Application of data mining techniques in customer realationship management for an automobile company

No Thumbnail Available
Tang A.Y.C.
Azami N.H.
Osman N.
Journal Title
Journal ISSN
Volume Title
Research Projects
Organizational Units
Journal Issue
This work analyzes well-known DM techniques in Weka workbench, and reports the simulation results of applying four selected DM techniques and classifiers in the open source workbench to the Customer Relationship Management (CRM) problem in an automobile enterprise. It is proposed that data mining techniques to be used in aiding the salesperson and management of the enterprise for effective decision making. This approach was applied to 500 preprocessed records out of 2000 raw data sets for the past 5 years. Simulation results show that the large volume of customer historical data can play a value-added role for enterprise development in a way that the mined data helps them to study customer behavior so that personalized services can be provided. This paper also discusses the evaluation results of the four classifiers used in mining the customer data. � 2011 IEEE.
classifiers , data mining , evaluation , Weka , Behavioral research , Classifiers , Customer satisfaction , Decision making , Information technology , Open systems , Public relations , Sales , Automobile companies , Customer behavior , Customer data , Customer relationship management , Data mining techniques , Data sets , Enterprise development , evaluation , Evaluation results , Historical data , Open sources , Personalized service , Weka , Data mining