Data Mining in Customer Profitability Analysis

Abstract

The aim of this paper is a presentation of data mining model that could be used for the measurement of current and forecasting of the future customer profitability. The purpose of this model is to forecast activities of individual customers in the future, and to value that company could expect in doing business with them. Modern customer profitability analysis shows that product cost is just one part of the relation enterprise-customer. A general framework for defining customer profitability, besides pure financial items, has to include a lot of non-linear and non-financial elements. Data mining methods do not use conventional learning methods that suffer from imperfections such as inability to explicitly transfer the knowledge from experts to machines or nonexistence of experts' will for knowledge transfer. Data mining can identify and adopt patterns and rules that exist in historical data stored in databases and/or data warehouses. It can work equally well with nonlinear and nonfinancial elements of environment which have influence on profitability results. Neural networks approved their capability for approximate description of any continuous function. Together with robust methods of genetic algorithms used in the learning process of networks, they make a good choice in the process of selecting methods for forecasting customer profitability. The proposed model for the forecasting of the customer profitability uses two data mining methods: neural networks and genetic algorithm. The paper presents results of empirical research related to forecasting of customer determination to specific segment made in a company which produces and distributes products like dry fruits, nuts, seeds and cereals for the market of South-East Europe.