High Value Customer Profile, Using Data Mining Techniques

1311 Words May 15th, 2016 6 Pages
Abstract
Studying about customer segmentation and creating a customer ranking plan has attracted more attention in recent years. In this regard, this project tries on providing a methodology for customer segmentation depending on their value driver parameters which will be extracted from transaction data. The objectives of this project is to identify the High Value Customer Profile, using data mining techniques such as classification and clustering approaches. In the first phase, the data will be cleaned and patterns will be developed. In the second phase, the data will be profiled and clustered to identify High Value Customer Profile. Background and the Problem Domain
Companies are increasingly interested in identifying customers who generate the highest revenues. Typically, customers are considered as isolated individuals whose purchasing behavior depends exclusively on their own characteristics such as previous buying behavior and demographics etc. Based on these features the customers are categorized as “High Value Customers”. High Value Customers can be defined as clients on whom the profitability or existence of a company depend on. Without these customers, a company loses its competitive edge and market share, resulting in reduced performance over the rivals. In worst case scenario, loss of High Value Customers could result in the collapse of an organization. Therefore, these customers should be given extremely personalized care and attention.
So today, many…
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