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Big Data analysis has played a great role in extracting valuable insights from a vast amount of data, which can help companies make better business decisions and gain significant benefits. This paper investigates a Big Data based solution which applies the K-means clustering algorithm integrated with RFM analysis in a Spark processing pipeline for customer segmentation. Spark plays as a primary tool to deploy the entire experimentation process into a pipeline for automation, reusability, scalability, and enhanced computational power and speed for large datasets. The experimental results indicate the algorithm’s effectiveness in consumer segmentation with the proposed Big Data based model.

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An Automatic Machine Learning Based Customer Segmentation Model with RFM Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Tran Thi Xuan,
  • Nguyen Thai Hoc

摘要

Big Data analysis has played a great role in extracting valuable insights from a vast amount of data, which can help companies make better business decisions and gain significant benefits. This paper investigates a Big Data based solution which applies the K-means clustering algorithm integrated with RFM analysis in a Spark processing pipeline for customer segmentation. Spark plays as a primary tool to deploy the entire experimentation process into a pipeline for automation, reusability, scalability, and enhanced computational power and speed for large datasets. The experimental results indicate the algorithm’s effectiveness in consumer segmentation with the proposed Big Data based model.