Uncovering Paths to Purchase of Heterogeneous Consumers Using Clustered Multivariate Autoregression

Abstract

A digital consumer’s purchase journey, referred to as the path to purchase, is non-linear and heterogeneous. Despite a strong interest in this concept, there are few published approaches to empirically extract consumers’ path to purchase (in terms of a sequence of different types of activities leading to purchase), especially in settings where consumers engage in multiple simultaneous activities in each period. We address this gap by proposing a methodology that identifies consumers’ paths to purchase from commonly available CRM touch point data. We propose a generalized multivariate autoregressive (GMAR) model to capture the interactions among distinct but potentially simultaneous activities of a consumer over time. Using the proposed model we show how to attribute parts of the purchase volume to consumer activity sequences, or paths, starting from an initial marketing stimulus leading to the maximal purchase response. We embed the GMAR model in a clustering framework that endogenously identifies segments of consumers who exhibit similar paths to purchase. We apply the methodology to a dataset from a multi-channel North American Specialty Retailer to uncover the distinct paths of five consumer segments: loyal and engaged shoppers, digitally-driven offline shoppers, holiday shoppers, infrequent offline shoppers, and frequently targeted occasional shoppers. Using out-of-sample forecasts, we demonstrate improved predictions of future purchases compared to extant methods. Finally, we perform policy simulations to show that managers can use the uncovered path information to dynamically optimize marketing campaigns for each segment.