The Effects of Advertising Media Channel Combinations on Brand Performance

Abstract

Prior research on advertising media mixes has mostly focused on single channels (e.g., television), pairwise cross-elasticities, or budget optimization within single campaigns. This is starkly detached from advertising practice where (i) there is an increasingly large number of media channels available to marketers, (ii) media plans employ complex combinations of channels, and (iii) marketers manage complementarities among many (i.e., more than pairs) channels. This research empirically learns complex channel complementaries using Latent Class analysis. Latent classes have three useful properties: (i) they account for non-random selection of channels into campaigns, (ii) they capture pairwise and higher-order interactions between channels, and (iii) they allow for meaningful interpretation. We empirically describe the most common media channel archetypes and estimate their effectiveness on a set of common brand-related campaign performance metrics using a dataset of 1,083 advertising campaigns from around the world run between 2008 and 2019. We find that there is not a systematically “best” media mix that generates dominant performance across metrics. However, for each metric, clear recommendations can be made. We find that traditional channels (TV, outdoor) pair quite well with digital channels (Facebook, YouTube), current marketing practice appears far from optimal, and simple strategies are predicted to increase brand mindset metric lifts by 50% or more.