Marketing Mix Modeling Overview
Marketing Mix Modeling (MMM) is a statistical analysis technique that quantifies the impact of various marketing inputs on sales and other business outcomes. It uses historical data to understand how different marketing channels and external factors contribute to performance.
MMM is an econometric approach that uses regression analysis to determine the relationship between marketing investments and business outcomes. Unlike digital attribution, MMM can measure both online and offline channels, making it essential for holistic marketing measurement.
Core Components of MMM
- Dependent Variable: The outcome you're measuring (sales, revenue, conversions)
- Marketing Variables: Spend, impressions, or GRPs by channel
- Control Variables: Seasonality, pricing, promotions, economic factors
- Adstock/Carryover: The lingering effect of advertising over time
- Saturation: Diminishing returns as spend increases
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Cross-Channel Measurement
Measures all channels including TV, radio, print, outdoor, and digital in a unified framework.
Budget Optimisation
Provides data-driven recommendations for optimal budget allocation across channels.
External Factor Control
Accounts for seasonality, competition, economic conditions, and other external influences.
Long-Term Effects
Captures brand-building effects and advertising carryover that attribution misses.
Scenario Planning
Enables "what-if" analysis to predict outcomes of different budget scenarios.
| Data Type | Examples | Minimum History |
|---|---|---|
| Outcome Data | Sales, revenue, conversions, leads | 2-3 years weekly |
| Marketing Spend | TV, digital, print, radio, OOH by week | 2-3 years weekly |
| Media Metrics | GRPs, impressions, reach, clicks | 2-3 years weekly |
| Pricing Data | Average selling price, promotions, discounts | 2-3 years weekly |
| Distribution | Store count, online availability, inventory | 2-3 years weekly |
| External Factors | Weather, holidays, economic indicators, competitor activity | 2-3 years weekly |
Contribution Analysis
Breakdown of what percentage of sales each marketing channel and factor contributed. Shows the relative importance of each driver.
ROI by Channel
Return on investment for each marketing channel, enabling comparison of efficiency across the mix.
Response Curves
Visualize how sales respond to different spending levels, showing saturation points and optimal investment ranges.
Optimal Budget Allocation
Recommendations for how to redistribute budget across channels to maximise overall ROI.
Scenario Simulations
Predict outcomes for different budget scenarios, helping with planning and forecasting.
| Tool | Developer | Key Features |
|---|---|---|
| Robyn | Meta (Facebook) | Automated hyperparameter tuning, budget optimiser, R-based |
| LightweightMMM | Bayesian approach, JAX-based, geo-level modeling | |
| Meridian | Next-gen MMM, reach/frequency support, Python-based | |
| PyMC-Marketing | PyMC Labs | Flexible Bayesian modeling, CLV integration |