Glossary · Marketing Engineering
Marketing Mix Modeling
also: MMM · media mix modeling
Definition
Marketing mix modeling is a top-down econometric approach that estimates the causal contribution of each marketing channel to sales using aggregate historical data and controls for non-marketing drivers (price, distribution, seasonality, competition). Privacy-first MMM has returned as deterministic user-level tracking has eroded.
MMM regresses a sales outcome on marketing spend variables (with adstock and saturation transforms), price, distribution, seasonality, and macro controls. The output is channel-level elasticity curves, how sales respond to incremental spend. Bayesian hierarchical MMM (Google's Meridian, Robyn) has become the modern standard because it handles uncertainty quantification and multiple geographies naturally.
Essays on this concept
- Marketing Engineering
Marketing Mix Modeling in the Privacy-First Era: Bayesian Structural Time Series Without User-Level Data
Cookies are dying. Deterministic attribution is shrinking. The irony: the measurement approach from the 1960s, Marketing Mix Modeling, is making a comeback, now powered by Bayesian inference that would have been computationally impossible when it was first invented.
- Marketing Engineering
Unified Measurement Architecture: Connecting MMM, MTA, and Experimentation Into a Single Source of Truth
MMM says Facebook works. MTA says Google works. The incrementality test says neither works as well as you thought. Three measurement systems, three different answers, here's how to reconcile them into one coherent picture.
- Marketing Strategy
Brand vs. Performance: A Portfolio Optimization Framework Using Markowitz Theory for Marketing Budget Allocation
Finance solved the allocation problem in 1952. Marketing still argues about it in 2026. Markowitz's portfolio theory, applied to marketing channels instead of stocks, reveals an efficient frontier that makes the brand-versus-performance debate quantitatively resolvable.
- Marketing Engineering
Creative Fatigue Detection Using Entropy Metrics: An Automated Framework for Ad Refresh Cycles
By the time your dashboard shows declining CTR, creative fatigue has already cost you weeks of wasted spend. Shannon entropy applied to engagement signals detects fatigue 11 days earlier than traditional frequency caps.
- Marketing Engineering
The Hidden Cost of Optimization: How Over-Fitted Algorithms Destroy Long-Term Brand Equity
Your bidding algorithm gets better every quarter. Your brand gets weaker every year. This is not a coincidence, it's Goodhart's Law applied to marketing, and the compounding damage is invisible until it's too late.
- Marketing Engineering
Incrementality Testing at Scale: A Geo-Lift Framework for Measuring True Campaign Impact
Half your marketing budget is wasted. The classic joke, except now we can identify which half, geo-lift experiments measure what would have happened without the campaign, not just what happened with it.
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