Sequential pricing for social networks with multi-state diffusion

Abstract

The rapid development of online social networks (OSN) makes viral marketing through the word-of-mouth effect possible. Designing effective marketing strategy is critical in monetizing social networks. However, most existing studies focus on conducting effective influence maximization analysis to propagate information widely instead of explicitly incorporating the pricing factor to design intelligent marketing strategies. In this paper, we study the sequential pricing models in which a monopoly seller iteratively posts a sequence of public unique prices for a product in stages, and any interested buyer can buy the product at the posted price at a particular stage if his valuation is above the posted price. Specifically, our model is built on the multi-state diffusion scheme where an active user may be in the “AWARE” and “ADOPT” states. Users in “ADOPT” state could influence their neighbors’ valuation for the given product. To realize the goal of revenue maximization, we develop a Dynamic Programming Based Heuristic (DPBH) to obtain the optimal pricing sequence. Application of the DPBH in the revenue maximization problem shows that it performs well in the expected revenue achieved and in running time. This leads to fundamental ramifications to many related OSN marketing applications.