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

Department of Business Management, College of Management, North Carolina State University, Raleigh, North Carolina 27695
Department of Marketing, Tippie College of Business, University of Iowa, Iowa City, Iowa 52242
smoon2{at}ncsu.edu
gary-j-russell{at}uiowa.edu

Product recommendation models are key tools in customer relationship management (CRM). This study develops a product recommendation model based on the principle that customer preference similarity stemming from prior purchase behavior is a key element in predicting current product purchase. The proposed recommendation model is dependent on 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 on past purchase behavior, a predictive model is calibrated in which the probability of product purchase depends on 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.

Key Words: marketing; recommendation system; spatial statistics; autologistic model
History: Received: October 6, 2005;