Starting Cold: The Power of Social Networks in Predicting Customer Behavior in Peer-to-Peer Digital Platforms

Abstract

The last decade has seen a rapid emergence of non-contractual networked services. The standard approach in predicting future customer behavior in those services involves collecting data on a user's past purchase behavior and building statistical models to extrapolate a user's actions into the future. However, this method fails in the case of newly acquired customers where you have little or no transactional data. In this work, we study the extent to which knowledge of a customer's social network can solve this “cold-start” problem and predict the following aspects of customer behavior: (1) activity, (2) transaction levels, and (3) membership to the group of most frequent customers. We conduct a dynamic analysis on approximately 200,000 users from Venmo, the most popular peer-to-peer mobile payment application. Our models produce high-quality forecasts and demonstrate that social networks lead to a significant boost in predictive performance primarily during the first month of a customer's lifetime. Finally, utilizing propensity score matching, we investigate how the local cohesiveness of a user's initial community at the time of joining influences their likelihood of becoming a top 20% most frequent user within their cohort.

Keywords: Dynamic Social Networks; Customer Behavior; Cold-Start; Peer-to-Peer Digital Platforms; Predictive Analytics; Matching

JEL Classification: M31