<![CDATA[This study aims to determine consumer segmentation and targeting strategies for fashion buyers in online marketplaces using the K-Means Clustering method. In the rapidly evolving digital retail landscape, understanding heterogeneous consumer behaviour is essential for designing precise and effective marketing strategies. Using data from 204 respondents, the study incorporates demographic, psychographic, behavioural, and preference-based variables including fashion style orientation, purchasing behaviour, and platform usage to construct meaningful clusters. The analysis produced five distinct consumer segments: Functional Casual Buyers, Trend-Oriented Streetwear Males, Premium Classic Enthusiasts, Trendy Female Shoppers, and Mass-Market Casual–Sporty Consumers. Each segment was examined comprehensively to identify personas, core needs, pain points, and corresponding Online Value Propositions (OVPs). Furthermore, a Maintain–Stop–Start marketing mix strategy based on the 8P framework was developed to guide practical implementation for each cluster. The findings demonstrate that data-driven segmentation enhances targeting accuracy, personalization effectiveness, and strategic alignment in digital fashion commerce. Overall, this research highlights how K-Means Clustering serves as a robust analytical tool for improving competitive positioning and optimizing marketing strategies in increasingly fragmented online fashion markets.]]>
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