Katsutoshi Yada

Mining Pharmacy Data Helps to Make Profits

Pharma, a drugstore chain in Japan, has been remarkably successful in the effective use of data mining. From over one tera bytes of sales data accumulated in databases, it has derived much interesting and useful knowledge that in turn has been applied to produce profits. In this paper, we shall explain several interesting cases of knowledge discovery at Pharma. We then discuss the innovative features of the data mining system developed in Pharma that led to meaningful knowledge discovery.

String analysis technique for shopping path in a supermarket

The sensor network technology developed in recent years has made it possible to accurately track the in-store behavior of customers which was previously indeterminable. The information on the in-store behavior of customers obtained by using this technology, namely information on their shopping path, provides us with useful information concerning the customer’s purchasing behavior. The purpose of this research is to apply character string analysis techniques to shopping path data so as to analyze customers’ in-store behavior, and thereby clarify technical problems with them (the character string analysis techniques) as well as their usability. In this paper we generated character strings on visit patterns to store sections by focusing exclusively on customers stopping by these sections in order to elucidate the visiting patterns of customers who made a large quantity of purchases. In this paper, we were able to discover useful information by using the character string analysis technique EBONSAI, thereby illustrating the usability and usefulness of character string analysis techniques in shopping path analysis.

Consumer Behavior Analysis By Graph Mining Technique

In this paper, we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for sequential purchase pattern is reviewed. Then we propose to represent the complicated customer purchase behavior by a directed graph retaining temporal information in a purchase sequence and apply a graph mining technique to analyze the frequent occurring patterns. In this paper, we demonstrate through the case of healthy cooking oil analysis how graph mining technology helps us understand complex purchase behavior.

Is this brand ephemeral? A multivariate tree-based decision analysis of new product sustainability

Decision tree methodology has become an increasingly important tool set in the field of decision science. We develop a multivariate, tree-based decision system for a new application: the determination of whether a newly launched consumer product should be allowed to continue in a highly competitive market. The system is designed to overcome a shortcoming-the inability to capture multivariate interactions-of traditional decision methods. We apply the proposed method to an instant noodle sales data set that contains 38 million transactions, and compare results across several methods.

Relation between stay-time and purchase probability based on RFID data in a Japanese supermarket

Radio Frequency Identification (RFID) technology uses radio waves to track an object to which a small tag is attached. In a Japanese supermarket, we attach the RFID device to the cart and collect data on purchase behavior. In this article, we clarify the relation between purchase probability and the time customers spend in the store section by analyzing the RFID data with main use of descriptive methods. We clarify the way how the stay-time explains the purchase probability and characteristics of each store section. Based on the result, some implications for business are made as well.

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.

Modeling Bank Runs in Financial Crises

Abstract.In recent years, financial crises have occurred frequently in each region, and banks are facing harsh management environments. Bank runs of customers during a financial crisis are one of a bank’s most serious risks. This research aims to build a bank run model for financial crises, use that model to estimate the amount of deposit funds which flow out, and propose a framework for financial crisis risk management. The model proposed in this paper enables understanding of the factors which have the largest impacts on bank runs, providing valuable information for banks to cope with such risks. The model uses survey data, and clarified that bank runs have differences which depend on customer characteristics and branch location. We understood that during a financial crisis, an appropriate branch strategy must be adopted depending on the location and customer characteristics of each bank branch.

A framework for analysis of the effect of time on shopping behavior

Due to technological developments, data about how many items a customer buys and how long the customer spends in a supermarket are available. A major problem with the data, however, is that there is no framework that considers the heterogeneity hidden in the data. In this article, we propose a framework that considers heterogeneity in the number of items a customer buys. The first step of our framework is based on the Poisson mixture regression model using a stationary time in the department where the items are sold as its independent variable. This model finds latent homogeneous groups of customers and gives the regression models within each group. It simultaneously classifies the customers into the homogeneous groups. In the second step of our framework, a method to investigate whether another factor (variable) influences the classification into homogeneous groups is presented. This proposed framework is applied to real data collected from the customers, and the effectiveness of the framework is shown. The managerial implications are drawn from the result of the analysis.

Analysis of Residence Time in Shopping Using RFID Data -- An Application of the Kernel Density Estimation to RFID

This study shows a method of determining and visualizing the existence probability of customers from shopping-path data in supermarkets using a database collected by a RFID (Radio Frequency Identification) technique, which allows us to analyze the detailed behaviors of customers. First, we present a method to estimate customer existence probability density on the sales floor using a Kernel density estimation. The kernel density estimation obtains continuous distribution of the existence probability density and grasps the detailed movements of customers. This estimation is better than an aggregation of residence time in each sales floor zone. Secondly, we visualize the customer existence probability density as cartographic output. Thirdly, we assess the relations between customer existence probability and sales in each sales floor zone using both shopping-path data and customer purchasing records. Finally, we assess the relation between customer existence probability and sales on each store shelf to verify the utility of this method for sales and marketing.