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DESCRIPTION:Scalable Price Targeting\n\nAbstract\n\nWe propose a Bayesion D
 ecision-Theoretic approach for implementing targeted “personalized” price 
 discrimination using a high-dimensional vector of observed customer charac
 teristics. The approach consists of applying a Bayesian Bootstrap to a reg
 ularized logit demand model using a lasso. We use the bootstrap to quantif
 y the uncertainty around the regularized demand estimates and the firm's p
 rofitability from different pricing decisions. We illustrate the proposed 
 approach using a case study of business-to-business pricing at a large\, o
 nline recruiting company. We first run a randomized price experiment to en
 sure that our training data can identify the causal effect of price on ind
 ividual demand. The experiment provides us with a model-free estimate of d
 emand. We use these data to estimate demand and conduct decision-theoretic
  optimal uniform and personalized pricing. The approach allows for custome
 r-specific personalized prices. We then conduct a second experiment with n
 ew customers to create a prediction sample to validate our price recommend
 ations and the proposed method for quantifying uncertainty. Optimized unif
 orm pricing improves revenues by 64.9% relative to the control pricing\, w
 hereas personalized pricing structure improves revenues by 81.5%. These im
 provements hold both in the training sample and in the subsequent predicti
 on sample. \n\n \n
DTSTART:20170131T183000Z
DTEND:20170131T200000Z
SUMMARY:BRIDGE Webinar: Jean-Pierre Dubé\, University of Chicago
URL:/desautels/channels/event/bridge-webinar-jean-pier
 re-dube-university-chicago-265364
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