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To analyze features of customers’ point use behavior, we define and classify a unit based on five feature values to extract the behavioral features. Our results will contribute to the construction of a customer-specific measurement system through visualizing time-series transitions of customer point use behavior. Introduction Many retailers now use frequent shopper programs (FSPs) to improve customer loyalty and obtain identification (ID) point-of-sale (POS) data. A retailer using an FSP issues a point card to each customer. The customer can present points corresponding to a purchase amount using the point card, and can receive services, such as discounts, according to the amount of points at the next purchase. Thus, an FSP that gives a reward to customers in the form of points is a type of sales promotion. An FSP has two advantages. First, it may increase customer loyalty. Although discounts and coupons lose their effect after a single purchase, points accumulated must be used for subsequent purchases, which may lead to the customer’s next purchase. Second, customer attribute data can be acquired and linked with purchase history. Customers who want to benefit from an FSP must use a point card. When the point card is issued, the customer must enter details, such as their name, address, gender, and age, which can be used as customer attribute data. Furthermore, because the customer must present their point card at the time of purchase, it is possible to identify who made the purchase. The data stored by FSPs are ID-POS data. However, as the number of companies using FSPs increases, the effect of improving customer loyalty diminishes. If competitors have introduced similar programs, it is difficult to terminate an existing FSP even if a company’s profits are suffering. Therefore, it is necessary to use a current point service and propose measures to encourage effective point use that leads to an increase in customer loyalty. However, it is impossible to propose such measures at present because customers do not know how to effectively use points. Verhoef [1] has shown that FSPs have a positive effect on the customer retention rate and the development of customer share. Ishigaki et al. [2] classified customers by probabilistic latent semantic analysis [3] of ID-POS data. However, few studies have analyzed point use behavior quantitatively, proposed measures to make better use of point services, and focused on point history to classify customers. In this study, focusing on these gaps in the literature, we construct a model to analyze the point use behavior of customers, and we propose a measure for using point services more effectively. First, the point use behavior pattern of customers is extracted and the feature value is specified. Next, a classification model based on feature quantities is constructed to visualize time-series transitions of customer point use behavior.

Extraction of Behavioral Features Based on Customers’ Point Use History