Scalable Inference of Customer Similarities from Interactions Data using Dirichlet Processes

TL;DR

This paper introduces a scalable Bayesian nonparametric method using Dirichlet processes to infer customer similarities from interaction data, addressing computational challenges in large social networks for marketing insights.

Contribution

It develops a novel probabilistic framework grounded in homophily theory that efficiently models customer interactions in large networks using Dirichlet processes.

Findings

Framework effectively captures latent customer similarities.

Method scales to large interaction networks.

Provides actionable insights for marketing segmentation.

Abstract

Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from which, with homophily as a guiding principle, inferences could be made about the underlying similarities. However, larger networks face a quadratic explosion in the number of potential interactions that need to be modeled. This scalability problem renders probability models of social interactions computationally infeasible for all but the smallest networks. In this paper we develop a probabilistic framework for modeling customer interactions that is both grounded in the theory of homophily, and is flexible enough to account for random variation in who interacts with whom. In particular, we present a novel Bayesian nonparametric approach, using Dirichlet processes, to moderate the scalability problems that marketing researchers encounter when working with networked data. We find that this framework is a powerful way to draw insights into latent similarities of customers, and we discuss how marketers can apply these insights to segmentation and targeting activities.