Generalizing Latent Attrition Models for Multi-Activity Customer Base Analysis

Abstract

Customer base analysis is a key element in customer valuation and can provide guidance for decisions such as resource allocation. Yet, extant models often focus on a single activity, such as purchases from a retailer or donations to a non-profit organization. These models do not consider other ways that an individual may engage with an organization, such as purchasing in multiple categories or contributing user-generated content. In this research, we propose a framework to generalize extant models for customer base analysis to multiple activities.

Using the data from a website that allows users to purchase digital content and/or post digital content at no charge, we develop a flexible "buy 'til you die" model to empirically examine how the two activities are related. Compared to benchmarks, our model more accurately forecasts the future behavior for both types of activities. In addition to finding evidence of coincidence between the activities while customers are "alive," we find that the latent attrition processes are related. This suggests that conducting one type of activity is informative of whether customers are still "alive" to conduct another type of activity, and consequently impacts inferences of customer value.

Keywords: customer base analysis, Latent Changepoint Model, Multivariate Choice Model