Takanobu Nakahara

The influence of shopping path length on purchase behavior in grocery store

In this paper we analyze the new type of information, namely RFID (Radio Frequency Identification) data, collected from the experiment in one of the supermarkets in Japan in 2009. This new type of data allows us to capture different aspects of actual in-store behavior of a customer, e. g. the length of her shopping path. The purpose of this paper is to examine more closely the effect of shopping path length on sales volume, which is one of the established ideas in RFID research as well as in retailing industry. In this paper we developed a simple framework, based on criteria of Wandering Degree and Purchase Sensitivity, in order to see how the relationship between distance* walked within the store and sales volume interacts with walking behavior of customers. As a result, in this paper we came up with some useful suggestions for more efficient in-store area management.

Analyzing consumers’ shopping behavior using RFID data and pattern mining

The development of sensor networks has enabled detailed tracking of customer behavior in stores. Shopping path data which records each customer’s position and time information is attracting attention as new marketing data. However, there are no proposed marketing models which can identify good customers from huge amounts of time series data on customer movement in the store. This research aims to use shopping path data resulting from tracking customer behavior in the store, using information on the sequence of visiting each product zone in the store and staying time at each product zone, to find how they affect purchasing. To discover useful knowledge for store management, shopping paths data has been transformed into sequence data including information on visit sequence and staying times in the store, and LCMseq has been applied to them to extract frequent sequence patterns. In this paper, we find characteristic in-store behavior patterns of good customers by using actual data of a Japanese supermarket.

Recommender System for Music CDs Using a Graph Partitioning Method

Collaborative filtering is used for the prediction of user preferences in recommender systems, such as for recommending movies, music, or articles. This method has a good effect on a companys business. E-commerce companies such as Amazon and Netflix have successfully used recommender systems to increase sales and improve customer loyalty. However, these systems generally require ratings for the movies, music, etc. It is usually difficult or expensive to obtain such ratings data comparison with transaction data. Therefore, we need a high quality recommender system that uses only historical purchasing data without ratings. This paper discusses the effectiveness of a graph-partitioning method based recommender system. In numerical computational experiments, we applied our method to the purchasing data for CDs, and compared our results with those obtained by a traditional method. This showed that our method is more practical for business.

Decision tree-based classifier incorporating contrast patterns

In the last ten years, studies that focus on the extraction of patterns among contrast classes and reveal the differences among these classes have been conducted. There exist two major streams in such studies, namely, emerging patterns (EPs) and contrast patterns (CPs), and related works concerning both have been proposed. In this field of study, the main problems pertain to extracting the efficiency of EPs or CPs and constructing smart classifiers on their basis. In this study, we propose a decision tree-based classifier using contrast patterns extracted by LCM. We also propose a method to construct a decision tree model that incorporates contrast patterns. Contrast patterns are extracted by LCM efficiency, and diverse scenarios are indicated by decision tree models in terms of business applications. Further, an example of our studies is illustrated using practical case data.