1. 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

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

4. 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

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

6. Phases Department of Information and Computing Sciences

7. 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

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

9. PDD – Preprocess data Department of Information and Computing Sciences

10. Cluster Customers by CLV Department of Information and Computing Sciences

11. PDD - Recommend Products Department of Information and Computing Sciences

12. 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

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

14. 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

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

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