About

Wendy W. Moe holds the position of Dean’s Professor of Marketing at the University of Maryland’s Robert H. Smith School of Business and is also an Amazon Scholar. Her research focuses on online consumer behavior, digital advertising, social media analytics, and customer engagement, with particular implications for how consumers engage with artificial intelligence. She analyzes data to quantify consumer behaviors and latent constructs related to these areas. Professor Moe is highly published, with her research appearing in numerous leading business journals, and she is the author of the book Social Media Intelligence. She has consulted for various corporations and government agencies in web analytics, social media insights, and product forecasting, and her work in web analytics was foundational for NetConversions, Inc., an early innovator in online data collection and analysis. Additionally, she has served as an expert witness in litigation concerning online consumer behavior, data tracking, and online marketing and advertising.

Research topics

• Data Mining
• Linguistics
• Psychology
• Cognitive psychology
• Artificial Intelligence
• Computer Science
• Statistics
• World Wide Web
• Business
• Philosophy
• Social psychology
• Advertising
• Mathematics
• Data science

Selected publications

• Inferring Intent from AI-Mediated Interactions: A Comparison of LLMs vs. Traditional Graph Methods

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Blind Spots in Broad Strokes: Caveats for the Use of LLMs in Marketing Research

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Do social media fans walk their talk? The impact of advocacy and criticism on own purchasing

  International Journal of Research in Marketing · 2025-04-10 · 1 citations

  articleOpen access

  Marketers typically favor customers who advocate for a brand over those who criticize it, as managerial insights on word-of-mouth suggest that advocacy positively influences while criticism negatively impacts the purchase behavior of other customers. However, little is known about the effects of advocacy and criticism on the engaging customer’s own purchase behavior. To close this gap, this research examines how advocacy and criticism in social media are associated with the engaging customer’s own subsequent purchases, while considering external influences (i.e., marketer and community engagement, advertising). The authors draw on a unique data set from the social media brand community of a major European online retailer, matching survey-based mindset metrics, observable engagement behaviors, and archival purchase data at the individual level. The results of this field study and an experimental validation study indicate that customers who advocate for the brand tend to purchase less often, while customers who criticize the brand tend to purchase more often. Based on psychological contract theory and exit, voice, and loyalty theory, the authors argue that advocactes primarily seek social value through community bonding, while critics aim to extract transactional value, either by maintaining a balanced brand relationship or prompting improvements in the brand’s offerings. Overall, this research challenges the assumption that advocacy fosters more loyal behaviors than criticism.

  PublisherDOI

• Predicting Purchase Intent: Deciphering Customer Interactions with AI Assistants

  SSRN Electronic Journal · 2024-01-01 · 2 citations

  articleOpen accessSenior author
  PublisherDOI

• The Internet as a Social Medium

  World Scientific-Now Publishers series in business · 2023-06-19

  book-chapter1st authorCorresponding

  The following sections are included:IntroductionOnline WOM and Business PerformanceDynamics in User-Generated ContentContent Analysis of UGCNetworks and InfluenceDirections for Future ResearchReferences

  PublisherDOI

• What Holds Attention? Linguistic Drivers of Engagement

  Journal of Marketing · 2023 · 78 citations

  • Psychology
  • Cognitive psychology
  • Social psychology

  From advertisers and marketers to salespeople and leaders, everyone wants to hold attention. They want to make ads, pitches, presentations, and content that captivates audiences and keeps them engaged. But not all content has that effect. What makes some content more engaging? A multimethod investigation combines controlled experiments with natural language processing of 600,000 reading sessions from over 35,000 pieces of content to examine what types of language hold attention and why. Results demonstrate that linguistic features associated with processing ease (e.g., concrete or familiar words) and emotion both play an important role. Rather than simply being driven by valence, though, the effects of emotional language are driven by the degree to which different discrete emotions evoke arousal and uncertainty. Consistent with this idea, anxious, exciting, and hopeful language holds attention while sad language discourages it. Experimental evidence underscores emotional language's causal impact and demonstrates the mediating role of uncertainty and arousal. The findings shed light on what holds attention; illustrate how content creators can generate more impactful content; and, as shown in a stylized simulation, have important societal implications for content recommendation algorithms.

  PublisherOA PDFDOI

• What Holds Attention? Linguistic Drivers of Engagement

  SSRN Electronic Journal · 2022 · 14 citations

  • Linguistics
  • Psychology
  • Cognitive psychology
  PublisherDOI

• Measuring Brand Favorability Using Large-Scale Social Media Data

  Information Systems Research · 2021 · 21 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Data Mining

  For decades, brand managers have monitored brand health with the use of consumer surveys, which have been refined to address issues related to sampling bias, response bias, leading questions, etc. However, with the advance of Web 2.0 and the internet, consumers have turned to social media to express their opinions on a variety of topics and, subsequently, have generated an extremely large amount of interaction data with brands. Analyzing these publicly available data to measure brand health has attracted great research attention. In this study, we focus on developing a method to measure brand favorability while accounting for the measure biases exhibited by social media posters. Specifically, we propose a probabilistic graphical model–based collective inference framework and implement a block-based Markov chain Monte Carlo sampling technique to obtain an adjusted brand favorability measure that is correlated with traditional survey-based measures used by brands. To demonstrate the effectiveness of our model, we evaluate it using more than 3,300 brands and about 205 million unique users that interact with those brands collected through Facebook. Our model performs very well, providing brand managers with a new method to more accurately measure consumer opinions toward the brand using social media data.

  PublisherDOI

• Uniting the Tribes: Using Text for Marketing Insight

  Journal of Marketing · 2019-08-29 · 696 citations

  article

  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.

  PublisherDOI

• Should Music Labels Pay for Radio Airplay? Investigating the Relationship Between Album Sales and Radio Airplay

  Figshare · 2018-06-30 · 7 citations

  articleOpen accessSenior author

  Managers in the music industry closely monitor both radio airplay of an album as well as the album's sales. Their interest in radio airplay is due to the belief that airplay can increase an album’s sales. Therefore it is natural for managers to attempt to influence radio airplay so as to subsequently impact album sales and ultimately profits. Over the past several years the concept of “pay-for-play” has resurfaced. If direct payments for radio airplay are to be made, then a precise understanding of the dynamic relationship between sales and airplay is needed. Typically radio airplay and album sales both show an exponential declining pattern. It is natural to ask whether both series are evolving concurrently–but independently–or is there some type of dependence? If there is a causal relationship, what is the direction of causality, or is there be a feedback relationship where both series influence each other? The purpose of this paper is to address these modeling questions using vector autoregressive models (VARMA), and show how these models can be used to answer the substantive question of whether the music industry should pay for airplay.

  OA PDFDOI

Frequent coauthors

• David A. Schweidel

  30 shared

• Peter S. Fader

  10 shared

• Sha Yang

  University of Southern California

  4 shared

• Yuchi Zhang

  3 shared

• Michael Trusov

  3 shared

• Jonah Berger

  University of Pennsylvania

  3 shared

• Michael Braun

  3 shared

• Sarah Oates

  2 shared