A Bayesian Network Approach for Predicting Purchase Behavior via Direct Observation of In-store Behavior

Abstract

In strategic management of retail industry, the advanced investigation by using radio frequency identification (RFID) technology to capture customers’ in-store behavior has been dramatically attracted scholars and practitioners in past ten years. As a small RFID tag attached to the shopping carts can be recognized as surrogates instead of enumerators to trail the customers, it can provide us an objective and direct perspective to observe and measure the in-store behavior of customers. In this article, we present a study on this new type of in-store behavior data named RFID data, which can improve the understanding of purchase behavior of customers with emphasis on meaningful knowledge via analysis of RFID data. In contrast to prior studies in this research field, this paper has paid special attention to shopping time that customers spent in supermarket (so-called stay time), and presents methodological analysis into two folds. First, we develop a bayesian network (BN) model to combine both of purchase behavior and in-store behavior as features. As BN is a probabilistic graphical model, it can provide an quantitative analysis process of purchase behavior decision over stay time and also allow us to interpret the decision process of purchasing in a much more intuitive measurement. The results show BN has a better accuracy than other typical prediction models (linear discriminant analysis, logistic regression and support vector machine). Second, due to BN can estimate and predict in a nonlinear correlation between purchase intention and stay time, we examine a tedium effect on purchase behavior. During the customers wander in shopping, purchase intention represents a non-monotonic phenomena accounting for the long stay time. Finally, we also investigate the sensitivity and specificity of purchase behavior predicted by our proposal in adjustment of decision threshold and implement several business decision-making implications in actual business situations.