Character String Analysis and Customer Path in Stream Data

This purpose of this study is to propose a knowledge-discovery system that can abstract helpful information from character strings representing shopper visits to product sections associated with positive and negative purchasing events by applying character string parsing technologies to stream data describing customer purchasing behavior inside a store. Taking data that traced customers\u27 movements we focus on the number of times customers stop by particular product sections, and by representing those visits in the form of character strings, we propose a way to efficiently handle large stream data. During our experiment, we abstract store-section visiting patterns that characterize customers who purchase a relatively larger volume of items, and are able to show the usefulness of these visiting patterns. In addition, we examine index functions, calculation time, and prediction accuracy, and clarify technological issues warranting further research. In the present study, we demonstrate the feasibility of employing stream data in the marketing field and the usefulness of the employing character parsing techniques.IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008, 15-19 December 2008, Pisa, Ital

The Practice of an Optimal Pricing Strategy for Maximizing Store Profits Using PRISM

The purpose of this paper is to introduce a process for implementing optimal pricing that uses PRISM to maximize store profits. PRISM is a system and process that uses data mining technology to process large volumes of data, then develops a probability model for customer purchases, and which then uses a heuristic approach to identify the pricing pattern that will maximize store profits. For this paper, we used customer purchase data from Japanese supermarkets to identify the optimal pricing pattern for curry roux, which would maximize store profits.2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, Budapest, Hungary, October 9-12, 2016

Knowledge Discovery from the Structure of Persuasive Communication

It is difficult to carry out quantitative measurements of the persuasive power of business communications (i.e., persuasive skills) and such communications are likely to involve difficult to understand, unseen and unknown knowledge. However, using unstructured recorded communication data based on conversations with business customers, we have been developing explicit knowledge concerning skills necessary for effective communications in the form of an expression framework. The objective of this research is to generate a framework and a process for explicit management knowledge concerning understandable communication skills as opposed to the tacit, hard to understand the negotiating skills related to overdue payment collection personnel and to verify the actual usefulness of this knowledge using the accumulated data in a company. Using this process we have developed, it is possible to discover the special characteristics of the communication content of high success overdue payment collection personnel.2006 IEEE International Conference on Systems, Man and Cybernetics, Taipei, Taiwan, 8-11 October 200

The River-Rafting System for Knowledge Discovery Related to Persuasion Process Conversation Logs

The purpose of this research is to develop aframework to represent the content and process of persuasion communications for overdue payment collection, thus making it possible to examine how the skilled operators have used theme related keywords concerning motivations to pay, the payment methods and the payment confirmation in their negotiation to achieve higher collection success. This paper describes a basis for modeling a persuasion process. There has been no research or methods for dealing with large amounts of conversation logs for discovering useful knowledge about persuasion processes. In this paper, we report our successful efforts in discovering a part of the distinctive features of skilled worker techniques as indicated in their conversations related to overdue payment collection and the application of our methods to communication data related to a Japanese telecommunications company.December 18-22, 2006 Hong Kong, Chin

A Data Mining System for Managing Customer Relationship

This paper presents a data mining study that aims to identify potential high-value visitors for a drugstore chain in Japan. Our purpose is to provide timely decision support to the marketing and service departments for managing customer relationship. The conceptualization of customer value is discussed and is differentiated from a more commonly used construct, customer loyalty. We briefly describe the data mining system that supports the study. Our result show two supervised learning methods are comparable in terms of predictive accuracy

Discovering association strength among brand loyalties from purchase history

Analyzing purchase history of customers enables us to discover valuable knowledge that is helpful for developing effective sales promotion. In this respect, we shall introduce a new notion, association strength among brand loyalties, which is defined for every ordered pair of brands. If the association strength between loyalties of brands A and B is high, it represents that purchase of brand A is highly correlated to that of brand B. Conventional method for discovering associative purchasing is usually applied for one purchase opportunity (one receipt), i.e., it reveals how often two commodities are purchased at the same time. On the other hand, we are interested in discovering relationship among customers’ loyalties to certain brands or manufacturers by investigating long-term purchase history of customers. By computing association strengths from customers’ purchase history of drugstore chain in Japan, we could produce several interesting rules that will be useful for sales promotion planningISIE 2001, June 12-16, 2001, Paradise Hotel Pusan, Kore

Duration of Price Promotion and Retail Profit: An In-depth Study Based on Point-of-Sale Data

Anecdotal evidence has shown that retail price promotions can help small and medium-sized retailers enhance their sales, and thus, retail profits. However, most marketing managers usually stop a promotion after a certain duration. This study aims to explain why these retailers discontinue their price promotion. Our approach posits that the promotion’s overall contributions to the total retail profit progressively diminish with time. We present a theoretical framework to explain the relationship between duration and profit effects of price promotions and propose a statistical model to empirically examine this framework using point-of-sale (POS) data. Our findings provide empirical support that the overall profit effects of price promotions have a downward trend with elapsed time, upholding the hypothesis. The results are helpful for marketers to understand how price promotions dynamically influence retail profits and when the promotion should be terminated

Consumer Behavior Analysis by Graph Mining Technique (post print version)

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

The Future Direction of New Computing Environment for Exabyte Data in the Business World

With the rapid spread of the Internet and the computerization of trading a huge amount of data on the Internet and of transaction database in enterprises has been accumulated. The purpose of this paper is to explain the significance of the technology to process of exabyte-scale data and presents the business application, CODIRO, which will make it possible to integrate various types of large scale data. CODIRO is a consumer research system which discovers new knowledge by integrating the huge amount of different types of data both on the Internet and within companies. This paper will demonstrate the business implications for exabyte-scale information technology research, by explaining an example of the analysis of the sales effectiveness of television commercials using CODIRO.2005 IEEE/IPSJ International Symposium on Applications and the Internet Workshops (SAINT 2005 Workshops), 31 January - 4 February 2005, Trento, Italy

Pricing system for seeking optimal prices in the diet foods market

The purpose of this study is to introduce a case study on the application of data mining technology to the matter of pricing in business, and to clarify the latent risks contained in that process. In this paper, we have used data mining technology to analyze the purchase history data of customers for the purpose of discovering the price pattern that maximizes store profits. We describe the processes from data analysis to implementing a business plan, and we consider the risks involved in the business application of data mining.2008 IEEE International Conference on Systems, Man, and Cybernetics, October 12-15, 200