Inconsistent user-identifiers and the walled garden policies of dominant social media players, together with the (imminent) abolition of third-party cookies has led to renewed interest in the marketing mix model as an attribution tool. However, to be useful in a post-MTA world any ‘next generation’ MMM framework needs to deliver on three fundamental business issues.
Firstly, to serve as a true attribution solution, MMM needs to focus on causal estimation methods. Too often we see reliance on consumer journey solutions to address the problems of last-touch attribution. However, these ignore the critical issues of selection bias endemic in much online media – leading to endogeneity bias and misallocation of the marketing mix. The growing popularity of automated machine learning approaches to the mix model only serve to exacerbate this problem, where the focus is on prediction not causation.
Secondly, MMM needs to quantify the long-term (base building) effects of marketing and so inform brand-building strategy. Standard approaches are simply not set up to measure these effects, with fixed baselines and a focus on short to medium-term lag structures or Adstocks. Alternative time series structures are required that can quantify both short and long-term (base) variation – coupled with dynamic network models that can explain the causes of base variation and the economics of brand-building.
Finally, next-generation MMM needs to fill the gap left in a cookie-less world to deliver granular and swift insights on marketing ROI and optimal budget allocation. Suitably identified high dimension mix models – across consumer cohorts by day or hour – can fit the bill. This can provide many of the claimed benefits of MTA such as granular online media effectiveness ranking by publisher and placement with the added benefit of quantifying the contribution of pricing and offline media, controlling for the wider economic environment and the ability to analyze brand building.