Predicting Product Purchase from Inferred Customer Similarity : An Autologistic Model Approach

Product recommendation models are key tools in customer relationship management (CRM). This study develops a product recommendation model based upon the principle that customer preference similarity stemming from prior response behavior is a key element in predicting current product purchase. The proposed recommendation model is dependent upon two complementary methodologies: joint space mapping (placing customers and products on the same psychological map) and spatial choice modeling (allowing observed choices to be correlated across customers). Using a joint space map based upon past purchase behavior, a predictive model is calibrated in which the probability of product purchase depends upon the customer's relative distance to other customers on the map. An empirical study demonstrates that the proposed approach provides excellent forecasts relative to benchmark models for a customer database provided by an insurance firm