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Product bundling is a common selling mechanism used in online retailing. Toset profitable bundle prices, the seller needs to learn consumer preferencesfrom the transaction data. When customers purchase bundles or multipleproducts, classical methods such as discrete choice models cannot be used toestimate customers' valuations. In this paper, we propose an approach to learnthe distribution of consumers' valuations toward the products using bundlesales data. The approach reduces it to an estimation problem where the samplesare censored by polyhedral regions. Using the EM algorithm and Monte Carlosimulation, our approach can recover the distribution of consumers' valuations.The framework allows for unobserved no-purchases and clustered market segments.We provide theoretical results on the identifiability of the probability modeland the convergence of the EM algorithm. The performance of the approach isalso demonstrated numerically.

Learning Consumer Preferences from Bundle Sales Data