Partition and Prosper: Design and Pricing of Single Bundle

Design and Pricing of a Single Bundle

Product bundling is a widely used selling strategy among multiproduct firms, yet designing and pricing bundles optimally remain a complex challenge. In “Partition and Prosper: Design and Pricing of a Single Bundle,” Sun, Li, and Teo show that the selection and pricing of a single bundle from a range of products under multivariate normal valuations is polynomial-time solvable, provided that the associated covariance matrix can be decomposed into a positive diagonal matrix minus a positive semidefinite matrix of (small) fixed rank. Interestingly, they also show that if the individual product prices are predetermined, the optimization problem becomes NP hard even if customer valuations are independent. The authors use a Bayesian optimization algorithm combined with a novel conic integer programming reformulation to solve the problem under general valuation distributions and demonstrate the superior numerical performance of the algorithm via extensive simulations.