Eric T Bradlow

About

Eric T Bradlow is the K.P. Chao Professor, Professor of Marketing, Statistics, Education, and Economics, Chairperson of Wharton's Marketing Department, and Vice Dean of AI & Analytics at Wharton. An applied statistician, he utilizes high-powered statistical models to address a wide range of problems, from Internet search engines to product assortment issues. His research interests include Bayesian modeling, statistical computing, and developing new methodologies for analyzing unique data structures with applications to business problems. Bradlow has made significant contributions to marketing research methods, psychometrics, and analytics, and has published extensively in top-tier academic journals. He is recognized for his leadership roles in the academic community, including serving as past Chair of the American Statistical Association Section on Statistics in Marketing, past Editor-in-Chief of Marketing Science, and as a fellow of several professional societies such as INFORMS Society for Marketing Science, the American Statistical Association, and the American Educational Research Association. Bradlow has also worked at Bell Labs and the Educational Testing Service, and has received numerous teaching awards at Wharton, including the Linback Award for Distinguished PhD Teaching and Mentoring, the Anvil Award for MBA Education, and the Excellence in Teaching Award. He earned his PhD and Master's degrees in Mathematical Statistics from Harvard University and his BS in…

Research topics

• Computer Science
• Sociology
• Data science
• Artificial Intelligence
• Political Science
• Statistics
• Economics
• Mathematics
• Psychology
• Mathematics education

Selected publications

• “Statistical Significance” and Statistical Reporting: Moving Beyond Binary

  Journal of Marketing · 2023 · 72 citations

  Null hypothesis significance testing (NHST) is the default approach to statistical analysis and reporting in marketing and the biomedical and social sciences more broadly. Despite its default role, NHST has long been criticized by both statisticians and applied researchers, including those within marketing. Therefore, the authors propose a major transition in statistical analysis and reporting. Specifically, they propose moving beyond binary: abandoning NHST as the default approach to statistica…

  PublisherDOI

• Refocusing loyalty programs in the era of big data: a societal lens paradigm

  Marketing Letters · 2020 · 29 citations

  Abstract Big data and technological change have enabled loyalty programs to become more prevalent and complex. How these developments influence society has been overlooked, both in academic research and in practice. We argue why this issue is important and propose a framework to refocus loyalty programs in the era of big data through a societal lens . We focus on three aspects of the societal lens—inequality, privacy, and sustainability. We discuss how loyalty programs in the big data era impact…

  PublisherOA PDFDOI

• Testing Theories of Goal Progress in Online Learning

  Journal of Marketing Research · 2021 · 12 citations

  Online educational platforms increasingly allow learners to consume content at their own pace with on-demand formats, in contrast to the synchronous content of traditional education. Thus, it is important to understand and model learner engagement within these environments. Using data from four business courses hosted on Coursera, the authors model learner behavior as a two-stage decision process, with the first stage determining across-day continuation (vs. quitting) and the second stage determ…

  PublisherDOI

• Cross-reward effects in a coalition loyalty program: The impact of a point currency devaluation

  International Journal of Research in Marketing · 2022-09-06 · 9 citations

  articleOpen accessSenior author

  While single-brand reward programs encourage customers to remain loyal to that one brand, coalition programs encourage customers to be “promiscuous” by offering points redeemable across partner stores. Despite the benefits of this “open relationship” with customers, store managers face uncertainty as to how rewards offered by partners influence transactions at their own stores. We use a model of multi-store purchase incidence and spend to show how the value of points shared among partner stores…

  PublisherDOI

• Fast construction of interpretable whole-brain decoders

  Cell Reports Methods · 2022-06-01 · 8 citations

  articleOpen access

  Researchers often seek to decode mental states from brain activity measured with functional MRI. Rigorous decoding requires the use of formal neural prediction models, which are likely to be the most accurate if they use the whole brain. However, the computational burden and lack of interpretability of off-the-shelf statistical methods can make whole-brain decoding challenging. Here, we propose a method to build whole-brain neural decoders that are both interpretable and computationally efficien…

  PublisherDOI

Frequent coauthors

• Howard Wainer

  46 shared

• Peter S. Fader

  37 shared

• Eric M. Schwartz

  13 shared

• Xiaohui Wang

  10 shared

• Sam K. Hui

  University of Houston

  9 shared

• David A. Schweidel

  9 shared

• Alan M. Zaslavsky

  Harvard University

  8 shared

• Elea McDonnell Feit

  Drexel University

  8 shared

Labs

• Wharton Behavioral LabPI

Awards & honors

• Fellow of the INFORMS Society for Marketing Science
• Fellow of the American Statistical Association
• Fellow of the American Educational Research Association
• Past chair of the American Statistical Association Section o…
• Past Editor-in-Chief of Marketing Science

Similar researchers at University of Pennsylvania

• Andrew B. Abelh-60
• Raffi Amith-58
• Lawton R. Burnsh-48