KESHRI'S PREDICTIVE REVENUE ON AD SPEND (K-ROAS)

Traditional Return on Ad Spend (ROAS) models fall short in B2B marketing environments due to long sales cycles and delayed revenue attribution. This paper introduces the Keshri Predictive ROAS Model (K-ROAS), a forward-looking framework that combines actual collected revenue with forecasted pipeline value using historical win rates. The model offers a more accurate estimation of marketing return by incorporating conversion-weighted pipeline forecasting into the ROAS calculation.We present a six-month data study comparing Traditional ROAS, K-ROAS, and Actual ROAS, demonstrating that K-ROAS consistently delivers higher predictive accuracy (within 5% of actual revenue) while Traditional ROAS shows errors exceeding 30–50%. This model bridges marketing analytics, CRM data modeling, and campaign attribution—making it highly relevant for data-driven business systems, automated decision-making tools, and applied computational models in marketing.K-ROAS is proposed as a practical and computationally efficient tool for marketing teams, agencies, and business intelligence platforms looking to more effectively allocate ad budgets and evaluate ROI before revenue fully matures.