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SeqRFM: Fast RFM analysis in sequence data
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AI
DOI：10.1016/j.ins.2025.122652.png)
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Abstract
En 中文
In recent years, data mining technologies have been well applied to many domains, including e-commerce. In customer relationship management (CRM), the Recency-Frequency-Monetary (RFM) analysis model is one of the most effective approaches to increase the profits of major enterprises. 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, the chronological order of transactions reflects customer behavior and preferences. To address these challenges, we develop an effective algorithm called SeqRFM, which combines sequential pattern mining with RFM models. SeqRFM considers each customer's R, F, and M scores to represent the significance of the customer and identifies sequences with high recency, high frequency, and high monetary value. A series of experiments demonstrates the superiority and effectiveness of the SeqRFM algorithm compared to the most advanced RFM algorithms based on sequential pattern mining. Moreover, another algorithm named MSeqRFM is developed to compress the result of SeqRFM. The experiments demonstrate the effectiveness of MSeqRFM in compressing sequences. The source code and datasets are available at GitHub https://github.com/ DSI-Lab1/SeqRFM.
Keywords:
e-commerce
Customer relationship management
RFM model
Sequential pattern mining
Journal
IF:
6.8
Papers:
540
Citations:
6.2W
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