ENAiBLE Speaker Series
Translating Cutting-Edge Research Into Business Practice
Personalization in the Wild: Do More With Messy Data
Customer personalization should in principle be straightforward — collect data, build a predictive model, predict customer preferences, done. Unfortunately, retailers often find the reality to be messier:
- How do you stitch together multiple disparate sources of data on your customers?
- How do you do this when the data you have on an individual customer is tiny?
- How do you avoid the endless cycle of re-collecting data and rebuilding models?
Andrew will describe a new paradigm for organizing and learning from the exact kinds of data retailers collect, and discuss success cases from ecommerce and content personalization. This talk is aimed at a nontechnical, nonacademic audience.
Tuesday, February 23, 2021 | 4:30–5:30 p.m. EDT
A recording of this event can be viewed below.
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Speaker
Andrew Li
Assistant Professor, Tepper School of Business, Carnegie Mellon University
Andrew Li is an Assistant Professor of Operations Research at CMU’s Tepper School of Business. His research develops new methods in optimization and statistics, for problems in retail and personalized medicine. He also teaches and consults frequently in both spaces.
Andrew holds a B.S. in Operations Research (Columbia) and a Ph.D. in Operations Research (MIT).