What is Media Mix Modeling?
Media Mix Modeling (MMM), also known as Marketing Mix Modeling, is a statistical technique that uses regression analysis to measure the effectiveness of various marketing inputs on sales or other business outcomes. It helps marketers understand how different channels contribute to results and optimize budget allocation.
In the privacy-first era, MMM has gained renewed importance because it doesn't require user-level tracking data—it works entirely with aggregate data.
MMM vs. Other Measurement Methods
| Method | Data Level | Timeframe | Privacy |
|---|---|---|---|
| MMM | Aggregate | Long-term (years) | No user data needed |
| MTA (Attribution) | User-level | Real-time | Requires tracking |
| Incrementality | Test/control | Campaign-level | Minimal user data |
| Last-Click | User-level | Real-time | Requires tracking |
How MMM Works
- Data Collection: Gather 2+ years of marketing spend and business outcomes
- Variable Identification: Define marketing inputs, external factors, and base sales
- Model Building: Create regression models linking inputs to outcomes
- Decomposition: Separate contribution of each marketing channel
- Optimization: Simulate budget scenarios to find optimal allocation
- Validation: Test model against holdout data
Data Inputs for MMM
Marketing Variables
- Channel Spend: TV, digital, social, search, mobile, etc.
- Impressions/GRPs: Media delivery metrics
- Campaign Timing: Flight dates and intensity
- Creative Rotation: Different ad executions
External Variables
- Seasonality: Holiday periods, seasonal trends
- Economic Factors: GDP, consumer confidence
- Competitive Activity: Competitor spending
- Weather: For weather-sensitive products
Pro Tip: Combine MMM with Incrementality
MMM shows the "what" but incrementality testing shows the "why." Use incrementality tests to calibrate and validate your MMM models, especially for digital channels where MMM traditionally struggles.
MMM Outputs
| Output | What It Shows | Action |
|---|---|---|
| Channel ROI | Return for each marketing channel | Shift budget to high ROI channels |
| Saturation Curves | Diminishing returns threshold | Stop spending beyond optimal |
| Carryover/Adstock | Delayed effect of advertising | Plan campaign timing |
| Base vs. Incremental | What would happen with no marketing | Understand true lift |
MMM for Mobile Apps
For mobile app marketers, MMM can answer questions like:
- How much do each of my UA channels contribute to installs?
- What's the optimal budget split between iOS and Android?
- How do brand campaigns impact organic installs?
- Should I spend more on retargeting or new user acquisition?
- What's the true impact of my TV/OTT advertising on app downloads?
Modern MMM Solutions
| Solution | Type | Key Features |
|---|---|---|
| Meta Robyn | Open Source | Bayesian MMM, automated optimization |
| Google Meridian | Open Source | Google data integration, scalable |
| Nielsen MMM | Enterprise | Full service, extensive benchmarks |
| Measured | SaaS | Incrementality + MMM hybrid |
| Recast | SaaS | Modern MMM for digital-first brands |
MMM Limitations
- Data Requirements: Needs 2+ years of quality data
- Granularity: Can't measure at user or campaign level
- Speed: Results lag actual performance by months
- Digital Channels: Historically less accurate for digital
- New Channels: Can't measure channels without history