Field Experimentation in Marketing Research

How Field Experiments in Economics Can Complement Psychological Research on Judgment Biases

This review summarizes results of field experiments examining individual behaviors across several market settings—from open-air markets to rideshare markets to tax-compliance markets—where people sort themselves into market roles wherein they make consequential decisions. Using three distinct examples from my own research on the endowment effect, left-digit bias, and omission bias, I showcase how field experiments can help researchers understand mediators, heterogeneity, and causal moderation involved in judgment biases in the field. In this manner, the review highlights that economic field experiments can serve an invaluable intellectual role alongside traditional laboratory research.

Field Studies

Field research is conducted in the user’s natural setting. Learn the unexpected by leaving the office and observing people in their normal environments.

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.

Uniting the Tribes: Using Text for Marketing Insight

Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But how can marketers best use such data? This article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient. Next, they discuss how text can be a powerful tool both for prediction and for understanding (i.e., insights). Then, the authors overview methodologies and metrics used in text analysis, providing a set of guidelines and procedures. Finally, they further highlight some common metrics and challenges and discuss how researchers can address issues of internal and external validity. They conclude with a discussion of potential areas for future work. Along the way, the authors note how textual analysis can unite the tribes of marketing. While most marketing problems are interdisciplinary, the field is often fragmented. By involving skills and ideas from each of the subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.

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>

Search, Discovery, Pills, and Portals

Solving the distribution crisis in marketing

Unwillingness to pay for privacy: A field experiment

We measure willingness to pay for privacy in a field experiment. Participants bought at most one DVD from one of two competing online stores. One store consistently required more sensitive personal data than the other, but otherwise the stores were identical. In one treatment, DVDs were one Euro cheaper at the store requesting more personal information, and almost all buyers chose the cheaper store. Surprisingly, in the second treatment when prices were identical, participants bought from both shops equally often.

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

Why Most Published Research Findings Are False

Summary There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

How To Think In Funnels (And Achieve The Best Possible Outcomes)

Elias Elmo Lewis created a formula that changed marketing forever. But his method applies beyond marketing. Here's how to think in funnels:

Ideation with Generative AI—in Consumer Research and Beyond

Abstract The use of generative AI (genAI) in consumer research is rapidly evolving, with applications including synthetic data generation, data analysis, and more. However, their role in creative ideation—a cornerstone of consumer research—remains underexplored. Drawing on the human creativity literature, we propose that ideation with genAI is facilitated by its productivity and semantic breadth, which are psychologically analogous to the dual pathways of persistence and flexibility in human ideation. Further, we distinguish between the utility of genAI as a key ideator versus humans as key ideator, conceptualized through the genAI ideation roles of Designer and Writer and of Interviewer and Actor. While genAI excels in generating incremental improvements, its potential for groundbreaking innovation could be unlocked by leveraging its ability to prompt human creativity. This article advances the theoretical and practical understanding of genAI in ideation for consumer research, offering numerous practical guidelines for integrating generative AI into research while emphasizing human–AI collaboration to achieve radical insights.

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.

Exploring Psychology in the Field: Steps and Examples From the Used‐Car Market

Abstract The growing availability of large datasets in a variety of domains presents an opportunity for researchers to use field data to better understand psychological concepts. I discuss, from an empirical economics point of view, steps for how to study cognition in large datasets. I use two recent papers that explore psychology in the used‐car market as motivating examples. These examples help illustrate the potential importance of big data as a way to explore human psychology and cognition. , The growing availability of large datasets in a variety of domains presents an opportunity for researchers to use field data to better understand psychological concepts. I discuss from an empirical economics point of view, steps for how to study cognition in large datasets and illustrate these steps with recent empirical papers.

Selling the American People: Advertising, Optimization, and the Origins of Adtech

How marketers learned to dream of optimization and speak in the idiom of management science well before the widespread use of the Internet.Algorithms, data

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.