Fast RFM Analysis in Sequence Data

In recent years, data mining technology has been thriving and is capable of being well applied to e-commerce. In customer relationship management (CRM), the RFM analysis model is one of the most effective approaches to ensure that major enterprises obtain more profits. However, with the rapid development of e-commerce, the diversity and abundance of e-commerce data pose a challenge to mining efficiency. Moreover, in actual market transactions, there is a chronological order of transactions. To address these challenges, we have developed an effective algorithm called SeqRFM, combining sequential pattern mining (SPM) with RFM models. SeqRFM contributes to the customer’s recency (R), frequency (F), and monetary (M) scores to represent the significance of the customers and identifies the sequences with high recency, high frequency, and high monetary value. A series of experiments demonstrate the superiority and effectiveness of the algorithm compared to the most advanced RFM algorithms on sequence data. The source code and datasets are available at GitHub: https://github.com/DSI-Lab1/SeqRFM.