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

• Published in: Expert systems with applications Vol. 38; no. 1; pp. 631 - 639
• Main Authors: Mahdavi, Iraj, Movahednejad, Mahyar, Adbesh, Fereydoun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2011
• Subjects: Algorithms; Catalog segmentation; Data mining; e-CRM; Expert systems; Genetic algorithms; Mathematical analysis; Optimization; Segmentation; Self-adaptive genetic algorithms; e-CRM; Data mining; Self-adaptive genetic algorithms; Catalog segmentation
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2010.07.013

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.