Variable selection in clustering for marketing segmentation using genetic algorithms

Marketing segmentation is widely used for targeting a smaller market and is useful for decision makers to reach all customers effectively with one basic marketing mix. Although clustering algorithms is popularly employed in dealing with this problem, it cannot be useful unless irrelevant variables are removed because irrelevant variables will distort the clustering structure and make the results useless. In this paper, genetic algorithms (GA) is used for variable selection and for determining the numbers of clusters. A real case of bank data set is used for illustrating the application of marketing segmentation. The results show that variable selection through GA can effectively find the global optimum solution, and the accuracy of the classified model is dramatically increased after clustering.