Influence Maximization with Viral Product Design

Apr 24, 2014

Product design and viral marketing are two popular concepts in the marketing literature that, although following different paths, aim at the same goal: maximizing the adoption of a new product. While the effect of the social network is nowadays kept in great consideration in any marketing- related activity, the interplay between product design and social influence is surprisingly still largely unexplored. In this paper we move a first step in this direction and study the problem of designing the features of a novel product such that its adoption, fueled by peer influence and “word-of-mouth” effect, is maximized. We model the viral process of product adoption on the basis of social influence and the features of the product, and devise an improved iterative scaling procedure to learn the parameters that maximize the likelihood of our novel feature-aware propagation model. In order to design an effective algorithm for our problem, we study the property of the underlying propagation model. In particular we show that the expected spread, i.e., the objective function to maximize, is monotone and submodular when we fix the features of the product and seek for the set of users to target in the viral marketing campaign. Instead, when we fix the set of users and try to find the optimal features for the product, then the expected spread is neither submodular nor monotone (as it is the case, in general, for product design). Therefore, we develop an algorithm based on an alternating optimization between selecting the features of the product, and the set of users to target in the campaign. Our experimental evaluation on real-world data from the domain of social music consumption (LastFM) and social movie consumption (Flixster) confirms the effectiveness of the proposed framework in integrating product design in viral marketing.

  • SIAM International Conference on Data Mining (SDM)
  • Conference/Workshop Paper