Data-driven Audience Segmentation

Data-Driven Audience Segmentation is a methodological approach to audience segmentation that systematically categorizes target audiences into distinct groups based on empirical data and quantitative analysis, distinguished from heuristic or intuition-based segmentation by its reliance on measurable behavioral, demographic, and psychographic data derived from statistical modeling, machine learning, or algorithmic processing.

6

Publications

166

Citations

20

Authors

15

Institutions

Publications per year

2017–2026

4

Authors

20

  1. PS
    Pete S. Manasantivongs

    Virginia Tech, United States

    1 publications · 19 citations

    1

    h-index

  2. EX
    Eva X. Zhong

    Virginia Tech, United States

    1 publications · 19 citations

    1

    h-index

  3. SE
    Sibel Eker

    International Institute for Applied Systems Analysis, Austria

    1 publications · 29 citations

    1

    h-index

  4. AM
    Ankur Mehra

    Film Independent, United States

    1 publications · 28 citations

    1

    h-index

  5. HV
    Hugo Valin

    International Institute for Applied Systems Analysis, Austria

    1 publications · 29 citations

    1

    h-index

1–5 of 20

Venues

  1. SSRN Electronic Journal

    Repository · 1 publications · 17 citations

    1

    h-index

  2. New Media & Society

    1 publications · 30 citations

    1

    h-index

  3. Expert Systems with Applications

    1 publications · 19 citations

    1

    h-index

  4. Communications of the ACM

    1 publications · 43 citations

    1

    h-index

  5. Environmental Research Letters

    ERL · 1 publications · 29 citations

    1

    h-index

1–5