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
Parents
6
Using data mining to profile TV viewers
William E. Spangler, Mordechai Gal‐Or, Jerrold H. May · Communications of the ACM · 2003 · 43 citations
Business, Communication, Marketing +17
1–5 of 6
20
1
h-index
1
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International Institute for Applied Systems Analysis, Austria
1 publications · 29 citations
1
h-index
1
h-index
International Institute for Applied Systems Analysis, Austria
1 publications · 29 citations
1
h-index
1–5 of 20
15
Graz University of Technology
Graz, Austria
1 h-index
1 publications
29 citations

Duquesne University
Pittsburgh, United States
1 h-index
2 publications
86 citations
Virginia Tech
Blacksburg, United States
1 h-index
4 publications
76 citations

Centre National de la Recherche Scientifique
Paris, France
1 h-index
1 publications
30 citations
International Institute for Applied Systems Analysis
Laxenburg, Austria
1 h-index
3 publications
87 citations
Medical University of Vienna
Vienna, Austria
1 h-index
1 publications
29 citations
Institut Polytechnique de Paris
Palaiseau, France
1 h-index
1 publications
30 citations
Orange (France)
Paris, France
1 h-index
2 publications
60 citations
1–8 of 15
Repository · 1 publications · 17 citations
1
h-index
1 publications · 30 citations
1
h-index
1 publications · 19 citations
1
h-index
1 publications · 43 citations
1
h-index
ERL · 1 publications · 29 citations
1
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1–5