Author:
Chieh-Yuan Tsai
Affiliation:
Yuan Ze University, Taiwan
Keyword(s):
e-Commerce, Customer Relationship Management, Data Mining, LabelSOM, Fuzzy Decision Tree.
Related
Ontology
Subjects/Areas/Topics:
Business Analytics
;
Business and Social Applications
;
Communication and Software Technologies and Architectures
;
CRM and Business Solutions
;
Data Engineering
;
Data Warehouses and Data Mining
;
e-Business
;
e-Marketing and Consumer Behaviour
;
Enterprise Information Systems
;
Enterprise Software Technologies
;
Global Communication Information Systems and Services
;
Software Engineering
;
Telecommunications
Abstract:
Credit card is one of the most popular e-payment approaches in current online e-commerce. To consolidate valuable customers, card issuers invest a lot of money to maintain good relationship with their customers. Although several efforts have been done in studying card usage motivation, few researches emphasize on credit card usage behaviour analysis when time periods change from t to t+1. To address this issue, an integrated data mining approach is proposed in this paper. First, the customer profile and their transaction data at time period t are retrieved from databases. Second, a LabelSOM neural network groups customers into segments and identify critical characteristics for each group. Third, a fuzzy decision tree algorithm is used to construct usage behaviour rules of interesting customer groups. Finally, these rules are used to analysis the behaviour changes between time periods t and t+1. An implementation case using a practical credit card database provided by a commercial ban
k in Taiwan is illustrated to show the benefits of the proposed framework.
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