Evaluation of the Shopping Path to Distinguish Customers Using a RFID Dataset

Authors:
Katsutoshi Yada;Takanobu Nakahara
Affiliations:
Kansai University, Japan;Kansai University, Japan
Venue:
International Journal of Organizational and Collective Intelligence
Year:
2011

Citing 11
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Efficient mining of emerging patterns: discovering trends and differences

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting change in categorical data: mining contrast sets

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling Online Browsing and Path Analysis Using Clickstream Data

Marketing Science
Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models

Decision Support Systems
Efficient serial episode mining with minimal occurrences

Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Path Data in Marketing: An Integrative Framework and Prospectus for Model Building

Marketing Science
Research Note---The Traveling Salesman Goes Shopping: The Systematic Deviations of Grocery Paths from TSP Optimality

Marketing Science
Direct and indirect effects of retail promotions on sales and profits in the do-it-yourself market

Expert Systems with Applications: An International Journal
Modeling complex longitudinal consumer behavior with Dynamic Bayesian networks: an Acquisition Pattern Analysis application

Journal of Intelligent Information Systems
String analysis technique for shopping path in a supermarket

Journal of Intelligent Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, the authors use radio-frequency identification RFID data, which show the position of a shopping cart through an RFID tag attached to the shopping cart. The RFID data contain valuable information for marketing, such as shopping time and distance as well as the number of shelf visits. The authors analyze customers' purchasing behavior and in-store movement information using POS data combined with RFID data. The purpose of this study is to discover a promising shopping path that can distinguish customers' instore movements by sequential pattern analysis using RFID data. These shopping paths are extracted using a pattern mining method. Finally, shopping paths are used in the decision tree analysis to generate the rules that expressed customers' in-store movements and purchasing characteristics. As a result, in this study, the authors propose useful suggestions for more efficient in-store area management.