A Framework for Automated Bundling and Pricing Using Purchase Data

We present a framework for automatically suggesting highprofit bundle discounts based on historical customer purchase data. We develop several search algorithms that identify profit-maximizing prices and bundle discounts. We introduce a richer probabilistic valuation model than prior work by capturing complementarity, substitutability, and covariance, and we provide a hybrid search technique for fitting such a model to historical shopping cart data. As new purchase data is collected, it is integrated into the valuation model, leading to an online technique that continually refines prices and bundle discounts. To our knowledge, this is the first paper to study bundle discounting using shopping cart data. We conduct computational experiments using our fitting and pricing algorithms that demonstrate several conditions under which offering discounts on bundles can benefit the seller, the buyer, and the economy as a whole. One of our main findings is that, in contrast to products typically suggested by recommender systems, the most profitable products to offer bundle discounts on appear to be those that are occasionally purchased together and often separately.