Predictive Retention Modeling

Predictive Retention Modeling is a computational analytical framework that distinguishes itself by leveraging historical behavioral and demographic data to forecast the likelihood of individual member retention or churn within a defined period, using statistical and machine learning techniques.

110

Publications

5.2K

Citations

301

Authors

165

Institutions

Publications per year

2017–2026

44

Authors

301

  1. CP
    C.T. Peng

    University of California, San Francisco, United States

    5 publications · 174 citations

    5

    h-index

  2. RV
    R.M. Villanueva-Camañas

    Universitat de València, Spain

    3 publications · 41 citations

    3

    h-index

  3. SS
    S. Sagrado

    Universitat de València, Spain

    3 publications · 41 citations

    3

    h-index

  4. YM
    Yolanda Martín‐Biosca

    Universitat de València, Spain

    3 publications · 41 citations

    3

    h-index

  5. MM
    M.J. Medina-Hernández

    Universitat de València, Spain

    3 publications · 41 citations

    3

    h-index

1–5 of 301

Venues

  1. Journal of Chromatography A

    8 publications · 261 citations

    8

    h-index

  2. 3

    h-index

  3. IEEE Access

    2 publications · 67 citations

    2

    h-index

  4. Industrial Marketing Management

    2 publications · 185 citations

    2

    h-index

  5. Expert Systems with Applications

    2 publications · 324 citations

    2

    h-index

1–5