Dynamic Capabilities for Real‐Time Customer Segmentation: Insights From a Sequential Mixed‐Methods Study

Conventional approaches to customer segmentation, static and retrospective by nature, fail to adequately capture rapid consumer shifts in dynamic market environments. Drawing from dynamic capabilities theory, this article reconceptualizes segmentation as an adaptive, capability‐driven practice rather than as a passive classification exercise. Using a sequential mixed‐methods approach comprising qualitative insights from industry practitioners and quantitative validation via structural equation modelling, this study affirms and extends the dynamic capabilities of sensing, seizing, and reconfiguring. The findings reveal that customer understanding, market scanning, and data integration capabilities (sensing) drive the capability to process big data, which, in turn, improves data interpretation competency and strengthens agile strategic capability (seizing), as well as enhancing data‐driven positioning effectiveness to support real‐time segmentation reconfiguration (reconfiguring). In contrast, data and information processing overload and organizational inertia showed the hypothesized directional weakening of these capability pathways, though none of the three interaction effects reached statistical significance, which positions both as directionally suggested rather than statistically confirmed constraints on the transition from passive to adaptive segmentation practices. Marketers can thus leverage the actionable insights from this study to implement real‐time segmentation and sustain competitive advantage in dynamic, rapidly evolving markets.