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

Authors

27

  1. MW
    Michel Wedel

    University of Groningen, Netherlands

    4 publications · 377 citations

    4

    h-index

  2. JE
    Jan‐Benedict E.M. Steenkamp

    University of North Carolina at Chapel Hill, United States

    3 publications · 261 citations

    3

    h-index

  3. JW
    James W. Harvey

    George Mason University, United States

    1 publications · 94 citations

    1

    h-index

  4. DA
    Deepak Arunachalam

    University of Sheffield, United Kingdom

    1 publications · 89 citations

    1

    h-index

  5. KL
    Kent L. Granzin

    University of Utah, United States

    1 publications · 21 citations

    1

    h-index

1–5 of 27

Venues

  1. Journal of the Academy of Marketing Science

    3 publications · 208 citations

    3

    h-index

  2. Journal of Marketing Research

    2 publications · 230 citations

    2

    h-index

  3. Journal of Advertising Research

    JAR · 1 publications · 64 citations

    1

    h-index

  4. PubMed

    Repository · 1 publications · 14 citations

    1

    h-index

  5. Expert Systems with Applications

    1 publications · 89 citations

    1

    h-index

1–5