Predicting customer purchase in an online
retail business, a Data Mining approach.

Abstract

Identifying segments of customers and their behavioral patterns over different time intervals, is an important application for businesses, especially in case of the last tier of the online retail chain which is concerned with “electronic Business-to-Customer relationship”
(B2C) . This is particularly important in dynamic and ever-changing markets, where customers are driven by ever changing market competition and demands. This could lead to the prediction of ‘churn’, or which customers are leaving the company’s loyalty. Also, the provision of customized service to the customers is vital for a company to establish long lasting and pleasant relationship with consumers. It has also been observed that keeping old customers generates more profit than attracting new ones. So, customer retention is a big factor too. So, there is
always a trade-off between customer benefits and transaction costs, which has to be optimized
by the managers.
The purpose of this thesis is to study, implement and analyze various Data-mining tools and techniques and then do an analysis of the sample / raw data to obtain a meaningful interpretation. Some of the data mining algorithms I have used, are a vector quantization based
clustering algorithm, and then an ‘Apriori’ based Association rule mining algorithm. The first one is aimed at a meaningful segregation of the various customers based on their RFM values,
while the latter algorithm tries to find out relationships and patterns among the purchases
made by the customer, over several transactions.