Journal of E-Technology Hajar Ait Lamkademe 17 4 2026 https://doi.org/10.6025/jet/2026/17/4/193-216 https://www.dline.info/jet/fulltext/v17n4/jetv17n4_1.pdf As social media ecosystems grow increasingly complex, understanding content strategies requires moving beyond traditional demographic or industry based segmentation toward multidimensional behavioral configurations. This study presents a multivariate clustering analysis of social media content strategies, utilizing a dataset of 20 industry specific strategic profiles. By employing Multiple Correspondence Analysis (MCA), hierarchical clustering, Gower distance clustering, text embedding analysis, and K-modes clustering, we identify latent strategic archetypes based on combinations of platform, content format, primary goals, and audience engagement mechanisms. The analysis reveals four distinct exploratory archetypes: Professional Trust Building, High Energy Product Discovery, Visual Lifestyle and Brand Affinity, and Informational Consideration and Conversion. Notably, the findings demonstrate that strategic similarities frequently transcend conventional industry boundaries, and that structured categorical choices provide stronger strategic differentiation than semantic textual descriptions. Ultimately, this research conceptualizes social media content strategy as a multidimensional configuration, offering a data driven framework for marketers and organizations to design more effective, archetype-aligned communication strategies.