Abstract
Nowadays a thorough understanding of the business processes and the organization`s customers is the essential point to survive in the market competition. In this study, a new idea was applied to import customer purchased basket data into data mining computations. First, the products were divided into families, and we assigned a numerical code for each product in the family. The sum of these assigned numbers indicates the status of the basket. After this step, the transactions were clustered based on their basket values. Customers are then clustered in each cluster using the RFM method. Using a new fuzzy LP-metric approach and pairwise comparisons, RFM indices were weighted and we obtain customer value per cluster. Then we will proceed by averaging the customer value according to the presence of each customer in different clusters. Then we clustered customers based on customer lifetime value.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
