Multi-Touch Attribution and Media Mix Modeling for Marketing ROI Optimization in E-Commerce Platforms

Abstract

The exponential growth of digital marketing channels has created unprecedented complexity in understanding customer journeys and optimizing marketing investments in e-commerce platforms. Multi-touch attribution (MTA) and media mix modeling (MMM) have emerged as complementary approaches for measuring marketing effectiveness and maximizing return on investment (ROI). This review examines the theoretical foundations, methodological developments, and practical applications of MTA and MMM in e-commerce contexts from 2019 onwards. Multi-touch attribution enables granular tracking of individual customer touchpoints across digital channels, while media mix modeling provides aggregate-level insights into marketing effectiveness through econometric analysis. Machine learning (ML) and artificial intelligence (AI) have revolutionized both approaches, enabling more accurate attribution modeling and predictive optimization. Recent advances integrate unified measurement frameworks that combine the strengths of MTA and MMM to overcome their individual limitations. This paper synthesizes current research on data integration challenges, algorithmic innovations, privacy considerations, and implementation strategies. The review highlights how modern attribution systems leverage deep learning (DL), Bayesian methods, and causal inference techniques to navigate the increasingly complex digital marketing ecosystem. Emerging trends include privacy-preserving measurement, cross-device attribution, and real-time optimization algorithms that adapt to dynamic market conditions. The synthesis reveals that successful ROI optimization requires not only sophisticated analytical techniques but also organizational alignment, data infrastructure investment, and continuous model validation against business outcomes.