The Measurement Renaissance: Why MMM is Making a Comeback (And Why It's Still Broken)
The False Promise of Real-Time Marketing Mix Modeling
60% of marketers believe data privacy regulations will negatively affect their analytical approach to marketing, while marketers are overwhelmed by data that's siloed across various ad platforms. This measurement crisis has sparked renewed interest in Marketing Mix Modeling (MMM), but the fundamental problems that killed it the first time haven't been solved.
Why MMM Died (And Why We Forgot)
Traditional MMM failed because it was too slow, too expensive, and too generic. Six-month modeling studies delivered insights that were obsolete before implementation. Marketers often make the mistake of relying on a single-touch attribution model—whether it's first-touch, last-touch, or any other simplified approach.
The problem wasn't speed—it was causality. No amount of machine learning can solve the fundamental challenge of isolating marketing impact from all other business variables without making heroic assumptions about consumer behavior.
The False Promise of Real-Time MMM
Marketers face the challenge of allocating their marketing budgets effectively and proving ROI, with data scattered across platforms and channels making it difficult to integrate information. Today's MMM vendors promise real-time insights and automated optimization, but they're solving the wrong problem.
Limited resources (46%) and complexities (46%) are the top challenges preventing marketing attribution implementation, with only 29% considering themselves very successful at using attribution to achieve strategic objectives. The issue isn't computational power—it's the fundamental impossibility of proving causation from correlation in complex, multi-variable marketing environments.
The Hybrid Future: MMM Plus Incrementality
Consumer privacy has been front and center and will continue to be. There's third party tracking capabilities that have started to become less and less effective, making the attribution discussion even more challenging.
The future belongs to measurement systems that combine MMM's macro-level insights with micro-level experimentation. Brands need both the forest view (MMM for strategic budget allocation) and the tree view (incrementality testing for tactical optimization).
AI-powered attribution models can connect customer interactions from various touchpoints, including online and offline channels, and create a unified view of the customer journey. Success requires accepting that perfect attribution is impossible while building systems that provide directionally correct insights for decision-making.