Discovering valuable frequent patterns based on RFM analysis without customer identification information

RFM analysis and market basket analysis (i.e., frequent pattern mining) are two most important tasks in database marketing. Based on customers’ historical purchasing behavior, RFM analysis can identify a valuable customer group, while market basket analysis can find interesting purchasing patterns. Previous studies reveal that recency, frequency and monetary (RFM) analysis and frequent pattern mining can be successfully integrated to discover valuable patterns, denoted as RFM-customer-patterns. However, since many retailers record transactions without collecting customer information, the RFM-customer-patterns cannot be discovered by existing approaches. Therefore, the aim of this study was to define the RFM-pattern and develop a novel algorithm to discover complete set of RFM-patterns that can approximate the set of RFM-customer-patterns without customer identification information. Instead of evaluating values of patterns from a customer point of view, this study directly measures pattern ratings by considering RFM features. An RFM-pattern is defined as a pattern that is not only occurs frequently, but involves a recent purchase and a higher percentage of revenue. This study also proposes a tree structure, called an RFM-pattern-tree, to compress and store entire transactional database, and develops a pattern growth-based algorithm, called RFMP-growth, to discover all the RFM-patterns in an RFM-pattern-tree. Experimental results show that the proposed approach is efficient and can effectively discover the greater part of RFM-customer-patterns.