Organic Virality Detection System

Context Brands often struggled to identify which organic social content was most likely to benefit from paid amplification before performance peaks had already passed. ⸻ Problem Paid media investment decisions were largely reactive, limiting the ability to prioritize high-potential organic content early enough to maximize amplification efficiency. ⸻ Approach Developed an experimental R-based analysis system using Facebook API data to identify organic content with strong amplification potential based on early engagement behavior. The system evaluated: • organic performance velocity • post engagement behavior • comparative post distribution • outlier performance signals and estimated potential paid media outcomes using average CPC and CPM benchmarks across categories. ⸻ Outcome The project established an early-stage predictive framework for identifying high-performing organic content and supporting more informed paid amplification decisions. The initiative also expanded the agency’s experimentation around data-driven media prioritization and automated performance analysis. ⸻