Designing customer-oriented catalogs in e-CRM using an effective self-adaptive genetic algorithm

Authors:
Iraj Mahdavi;Mahyar Movahednejad;Fereydoun Adbesh
Affiliations:
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran;Department of Industrial and Manufacturing Engineering, Wayne State University, Detroit, MI 48202, USA;Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Analysis of customer interactions for electronic customer relationship management (e-CRM) can be performed by way of using data mining (DM), optimization methods, or combined approaches. The microeconomic framework for data mining addresses maximizing the overall utility of an enterprise where transaction of a customer is a function of the data available on that customer. In this paper, we investigate an alternative problem formulation for the catalog segmentation problem. Moreover, a self-adaptive genetic algorithm has been developed to solve the problem. It includes clever features to avoid getting trapped in a local optimum. The results of an extensive computational study using real and synthetic data sets show the performance of the algorithm. In comparison with classical catalog segmentation algorithms, the proposed approach achieves better performance in Fitness and CPU-time.