Mining knowledge-sharing sites for viral marketing

TLDR

Viral marketing exploits customer influence networks to cheaply drive large behavioral changes, and knowledge‑sharing sites provide a rich source of such network data. The study aims to strengthen viral marketing by mining customer networks, building probabilistic models, and selecting optimal marketing plans, extending prior methods with reduced computational cost and applying them to knowledge‑sharing site data. The approach optimizes marketing spend per customer, accounting for partial network knowledge and the cost of acquiring that knowledge. Our results show the robustness and utility of this approach.

Abstract

Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, and using these models to choose the best viral marketing plan. Knowledge-sharing sites, where customers review products and advise each other, are a fertile source for this type of data mining. In this paper we extend our previous techniques, achieving a large reduction in computational cost, and apply them to data from a knowledge-sharing site. We optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him. We take into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost. Our results show the robustness and utility of our approach.