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 recommendation and pricing (BRP), to enhance the performance of recommendation system. BRP keeps all the separately priced products in the recommended set, and adds a subset of products as a new with a discounted price to customers. This approach extends pure bundling (PB), where all the products are sold in a single 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 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 by extracting the deadweight loss, but retains the profitability of 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 selection and pricing decisions. Extensive numerical results show that our algorithm outperforms other common heuristics. More importantly, by simply adding one more option to the common mechanism, our results show that BRP tends to significantly increase both the monopolist's profit and customers' utility as compared with and PB.