Uncovering path-to-purchase segments in large consumer population using clustered multivariate autoregression

We propose a novel method to identify predominant paths-to-purchase of retail consumers from activity level dataset collected in CRM systems. We verify the effectiveness of the proposed model on a simulated dataset. Following successful verification, we apply the model on a retail dataset from a major multi-channel, multi-brand North American Retailer. We uncover three different types of consumers based on how they respond to external stimuli over time: catalog driven shoppers, email driven shoppers, and holiday driven online shoppers. We also find significant activity across channels by these consumers. Finally, we use the path information in the segments to identify the groups that are most sensitive to a certain type of marketing contact. By analyzing the response of customers in different groups in a test dataset, we show that managers can optimize marketing budget allocation using our proposed segmentation approach.