Biography
David A. Schweidel is Professor of Marketing at Emory University’s Goizueta Business School. Schweidel received his BA in mathematics, MA in statistics, and PhD in marketing from the University of Pennsylvania. He was previously on the faculty of the Wisconsin School of Business at the University of Wisconsin-Madison and Georgetown University’s McDonough School of Business.
Schweidel is an expert in the areas of customer relationship management and social media analytics. His research focuses on the development and application of statistical models to understand customer behavior and inform managerial decisions. His research has appeared in leading business journals including Journal of Marketing, Journal of Marketing Research, Marketing Science and Management Science. His research has garnered numerous awards, including the Gaumnitz Junior Faculty Research Award from the Wisconsin School of Business and the Marketing Science Institute’s Buzzell Award. He has been recognized as a leading scholar by the Marketing Science Institute’s Young Scholar and Scholar programs, and by Poets and Quant’s “Top 40 Under 40.” Based on his research, he has consulted for companies including Airbnb, Thumbtack, eBay, and General Motors.
Schweidel is the author of Social Media Intelligence (Cambridge University Press) in which he and his co-author discuss how organizations can leverage social media data to inform their marketing strategies. He is also the author of Profiting from the Data Economy (Pearson FT Press), in which he details the value of businesses tapping into consumer data for both individuals and companies.
Media
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Droid Duo Ep. 9 — The Great Migration: QuitGPT, Checkout Retreats & AI Brand Blind Spots -
Episode 8: There Can Be Only One: The Great AI Ad Race
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Big Data and the Future of Privacy -
Profiting from the Data Economy: Understanding the Roles of Consumers, Innovators and Regulators in a Data-Driven World (FT Press Analytics) -
Social Media Intelligence
Education
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PhD in MarketingThe Wharton School of the University of Pennsylvania
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MS in StatisticsThe Wharton School of the University of Pennsylvania
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BA in Mathematics, Economics and Actuarial MathematicsUniversity of Pennsylvania
Frontiers: Supporting Content Marketing with Natural Language Generation
Advances in natural language generation (NLG) have facilitated technologies such as digital voice assistants and chatbots. In this research, we demonstrate how NLG can support content marketing by using it to draft content for the landing page of a website in search engine optimization (SEO). Traditional SEO projects rely on hand-crafted content that is both time consuming and costly to produce...
The role of slant and message consistency in political advertising effectiveness: evidence from the 2016 presidential election
We explore the relationship between the content of political advertising on television and ad effectiveness. Specifically, we investigate how slant – the extremeness of the message – and consistency with the candidate’s primary campaign messaging in national ad buys relate to two measures of voter behavior: online word-of-mouth (WOM) and voter preference (captured through daily polls) for the candidates. Using data from the 2016 presidential election, we find that ad messages that are more (1) centrist and (2) consistent with a candidate’s primary-election platform associate with increases in online WOM and voter preference for the candidate.
How consumer digital signals are reshaping the customer journey
Marketers are adopting increasingly sophisticated ways to engage with customers throughout their journeys. We extend prior perspectives on the customer journey by introducing the role of digital signals that consumers emit throughout their activities. We argue that the ability to detect and act on consumer digital signals is a source of competitive advantage for firms. Technology enables firms to collect, interpret, and act on these signals to better manage the customer journey. While some consumers’ desire for privacy can restrict the opportunities technology provides marketers, other consumers’ desire for personalization can encourage the use of technology to inform marketing efforts.
Measuring the Impact of Product Placement with Brand-Related Social Media Conversations and Website Traffic
Advertisers are growing increasingly concerned about the ease with which traditional television advertising can be avoided. Product placement activities, where brands are visually and/or verbally incorporated into television and movies, have continued to grow. In contrast to television commercials that can be avoided by viewers, product placement is embedded in the programming itself and is more difficult to avoid. Despite its popularity, there is limited research in marketing that has investigated the impact of product placement.
Incorporating direct marketing activity into latent attrition models
When defection is unobserved, latent attrition models provide useful insights about customer behavior and accurate forecasts of customer value. Yet extant models ignore direct marketing efforts. Response models incorporate the effects of direct marketing, but because they ignore latent attrition, they may lead firms to waste resources on inactive customers.
Online product opinions: Incidence, evaluation, and evolution
Whereas recent research has demonstrated the impact of online product ratings and reviews on product sales, we still have a limited understanding of the individual's decision to contribute these opinions. In this research, we empirically model the individual's decision to provide a product rating and investigate factors that influence this decision.
Portfolio dynamics for customers of a multiservice provider
Multiservice providers, such as telecommunication and financial service companies, can benefit from understanding how customers' service portfolios evolve over the course of their relationships. This can provide guidance for managerial issues such as customer valuation and predicting customers' future behavior, whether it is acquiring additional services, selectively dropping current services, or ending the relationship entirely. In this research, we develop a dynamic hidden Markov model to identify latent states that govern customers' affinity for the available services through which customers evolve.
Understanding service retention within and across cohorts using limited information
Service churn and retention rates remain central as constructs in marketing activities such as valuation of service subscribers and resource allocation. While extant approaches have been proposed to relate service churn to external factors such as reported satisfaction, marketing mix activities, and the like, managers often face situations in which the only information available is the duration for which subscribers have had service. In such cases, can they forecast service churn and understand the contributing factors, which may allow for subsequent intervention?
A bivariate timing model of customer acquisition and retention
Two widely recognized components, central to the calculation of customer value, are acquisition and retention propensities. However, while extant research has incorporated such components into different types of models, limited work has investigated the kinds of associations that may exist between them. In this research, we focus on the relationship between a prospective customer's time until acquisition of a particular service and the subsequent duration for which he retains it, and examine the implications of this relationship on the value of prospects and customers.