Numerical investigation on mixed bundling and pricing of information products

The bundling sale of information products has been a prevalent strategy in information industries. This paper attempts to study two issues that have not been adequately addressed in previous researches. First, we define the measure of customer heterogeneity by considering both the statistic characteristics of customer reservation price and the marginal cost of information products. Numerical computation indicates that the maximal profit of three bundling strategies (individual sale, pure bundling, and mixed bundling) drops monotonically with the increasing of heterogeneity among customers, and that mixed bundling is more profitable than schemes of either the pure bundling or individual sale when non-negligible heterogeneity exists among customers. Second, we present the mixed partial bundling scheme for real-world situations in which a large number of information products are offered and the preferences or valuations of customers considerably vary. We build a bilevel programming model to define the behaviors of the monopolist and customers, and develop a heuristic algorithm to construct optimal mixed partial bundling schemes. Numerical experiments verify that the mixed partial bundling engenders less substantial deadweight loss and extracts more surplus from heterogeneous customers, which illustrates that the mixed partial bundling strategy is a more flexible and efficient way to accommodate multiple segments of customers.