Special Issue Article Open Access

Employing Descriptive Methods for Customer Segmentation

Abstract

Customer segmentation is the process of finding homogenous sub-groups within a heterogeneous aggregate market. This approach is used in direct marketing to target and focus on increasingly well-defined and profitable market segments. The process of segmentation begins with observing customer actions and continues with learning about the demographic and psychographic characteristics of these customers. This intelligence can be made available to the customer facing teams which may be a great tool to increase cross selling and up selling capability of a company. Data Mining is the process of discovering knowledge from huge volumes of data. It is widely used in customer segmentation where various classes can be formed based on the customers buying behaviour. This paper deals with two such methods: K-Means and Hierarchical Clustering. K-Means groups the customers into K-clusters. It is an Iterative algorithm which repeatedly calculates the distance and reforms the centroids based on the distance. The Hierarchical clustering employed here uses divisive method where it begins with just only one cluster that contains all sample data and it slits into two or more clusters that have higher dissimilarity between them. Both the techniques were experimented on NORTHWIND database and the results were analyzed based on the execution time and iteration count. The results concurred that KMeans performs well comparably to Hierarchical clustering.

R.Sabitha, Dr.S.Karthik

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