Notice of RetractionA customer identification method based on genetic algorithm and customer value model: Theoretical model and empirical application

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Customer value (CV) identification is the basis for customer relationship retention strategic decision-making, and both variables and criteria for value identification need to reflect strategic intentions of enterprise customer resources. In this paper, we analyzed the limitations of one of the widely-used methods, K-means cluster method. Then, we proposed a modified method for customer value identification, and this method combined the technique of selecting the optimal K-value based on genetic algorithm with the technology of setting weights for customer value variables. This paper also systematically described the key methodologies and implementation procedures for design of evaluation system, index weight setup, value measurement and evaluation, and genetic cluster analysis. Furthermore, we used a data set related to customer relationship retention collected from a real-life enterprise to demonstrate the application of the proposed method. Finally, we compared the solutions of genetic cluster algorithm with K-means.