- Research Article
- 10.1080/0965254x.2026.2667800
Revitalizing segmentation in strategic marketing: introducing a special issue driven by technological, empirical, and methodological advancements
- May 08, 2026
- Journal of Strategic Marketing
- Niklas Mergner + 2 more +2
ABSTRACT Segmentation has long been a cornerstone of strategic marketing, grounded in the recognition that markets are inherently heterogeneous and that effective strategies must reflect meaningful differences in customer needs and responses. However, its relevance and practical applicability have been widely debated in recent years, alongside the emergence of numerous alternative approaches. Historically, advances in computational power have enabled increasingly sophisticated segmentation techniques. More recently, rapid progress in artificial intelligence (AI), data availability, and methods has opened up new possibilities while also raising the question of whether segmentation in its traditional form may become obsolete. Against this backdrop, this Special Issue on the revitalization of segmentation was conceived. It aims to encourage scholars to reassess established approaches, incorporate technological developments, and develop novel frameworks. The contributions advance theory guided and data enabled segmentation to improve targeting, pricing, and the estimation of segment specific effects. Modern machine learning techniques help uncover latent structures in high dimensional data, enhancing segment formation and predictive performance, while emerging approaches leverage unstructured data such as text to identify segment level drivers and strategic groups. At the same time, the growing reliance on data intensive methods highlights the importance of data quality, validation, and governance to avoid misguided decisions. Overall, the contributions in this Special Issue demonstrate that segmentation is far from obsolete. On the contrary, the opportunities for segmentation research are greater than ever, calling on researchers to revitalize the field.
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