A new version of the rule induction system LERS
Fundamenta Informaticae
E-business: roadmap for success
E-business: roadmap for success
Data mining: concepts and techniques
Data mining: concepts and techniques
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Structural equation model for effective CRM of digital content industry
Expert Systems with Applications: An International Journal
Electronic Commerce Research and Applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Customer segmentation of multiple category data in e-commerce using a soft-clustering approach
Electronic Commerce Research and Applications
Segmentation of telecom customers based on customer value by decision tree model
Expert Systems with Applications: An International Journal
A case study of applying LRFM model in market segmentation of a children's dental clinic
Expert Systems with Applications: An International Journal
Segmenting customers by transaction data with concept hierarchy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Electronic Commerce Research and Applications
Group RFM analysis as a novel framework to discover better customer consumption behavior
Expert Systems with Applications: An International Journal
Direct marketing decision support through predictive customer response modeling
Decision Support Systems
Knowledge discovery of weighted RFM sequential patterns from customer sequence databases
Journal of Systems and Software
Review: Soft computing applications in customer segmentation: State-of-art review and critique
Expert Systems with Applications: An International Journal
Customer behavior analysis using rough set approach
Journal of Theoretical and Applied Electronic Commerce Research
Expert Systems with Applications: An International Journal
A new evolution model for B2C e-commerce market
Information Technology and Management
| Hi-index | 12.06 |
Data mining is a powerful new technique to help companies mining the patterns and trends in their customers data, then to drive improved customer relationships, and it is one of well-known tools given to customer relationship management (CRM). However, there are some drawbacks for data mining tool, such as neural networks has long training times and genetic algorithm is brute computing method. This study proposes a new procedure, joining quantitative value of RFM attributes and K-means algorithm into rough set theory (RS theory), to extract meaning rules, and it can effectively improve these drawbacks. Three purposes involved in this study in the following: (1) discretize continuous attributes to enhance the rough sets algorithm; (2) cluster customer value as output (customer loyalty) that is partitioned into 3, 5 and 7 classes based on subjective view, then see which class is the best in accuracy rate; and (3) find out the characteristic of customer in order to strengthen CRM. A practical collected C-company dataset in Taiwan's electronic industry is employed in empirical case study to illustrate the proposed procedure. Referring to [Hughes, A. M. (1994). Strategic database marketing. Chicago: Probus Publishing Company], this study firstly utilizes RFM model to yield quantitative value as input attributes; next, uses K-means algorithm to cluster customer value; finally, employs rough sets (the LEM2 algorithm) to mine classification rules that help enterprises driving an excellent CRM. In analysis of the empirical results, the proposed procedure outperforms the methods listed in terms of accuracy rate regardless of 3, 5 and 7 classes on output, and generates understandable decision rules.