@article{4835,
  author = {Hajar Ait Lamkademe},
  title = {Social Media Content Strategy: A Multivariate Clustering Analysis of Strategic Archetypes},
  journal = {Journal of E-Technology},
  year = {2026},
  volume = {17},
  number = {4},
  doi = {https://doi.org/10.6025/jet/2026/17/4/193-216},
  url = {https://www.dline.info/jet/fulltext/v17n4/jetv17n4_1.pdf},
  abstract = {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.},
}