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.

Most Other MMM Solutions

Marketing Granularity

Paid SearchPaid SocialDisplayVideo

InsightsFrequency

Every 6-12 months
Maxma MMM

Marketing Granularity

Brand SearchNon-Brand Search CompetitorSocial ProspectingSocial RetargetingVideo Lead GenVideo Awareness

Insights Frequency

As fast as weekly

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.
Most Other MMM Solutions
ChannelIncremental ROAS
Google Search
1.5
TikTok
2.3
Facebook
2.1
YouTube
2.8
Maxma MMM

Incremental ROAS over time

Google Search
TikTok
Facebook
YouTube
Sep'23Feb'25

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:

FeatureMaxma MMMMost Other MMMTracking-based Attribution
Input DataDaily, geo-level data (1000x larger)Weekly, country-level dataIndividual user level but largely incomplete
Attribution AccuracyFair attribution based on true incrementalityDifficulty isolating individual channel impactAssign credit in isolation, not based on incrementality
Insights GranularityWeek x channel x tactic levelYearly x channel insightsUser journey level but largely incomplete
Update FrequencyWeekly updates, fresh insights1-2 times per year, outdated insightsReal-time
Privacy ComplianceUses aggregated data, no tracking neededUses aggregated data, no tracking neededRelies on user tracking, affected by privacy restrictions
Attribution MethodRobust Bayesian simulationBasic regression with limited accuracyRule-based or simple association
Time Lag EffectsData-driven adstockBasic adstockFixed lookback windows

Getting Started

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