Interpretable Churn Models

Interpretable Churn Models is a class of predictive modeling frameworks in customer churn prediction that belong to the broader category of machine learning models but are specifically designed to provide transparent, human-understandable representations of the factors and decision logic influencing churn predictions.

11

Publications

803

Citations

39

Authors

29

Institutions

Publications per year

2017–2026

6

Authors

39

  1. KW
    Koen W. De Bock

    Institut d'Economie Scientifique Et de Gestion, France

    2 publications · 115 citations

    2

    h-index

  2. WV
    Wouter Verbeke

    KU Leuven, Belgium

    1 publications · 357 citations

    1

    h-index

  3. XS
    Xiaolin Shi

    University of Michigan, United States

    1 publications · 84 citations

    1

    h-index

  4. CY
    Carl Yang

    Emory University, United States

    1 publications · 84 citations

    1

    h-index

  5. MA
    Mohd Afjal

    Vellore Institute of Technology University, India

    1 publications · 26 citations

    1

    h-index

1–5 of 39

Venues

  1. Expert Systems with Applications

    2 publications · 425 citations

    2

    h-index

  2. Journal of Applied Microeconometrics

    1 publications · 38 citations

    1

    h-index

  3. Omega

    1 publications · 60 citations

    1

    h-index

  4. Decision Support Systems

    1 publications · 47 citations

    1

    h-index

  5. Infolitika Journal of Data Science

    1 publications · 26 citations

    1

    h-index

1–5