Abstract
Recently, credit cards with point rewards functions (rewards credit cards) are widely used. Credit card companies can collect the users’ usage log data of various stores in multiple industries. The purposes of possessing a credit card varies depending on each user such as to use only the credit function, to use both the credit and point rewards functions, etc. Moreover, credit cards can be used in various situations in users’ lives, and the purchase history of each user is diverse. Focusing on the diversity of both card possessing purposes and purchasing behavior for each user, we propose two latent class models representing these diversities in this research.
Highlights
1.1 Introduce the ProblemIn recent years, credit card systems have been introduced, and various credit cards are being widely used in many countries (Japanese Ministry of Economy, Trade & Industory; 2018)
It is obvious that there are no studies which focus on rewards credit card usage history data considering these two viewpoints simultaneously
In this research, we propose new analysis methods based on the latent class model (Goto & Kobayashi, 2014; Bistore, 2013; Hoffman, 1999) for user behavior analysis, that can effectively model rewards credit cards usage history data, which consists of groups of different statistical characteristics
Summary
Credit card systems have been introduced, and various credit cards are being widely used in many countries (Japanese Ministry of Economy, Trade & Industory; 2018). With the spread of credit card systems, users are able to purchase items at retail stores, pay utility fees, and so on by possessing one card. It is possible for credit card companies to collect purchase history data of various kinds of stores and industries at the same time. There is a demand for stimulating a user’s consumption activities by analyzing the large-scale data accumulated in the credit card companies and utilizing it for proposing marketing measures. There is a demand to derive marketing measures that contribute to the increase in the number of users who use the card, by analyzing the large-scale data collected by the credit card companies, and utilizing these results
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