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2/23/21, ENAiBLE SS: Personalization in the Wild: Do More With Messy Data

Event Details

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 re-building models?

Andrew will describe a new paradigm for organizing and learning from the exact kinds of data retailers collect, and discuss success cases from e-commerce and content personalization. This talk is aimed at a non-technical, non-academic audience.

Tuesday, February 23, 2021
4:30-5:30 p.m. ET
Virtual Program

All participants must register for this event. A Zoom login link will be provided before the virtual program in a confirmation email. 

Register by Monday, February 22

Questions? Contact ENAiBLE

Faculty Speaker

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). 

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