Using statistical learning theory for purchase behavior prediction via direct observation of in-store behavior

The advanced investigation of in-store behavior has been dramatically intrigued researchers in the last decade, since the radio frequency identification (RFID) technology was introduced into the grocery stores for information acquisition via tracking the customers. Shopping carts and baskets with a small RFID tag attached are recognized as surrogates instead of enumerators to trail the customers, which can provide an objective and direct observation to measure the in-store behavior of customers. The main purpose of this article is to improve the understanding of purchase behavior of customers with emphasis on meaningful knowledge from the study of their in-store behavior. For this purpose, we present an extraction of purchase behavior using statistical learning theory on dealing with the RFID data, which can describe the purchase behavior into a process of the customers´ in-store behavior. In this paper, we introduce three types of factors - personal factor, attitudinal factor and behavioral factor to train a model of purchase behavior, and investigate their effect on purchase behavior among heterogeneous customers grouping by their past purchase incidence. Finally, due to the additional effect of choice switching on purchase intention is also investigated basing on the primary situation, we suppose that these investigation outcomes can result from measuring customers´ response to sales promotion due to micro details of RFID data, which can also be applicable to business decision-making implications from the perspective of customers.