Optimal Design on Customized Bundling Strategy of Information Goods for Customers with Two-Dimensional Heterogeneity

Abstract

Customized bundling is a pricing strategy that allows consumers to choose a certain quantity of products at a fixed price. In the reality, a customer usually has a specific rank on information goods based on their valuations, or information goods can be ranked into a list of products with decreasing valuations for a customer. Thus, we characterize customers in two dimensions for constructing the customized bundles of ranked information goods: (i) the valuation that a customer sets for his/her most favorite information good; and (ii) the total quantity of information goods with positive valuations that a customer requires. We derive the optimal customized bundling strategies in two typical scenarios and examine the impact of customer heterogeneity in terms of each dimension on the optimal pricing schemes of customized bundles. Analytical results indicate that the two features have similar effects on optimal bundle price, market penetration, and maximal profit, but impact differently on optimal bundle size. Larger customer heterogeneity leads to a lower or identical optimal bundle price, market penetration, and maximal profit. However, optimal bundle size shrinks or remains unchanged with increased customer heterogeneity on the total quantities of information goods with positive valuations, but it grows or stays the same when customers have larger heterogeneity on the valuations of their most favorite information goods. Our results provide explanations to the marketing practices of digital product firms, and also support the optimal decision of customized bundling of information goods for heterogeneous customers.