Rfm Feature Engineering

RFM Feature Engineering is a methodological construct in customer analytics that operationalizes customer behavior by transforming raw transactional data into the composite RFM (Recency, Frequency, Monetary) features, distinguishing it from general feature engineering through its specific theoretical grounding in behavioral scoring and temporal purchasing dynamics.

23

Publications

1.7K

Citations

77

Authors

37

Institutions

Publications per year

2017–2026

12

Authors

77

  1. YH
    Ya‐Han Hu

    National Chung Cheng University, Taiwan

    2 publications · 127 citations

    2

    h-index

  2. HC
    Huan-Ming Chuang

    National Yunlin University of Science and Technology, Taiwan

    1 publications · 28 citations

    1

    h-index

  3. BS
    Boyu Shen

    China Pharmaceutical University, China

    1 publications · 33 citations

    1

    h-index

  4. XN
    Xiaxia Niu

    Inner Mongolia University, China

    1 publications · 32 citations

    1

    h-index

  5. ST
    Sang‐Bing Tsai

    University of Electronic Science and Technology of China, China

    1 publications · 32 citations

    1

    h-index

1–5 of 77

Venues

  1. Knowledge-Based Systems

    2 publications · 123 citations

    2

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  2. Expert Systems with Applications

    2 publications · 495 citations

    2

    h-index

  3. 1

    h-index

  4. Business Systems Research Journal

    BSR · 1 publications · 26 citations

    1

    h-index

  5. The 2nd International Conference on Computing and Data Science

    Conference · 1 publications · 33 citations

    1

    h-index

1–5