Measuring Cross-Channel Ad Synergy with Machine Learning: TV, Social, and Search Integration

Abstract

This article examines how cross-channel ad synergy is currently measured when TV, social, and search are integrated in a single campaign, and what role machine learning plays in that measurement. In increasingly fragmented media environments, advertisers suspect that exposures across channels interact rather than simply add up, but existing evidence and tools are scattered and often channel centric. The study conducts a systematic literature review of peer-reviewed articles published between 2019 and 2023, focusing on research that applies machine learning or advanced data-driven methods to multi-channel advertising settings. The reviewed studies are coded by channel configuration, data sources, modelling approach, treatment of synergy or interaction effects, and outcome metrics. The findings show that machine learning is widely used for targeting, prediction, and multi-touch attribution, but that explicit modelling of synergy among TV, social, and search is rare, often methodologically fragile, and highly context dependent. The article concludes by outlining a research agenda for causal, interaction-aware machine learning frameworks for cross-channel ad synergy.