Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data

Bundle discounts are used by retailers in many industries. Optimal bundle pricing requires learning the joint distribution of consumer valuations for the items in the bundle, that is, how much they are willing to pay for each of the items. We suppose that a retailer has sales transaction data, and the corresponding consumer valuations are latent variables. Our main contribution is the development of a statistically consistent and computationally tractable inference procedure for fitting a copula model over correlated valuations. Simulations and data experiments demonstrate consistency, scalability, and the importance of incorporating correlations in the joint distribution.

By: Benjamin Letham, Wei Sun, Anshul Sheopuri

Published in: RC25418 in 2013


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