Marketing Mix Model
Ongoing Marketing Mix Model
MMM (Marketing Mix Modeling)
Marketing Mix Modeling (MMM) provides a scalable way to measure incrementality without relying on UTM, cookies, or device tracking, which are becoming less reliable due to privacy restrictions. It measures incrementality by modeling fluctuations in marketing activities in relation to conversions using advanced statistical and machine learning models, leveraging historical data without the need to set up holdout experiments.
How MMM Works Intuitively: At its core, if you consistently observe conversion values increase or decrease as you adjust marketing spend in specific channels or tactics, those activities are likely driving conversions incrementally. However, this is an oversimplified view of MMM's true capabilities.
Holistic Attribution Approach: MMM takes a comprehensive view by considering all drivers of your business outcomes—including different advertising channels, trends and seasonality, SEO, organic social, influencer marketing, and external factors. It assigns credit to each driver based on historical fluctuations across all data sources, ensuring the total contribution across all drivers aligns with your actual business KPIs.
See how Marketing Measurement Overview compares MMM to other approaches like traditional attribution and incrementality testing.
The Maxma Advantage
Most Marketing Mix Modeling solutions provide only high-level insights once or twice a year. Maxma MMM is different — it delivers ongoing, tactic-level incrementality measurement, allowing you to continuously track and optimize the true impact of your marketing efforts across channels and tactics.
Marketing Granularity
InsightsFrequency
Marketing Granularity
Insights Frequency
Continuous, Granular Incrementality Measurement:
- Incrementality from Maxma Marketing Mix Model is measured at the week x channel x tactic x market level.
- Retrospective measurement: Provides a trendline of incrementality for each channel and tactic over time, rather than just a single point estimate.
- Ongoing updates: Delivers weekly updates of incrementality across channels and tactics for the most recent week on an ongoing basis.
*Note: The exact level of granularity depends on factors such as marketing spend per channel and tactic. Maxma automatically determines the most feasible granularity based on the available data. The frequency can also be customized if a different cadence is preferred.
Why Ongoing MMM?
Ongoing MMM provides always-fresh insights so you can quickly test, measure, and optimize marketing efforts—adapting to shifts in performance, seasonality, and new opportunities.
Key Benefits:
- Always Up-to-Date:
- Fast growing business cannot rely on outdated insights developed from data that is a year old.
- Onging MMM provides insights that are always up-to-date with the latest data.
- Fast Feedback Loop:
- Test new ideas and see incrementality performance as quickly as one week later.
- A strong alternative to tracking-based attribution (without privacy or tracking limitations).
- Seasonality Insights:
- Businesses with strong seasonal patterns benefit from weekly measurement, revealing how efficiency fluctuates over time.
- Optimize not just across channels and tactics, but also across seasonality and timing.
| Channel | Incremental ROAS |
|---|---|
Google Search | 1.5 |
TikTok | 2.3 |
Facebook | 2.1 |
YouTube | 2.8 |
Incremental ROAS over time
Summary Comparison
To summarize, here's how Maxma MMM compares to most other MMM solutions in the market and tracking-based attribution approaches (e.g. in-platform reporting, third-party multi-touch attribution tools) that are currently widely used:
| Feature | Maxma MMM | Most Other MMM | Tracking-based Attribution |
|---|---|---|---|
| Input Data | Daily, geo-level data (1000x larger) | Weekly, country-level data | Individual user level but largely incomplete |
| Attribution Accuracy | Fair attribution based on true incrementality | Difficulty isolating individual channel impact | Assign credit in isolation, not based on incrementality |
| Insights Granularity | Week x channel x tactic level | Yearly x channel insights | User journey level but largely incomplete |
| Update Frequency | Weekly updates, fresh insights | 1-2 times per year, outdated insights | Real-time |
| Privacy Compliance | Uses aggregated data, no tracking needed | Uses aggregated data, no tracking needed | Relies on user tracking, affected by privacy restrictions |
| Attribution Method | Robust Bayesian simulation | Basic regression with limited accuracy | Rule-based or simple association |
| Time Lag Effects | Data-driven adstock | Basic adstock | Fixed lookback windows |
Getting Started
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