Product Bundle Recommendation and Pricing: How to Make It Work?

Abstract

Product bundling is a common marketing strategy for cross-selling in multiproduct firms. Motivated by settings in online product recommendation, we propose a new approach, dubbed bundle recommendation and pricing (BRP), to enhance the performance of bundle recommendation system. BRP keeps all the separately priced products in the recommended set, and adds a subset of products as a new bundle with a discounted price to customers. This approach extends pure bundling (PB), where all the products are sold in a single bundle with a discounted price to customers. Although PB can be more profitable than component pricing (CP) where products are priced and sold separately, it can be inferior to CP in the presence of high marginal cost. We show that such a simple "CP + one bundle" scheme can be more profitable than both PB and CP, and is near optimal in many environments.

BRP improves CP by extracting the deadweight loss, but retains the profitability of CP when some products have relatively high marginal costs. However, finding the optimal BRP solution is often intractable. We develop a new approximation to this problem and use a Bayesian optimization algorithm to optimize the bundle selection and pricing decisions. Extensive numerical results show that our algorithm outperforms other common heuristics. More importantly, by simply adding one more bundle option to the common CP mechanism, our results show that BRP tends to significantly increase both the monopolist's profit and customers' utility as compared with CP and PB.