spam campaigns. However, we also notice that the line between benign content and spam in the form of content and link farms becomes increasingly blurry—a situation that will surely worsen in the wake of generative AI. We conclude that dynamic adversarial spam in the form of low-quality, mass-produced commercial content deserves more attention. (Code and data: https://github.com/webis-de/ECIR-24).

Is Google Getting Worse? A Longitudinal Investigation of SEO Spam in Search Engines

Many users of web search engines have been complaining in recent years about the supposedly decreasing quality of search results. This is often attributed to an increasing amount of search-engine-optimized but low-quality content. Evidence for this has always been anecdotal, yet it’s not unreasonable to think that popular online marketing strategies such as affiliate marketing incentivize the mass production of such content to maximize clicks. Since neither this complaint nor affiliate marketing as such have received much attention from the IR community, we hereby lay the groundwork by conducting an in-depth exploratory study of how affiliate content affects today’s search engines. We monitored Google, Bing and DuckDuckGo for a year on 7,392 product review queries. Our findings suggest that all search engines have significant problems with highly optimized (affiliate) content—more than is representative for the entire web according to a baseline retrieval system on the ClueWeb22. Focussing on the product review genre, we find that only a small portion of product reviews on the web uses affiliate marketing, but the majority of all search results do. Of all affiliate networks, Amazon Associates is by far the most popular. We further observe an inverse relationship between affiliate marketing use and content complexity, and that all search engines fall victim to large-scale affiliate link spam campaigns. However, we also notice that the line between benign content and spam in the form of content and link farms becomes increasingly blurry—a situation that will surely worsen in the wake of generative AI. We conclude that dynamic adversarial spam in the form of low-quality, mass-produced commercial content deserves more attention. (Code and data: https://github.com/webis-de/ECIR-24).