Understanding shopping behavior of customers using transactional data

Official URL: http://risc01.sabanciuniv.edu/record=b1857581 (Table of Contents)

Digital technologies allow us to trace human behaviors by generating large amounts of data. In this study a private Turkish bank data containing 60 thousand customers and 2 million credit card transactions are used to analyze the shopping behaviors of individuals. Even though we are in an age of growing online shopping, people still prefer to visit shopping malls, or the stores placed in high streets to experience shopping. They usually make their shopping place decisions according to store variety, accessibility, comfort, and social aspects. In this study, we investigate people’s variety seeking behavior in the context of shopping malls and shopping categories to assess their shopping experience. We use K-means clustering algorithm to distinguish between customers’ shopping behaviors by using the behavioral features we extract from their credit card spending. In addition, we propose a method to assign individuals to one of the segments by measuring the demographic property similarity with segments. Our results indicate that there is an association between demographic properties and shopping behavior. The findings also suggest that females are more likely to search for variety of shopping malls and categories, and hence perceive shopping as an entertaining and social activity, whereas men prefer to shop in particular shopping malls for need-driven purchases indicating that they do not wish to lose time and energy for shopping. We hope that our research will guide the marketers to communicate the right group of customers with the right strategy.