Analysis of Customer Behavior using Clustering and Association Rules

Abstract

The analysis of customer behavior is used to maintain good relationship with customers. It maximizes the customer satisfaction. We can also improve customer loyalty and retention. The aim of this paper is to develop a very useful trend for launching products with configurations for customers of different gender based on past transactions. Based on the previous transactions of the customers, prediction is done and data is estimated with the help of clustering and association rules. This paper proposes an effective method to extract knowledge from transactions records which is very useful for increasing the sales. Customer details are segmented using k-means and then Apriori algorithm is applied to identify customer behavior. This is followed by the identification of product associations within segments. This paper aims to develop a new trend and launch a new series of products using the previous transactions of the customers. General Terms Clustering analysis, Association rules

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