Predictive Model for Detecting Customer’s Purchasing Behavior Using Data Mining

Abstract

Profitability is one of the most important marketing objectives in a company, so estimating and detecting in advance what a customer will purchase from a defined product portfolio is an important factor. Considering that the company under study maintains a large number of customers and has no clear methodologies for estimating future sales, purpose of the research is to develop a predictive model to detect the sales behavior of a specific customer. For the development of the model, the CRISP-DM method was followed and SPSS was used for variable prediction and exploration. Two predictive models were generated with data mining algorithms from which the one that offered the most reliable results was selected. From the obtained results, special sales campaigns were generated, focusing on those customers and/or products that are most likely to be purchased according to the predictive model.