Toward Targeted Mining of RFM Patterns

Xiaoye Chen, Wensheng Gan, Zefeng Chen, Jian Zhu, Ruichu Cai, Philip S. Yu

Abstract: In today’s era of information overload, leveraging data mining techniques to understand and analyze customer behavior has become essential for businesses. Among these techniques, the recency, frequency, and monetary value analysis model serves as a powerful tool for customer segmentation, enabling companies to identify high-value customers. However, traditional recency, frequency, and monetary (RFM) models do not focus on user-specific targets, often struggling to meet the increasing demands for personalization and efficiency. To address this challenge, this article introduces the concept of target RFM patterns, which must satisfy the three dimensions of recency, frequency, and utility while aligning with user interests. Based on this concept, we formulate the problem of mining target RFM patterns. More importantly, we define a mining order, called TaRFM order, and propose an efficient algorithm called TaRFM. This new algorithm is optimized through three pruning strategies based on the TaRFM order, which not only eliminates a significant number of invalid operations, thereby reducing pattern generation, but also accurately extracts all TaRFM patterns without requiring postprocessing techniques. Finally, extensive experiments conducted on multiple datasets demonstrate the accuracy and efficiency of the TaRFM algorithm.

External IDs:dblp:journals/tnn/ChenGCZCY25