Conversion Rate Forecasting

Conversion Rate Forecasting is a predictive analytics method that estimates the future proportion of users or interactions leading to a desired outcome within digital engagement platforms, distinguished by its focus on modeling temporal patterns and behavioral indicators to anticipate conversion performance.

24

Publications

1K

Citations

75

Authors

35

Institutions

Publications per year

2017–2026

11

Authors

75

  1. JY
    Jinyoung Yeo

    Yonsei University, South Korea

    2 publications · 71 citations

    2

    h-index

  2. MK
    Manfred K. Warmuth

    University of California, Santa Cruz, United States

    2 publications · 102 citations

    2

    h-index

  3. DP
    David P. Helmbold

    University of California, Santa Cruz, United States

    2 publications · 102 citations

    2

    h-index

  4. SK
    Sungchul Kim

    Adobe Systems (United States), United States

    2 publications · 71 citations

    2

    h-index

  5. SH
    Seung-won Hwang

    Pohang University of Science and Technology, South Korea

    2 publications · 71 citations

    2

    h-index

1–5 of 75

Venues

  1. Machine Learning

    2 publications · 102 citations

    2

    h-index

  2. Aesthetic Surgery Journal

    ASJ · 1 publications · 12 citations

    1

    h-index

  3. Journal of Retailing and Consumer Services

    1 publications · 85 citations

    1

    h-index

  4. Management Science

    1 publications · 205 citations

    1

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 39 citations

    1

    h-index

1–5