Abstract
A data-driven, statistically rigorous analytical framework is disclosed for determining performance decay and optimizing the replacement timing of digital media assets (such as images and videos) in digital media campaigns. The system ingests campaign-level and asset-level metrics on a daily basis and extracts high-dimensional visual and semantic embedding vectors from the media assets using a deep neural network feature extractor. A visual taxonomy is constructed by clustering similar assets into visual archetypes using K-Means clustering, allowing for aggregate fatigue analysis within specific thematic content clusters. The statistical decay engine fits a non-linear saturation model (e.g., a Hill function S-curve) to map the exposure-to-yield relationship (cumulative impressions vs. cumulative clicks or views). The model also has the flexibility to include seasonal or exogenous factors that may vary the asset performance, so we can isolate the media saturation effect from other factors that might affect media asset performance. A comparative analysis between the non-linear Hill model and a linear regression baseline is performed using a Nested Model F-test to mathematically prove that the decay is due to non-linear saturation (p < 0.05) rather than random variance or seasonality. For assets with sparse data, Monte Carlo simulations from the parameter covariance matrix provide a 95% confidence interval for the saturation ratio. The decision engine utilizes a multi-layered analysis framework (comprising an early launch phase audit, a lifecycle inflection analysis, a four-week performance persistence rule, and cluster-level risk analysis) to automatically classify assets into distinct health states (Healthy, Early Warning, or Fatigued) and trigger content swaps. By resetting saturation back to near-zero, the disclosed system quantifies the opportunity cost of fatigue and estimates the potential incremental yield recovered via proactive, automated asset refreshes.
KEYWORDS: Performance decay, creative fatigue, visual embeddings, Hill function, non-linear saturation, K-Means clustering, Nested Model F-test, Monte Carlo simulation, visual taxonomy, asset lifecycle, media asset performance, media campaign optimization.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Arora, Manisha; Idrissi, Yassine; and Caforio, Bruno, "System and Method for Statistical Modeling and Automated Lifecycle Refresh Detection of Digital Media Assets", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11534