CLV Based Product Recommendation Integrating AHP and data mining for product recommendation based on customer lifetime value Shaheen Syed Department of Information and Computing Sciences

Overview General info Related literature PDD Example Performance Conclusion Department of Information and Computing Sciences

The method Integrating AHP and data mining for product recommendation based on customer lifetime value. – AHP (Analytical Hierarchy Process) – Data mining (association rule mining) – Based on customer lifetime value (cluster customers by CLV) Recency: Period since last purchase Frequency: Number of purchases made in certain time period Monetary: Money spent in certain time period Department of Information and Computing Sciences

Related Literature Department of Information and Computing Sciences Method AHPData Mining K-means clustering Collaborative Filtering CLV

Phases Department of Information and Computing Sciences

Authors Duen-Ren liu Professor of the institute of Information Management BS, MS in CS PHD in CS Ya-Yueh Shih PHD student of the institute of Information Management BS, MS in Information Management Department of Information and Computing Sciences

PDD – Evaluate RFM by AHP Department of Information and Computing Sciences

PDD – Preprocess data Department of Information and Computing Sciences

Cluster Customers by CLV Department of Information and Computing Sciences

PDD - Recommend Products Department of Information and Computing Sciences

Example ‘PCHardware’ Department of Information and Computing Sciences Evaluate RFM by CLV Recency(Period since last purchase)0.731 Frequency(number of purchases made0.181 in certain time period) Monetary(money spent in certain time period)0.081

Example ‘PCHardware’ Department of Information and Computing Sciences Cluster customers by CLV – K-means clustering

Example ‘PCHardware’ Department of Information and Computing Sciences Recommend Products – Association Rule mining Cluster 1: Mainbord -> memory Cluster 2: CPU -> Cooler Cluster n: X -> Y

Performance Department of Information and Computing Sciences Experimentally tested – R=F=M – CB – No clustering – Provided better performance (F1-metrics)

Conclusion Department of Information and Computing Sciences Method needs some extra steps than traditional methods but yields better recommendation – Higher revenue – Higher customer loyalty