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LIDS Seminar Series

Personalized Dynamic Pricing with Machine Learning: High Dimensional Covariates and Heterogeneous Elasticity

April 9, 2019 @ 4:00 pm - 5:00 pm

Gah-Yi Ban (London Business School)


We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a $d$-dimensional feature vector. We assume a personalized demand model, parameters of which depend on $s$ out of the $d$ features. The seller initially does not know the relationship between the customer features and the product demand, but learns this through sales observations over a selling horizon of $T$ periods. We prove that the seller’s expected regret, i.e., the revenue loss against a clairvoyant who knows the underlying demand relationship, is at least of order $s\sqrt{T}$ under any admissible policy. We then design a near-optimal pricing policy for a “semi-clairvoyant” seller (who knows which s of the d features are in the demand model) that achieves an expected regret of order $s\sqrt{T}log(T)$. We extend this policy to a more realistic setting where the seller does not know the true demand predictors, and show this policy has an expected regret of order $s\sqrt{T}(log(d)+log(T))$, which is also near-optimal. Finally, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods such as myopic pricing and segment-then-optimize policies. Furthermore, our policy significantly improves upon the loan company’s historical pricing decisions in terms of annual expected revenue.

Bio: Gah-Yi Ban is an Assistant Professor of Management Science and Operations at London Business School. Gah-Yi’s research is in Big Data analytics, specifically decision-making with complex, high-dimensional and/or highly uncertain data with applications to operations management and finance. Gah-Yi’s research has appeared on most-downloaded lists of Management Science and Operations Research, and awarded Honorable Mention in 2018 INFORMS JFIG Paper Competition. Gah-Yi graduated from UC Berkeley with MSc/MA/PhD in Industrial Engineering/ Statistics/Operations Research.


The LIDS Seminar Series features distinguished speakers who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing. 

MIT Institute for Data, Systems, and Society
Massachusetts Institute of Technology
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Cambridge, MA 02139-4307