Machine Learning-Driven KPIs for Revenue Optimization in Adtech - Kodey, Naga Harini (International Journal of Computer Sciences and Engineering, 2024)
As the AdTech industry evolves, it increasingly relies on Key Performance Indicators (KPIs) to measure success. Traditional KPIs such as ad-impressions, ad click-through rates and survey responses have long served as benchmarks for campaign performance. However, with the rise of machine learning (ML) and automation, the need for more sophisticated and predictive KPIs is apparent. This paper introduces a novel approach, proposing machine learning-driven KPIs designed to optimize revenue streams and address challenges like ad fatigue, cross-device behavior, and accessibility. By automating KPI validation and implementing advanced metrics—such as Ad Accessibility Optimization, Ad Fatigue Prevention Index, and Cross-Device Path Efficiency—this paper offers an innovative framework for enhancing data-driven decision-making in real time. These new KPIs aim to predict optimal ad strategies and improve campaign performance, ultimately maximizing ROI.