Digital content platforms generate vast amounts of interaction data, yet most personalization strategies rely on broad assumptions rather than empirical behavioral evidence. Users navigate, consume, and engage with content in fundamentally different ways, forming distinct digital consumption patterns that remain hidden within raw session logs.
This project applies unsupervised machine learning to surface those hidden patterns. Using an anonymized dataset of 1,500 digital content sessions, two clustering algorithms — K-Means and DBSCAN — are deployed to discover natural behavioral segments without predefined labels or demographic information. Each resulting cluster represents a distinct consumption persona, characterized by unique combinations of session depth, content diversity, navigation behavior, and device preferences.
The outcome is a data-driven typology of digital consumers that enables algorithmic content curation, adaptive interface design, and targeted engagement strategies. This project bridges the gap between raw behavioral telemetry and strategic product decisions, demonstrating the full analytics pipeline from data generation through to actionable business interpretation.
Digital content platforms operate in an increasingly competitive landscape where user attention is the scarcest resource. Delivering relevant experiences depends on understanding how users consume content, yet this understanding often relies on explicit user inputs — preferences they may never set, surveys they may never complete, or demographic profiles that reveal little about actual behavior.
This project addresses a fundamental analytics question:
Can natural consumption personas be discovered solely from anonymous session interaction data, and can these personas inform real-time experience adaptation without any user-declared information?
These constraints mirror real-world scenarios where privacy regulations, anonymous browsing, and platform complexity limit the data available for personalization efforts.