Predicting product adoption in large-scale social networks

Online social networks offer opportunities to analyze user behavior and social connectivity and leverage resulting insights for effective online advertising. We study the adoption of a paid product by members of a large and well-connected Instant Messenger (IM) network. This product is important to the business and poses unique challenges to advertising due to its low baseline adoption rate. We find that adoption by highly connected individuals is correlated with their social connections (friends) adopting after them. However, there is little evidence of social influence by these high degree individuals. Further, the spread of adoption remains mostly local to first-adopters and their immediate friends. We observe strong evidence of peer pressure wherein future adoption by an individual is more likely if the product has been widely adopted by the individual's friends. Social neighborhoods rich in adoptions also continue to add more new adoptions compared to those neighborhoods that are poor in adoption. Using these insights we build predictive models to identify individuals most suited for two types of marketing campaigns - direct marketing where individuals with highest propensity for future adoption are targeted with suitable ads and social neighborhood marketing which involves messaging to members of the social network who are most effective in using the power of their network to convince their friends to adopt. We identify the most desirable features for predicting future adoption of the PC To Phone product which can in turn be leveraged to effectively promote its adoption. Offline analysis shows that building predictive models for direct marketing and social neighborhood marketing outperforms several widely accepted marketing heuristics. Further, these models are able to effectively combine user features and social features to predict adoption better than using either user features or social features in isolation.