Customer segmentation is increasingly needed in a context where customer interests are vital for companies to survive. This study proposes the use of the weighted RFM (Recency, Frequency, Monetary) supported by data mining techniques and the Analytic Hierarchy Process (AHP), to classify the customers according to their lifetime value (CLV). The customer segments obtained can be used to boost marketing strategies, as these segments enable to differentiate the customers. Each segment of customers is described by a set of rules based on the customers’ purchasing patterns. The methodology developed is validated by using a real case study, i.e. a food industry company, whose core business is the production of biscuits.
Applying Data Mining Techniques and Analytic Hierarchy Process to the Food Industry: Estimating Customer Lifetime Value
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