Data-driven Benefit Segmentation
Data-Driven Benefit Segmentation is a methodological framework within market segmentation that uses empirical data and analytical techniques to identify and categorize consumers based on distinct benefits they seek from products or services, distinguishing it from intuition-based or assumption-driven approaches by its reliance on observable and measurable consumer behavior and preference data.
16
Publications
1K
Citations
27
Authors
13
Institutions
Publications per year
2017–2026
2
Parents
16
Benefit segmentation for fund raisers
James W. Harvey · Journal of the Academy of Marketing Science · 1990 · 94 citations
Business, Finance, Fundraising +8
Benefit Segmentation for Fund Raisers
Jim Harvey · Journal of the Academy of Marketing Science · 1990 · 93 citations
Business, Finance, Fundraising +8
1–5 of 16
27
4
h-index
University of North Carolina at Chapel Hill, United States
3 publications · 261 citations
3
h-index
1
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1
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1
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1–5 of 27
13
George Mason University
Fairfax, United States
2 h-index
2 publications
187 citations
Aoyama Gakuin University
Tokyo, Japan
1 h-index
3 publications
48 citations
KU Leuven
Leuven, Belgium
1 h-index
1 publications
31 citations
Arizona State University
Tempe, United States
1 h-index
1 publications
15 citations

University of Liverpool
Liverpool, United Kingdom
1 h-index
1 publications
89 citations
Finland University
Helsinki, Finland
1 h-index
3 publications
276 citations

University of Eastern Finland
Joensuu, Finland
1 h-index
3 publications
276 citations
Georgia State University
Atlanta, United States
1 h-index
2 publications
30 citations
1–8 of 13
3 publications · 208 citations
3
h-index
2 publications · 230 citations
2
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JAR · 1 publications · 64 citations
1
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Repository · 1 publications · 14 citations
1
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1 publications · 89 citations
1
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1–5