Predictive Conversion Modeling

Predictive Conversion Modeling is a computational framework within conversion rate optimization that employs statistical and machine learning techniques to forecast the likelihood of user conversion by analyzing behavioral and contextual data, distinguishing itself through its focus on anticipatory analytics rather than reactive performance measurement.

43

Publications

2.4K

Citations

149

Authors

50

Institutions

Publications per year

2017–2026

28

Authors

149

  1. HW
    Hong Wen

    University of Electronic Science and Technology of China, China

    3 publications · 146 citations

    3

    h-index

  2. JP
    Junwei Pan

    Tencent (China), China

    3 publications · 92 citations

    3

    h-index

  3. WB
    Wentian Bao

    Alibaba Group (China), China

    3 publications · 146 citations

    3

    h-index

  4. AB
    Andrew B. Whinston

    The University of Texas at Austin, United States

    2 publications · 248 citations

    2

    h-index

  5. BC
    Bo Cheng

    University of North Carolina at Chapel Hill, United States

    2 publications · 242 citations

    2

    h-index

1–5 of 149

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 30 citations

    2

    h-index

  2. Management Science

    2 publications · 736 citations

    2

    h-index

  3. Machine Learning

    2 publications · 102 citations

    2

    h-index

  4. Journal of Pharmaceutical Sciences

    1 publications · 13 citations

    1

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 39 citations

    1

    h-index

1–5