Optimizing Digital Advertising Budget Allocation Using Marginal Return Efficiency Curves: A Multi-Platform Simulation Study

This study challenges the traditional reliance on Average Return on Ad Spend (ROAS) as the primary metric for budget allocation decisions and validates Marginal ROAS as a superior decision-making framework for multi-platform digital advertising campaigns. A quantitative simulation study employing constrained optimization modelling was conducted using synthetic performance data representing five major digital advertising platforms (Google Search, Meta Ads, TikTok, Snapchat, and LinkedIn) over a 12-month period. Historical performance datasets were generated for each platform, modeling spend-conversion relationships with characteristic concave response functions. Key metrics including Average ROAS, Marginal ROAS, audience saturation rates, and Allocad Intelligence Scores were calculated for each platform. A Lagrangian optimization algorithm was applied to reallocate a fixed advertising budget across platforms based on marginal efficiency, comparing pre-optimization and post-optimization allocation strategies. Analysis revealed systematic discrepancies between Average and Marginal ROAS across all platforms. Average ROAS values ranged from 1.9x to 4.0x, while Marginal ROAS values were significantly lower (1.2x to 3.5x), indicating that Average ROAS masks true efficiency decay. Following optimization, budget was reallocated from saturated platforms (Meta: -35%, LinkedIn: -80%, Snapchat: -60%) to platforms with higher marginal returns (Google: +46.7%, TikTok: +20%). This reallocation equalized Marginal ROAS across channels (2.4x-2.8x) while reducing total budget by 9.1%. Statistical analysis confirmed that platforms with audience saturation exceeding 70% demonstrated Marginal ROAS values below 2.0x. Marginal ROAS provides a mathematically robust and economically superior framework for multi-platform advertising budget allocation. Organizations relying on Average ROAS risk significant overspend on saturated channels and missed opportunities in underinvested platforms. These findings have immediate practical implications for marketing practitioners and suggest the need for real-time Marginal ROAS monitoring systems.