Marketing mix
Marketing mix modeling (MMM) services
Balanced acquisition ledger modeling.
Attributing and matching paid, search, cold outreach, and organic channels.
Marketing mix modeling (MMM) estimates how much each channel contributes to sales using aggregated data over time — spend, impressions, promotions, seasonality and outcomes — rather than tracking individual users. That makes it resilient to privacy changes and able to measure channels that click-based attribution struggles with, such as TV, YouTube, podcasts and brand campaigns.
We build MMMs with open-source frameworks such as Meta's Robyn, Google's Meridian or PyMC-Marketing, calibrate them with lift tests where possible, and turn the results into budget recommendations: where the next unit of spend earns most, where channels are saturated, and how much to reserve for testing.
The problem
Enterprises allocate media budgets by habit or incomplete attribution data, resulting in overfunded paid channels and neglected organic growth loops.
What we do
- Map channel overlaps
- Analyze cross-channel interactions
- Allocate budgets across organic and paid channels
- Model cohort conversion paths
What you get
- Marketing mix allocation plans
- Cross-channel attribution sheets
- Cohort conversion diagrams
- Budget reallocation guides
A balanced, multi-channel budget allocation that maximizes overall revenue yield.
How it runs
- Collect Data. We assemble historical spends and results across all marketing channels.
- Trace Overlaps. We calculate how first-touch channels (SEO) feed conversion channels (Paid Social).
- Model Spends. We run regression scenarios to estimate revenue yields at different spend levels.
- Refine Mix. We document a balanced allocation budget plan across paid, organic, and outbound.
Who this is for
- Brands spending across several channels with enough history to model
- Companies whose user-level attribution has weakened after privacy changes
- Advertisers investing in TV, YouTube, podcasts or other hard-to-track channels
- Leadership teams needing an independent view of channel contribution
What's included
- Data audit and preparation: spend, activity, outcomes and external factors
- Model build with an open-source MMM framework
- Calibration against lift tests or holdouts where available
- Channel contribution, response curves and saturation analysis
- Budget scenarios and allocation recommendations
- Refresh process so the model stays current
What MMM needs
Marketing mix models learn from variation over time, so they need history: commonly around two years of weekly data, though requirements depend on how many channels and how much spend variation you have. They also need the factors that move sales independently of marketing — seasonality, pricing, promotions, distribution changes, competitor activity where known. Without those, the model may credit marketing for effects it did not cause.
If you do not yet have that history, we start by collecting data in a model-ready format and use incrementality tests in the meantime. Our walkthrough of building an MMM in Python shows what the data and modelling involve.
Turning a model into decisions
An MMM's most useful outputs are response curves: how sales respond to more or less spend in each channel, including where returns flatten. Those curves let you compare channels at the margin — what the next unit of budget would earn — rather than on average return, which is how most budgets are wrongly allocated. We present budget scenarios with ranges, not false precision, and recommend tests to resolve the biggest uncertainties.
MMM alongside attribution
MMM does not replace platform optimisation or CRM reporting. Platforms still need conversion signals to bid; the CRM still tells you which deals came from which source. MMM sits above them, informing how much each channel should get. When MMM and attribution disagree sharply, that is a prompt to run a lift test on the channel in question.
Frequently asked questions
What is marketing mix modeling?
A statistical approach that estimates how each marketing channel and other factors contribute to sales, using aggregated data over time rather than user-level tracking.
How much data does MMM need?
Commonly around two years of weekly data on spend, activity and outcomes, plus external factors such as seasonality and promotions. Fewer channels or more spend variation can reduce the requirement.
What are Robyn and Meridian?
Robyn is Meta's open-source MMM package and Meridian is Google's. Both provide modelling frameworks; the quality of results depends on data and calibration rather than the tool alone.
Is MMM only for large brands?
It works best with meaningful spend across several channels and enough history. Smaller advertisers often get more from incrementality tests and clean attribution first.
How often should an MMM be refreshed?
Typically quarterly, or after major changes in spend or channel mix, so recommendations reflect current conditions.
What is Marketing Mix Modeling (MMM)?
A statistical method that evaluates how different marketing channels work together to drive business outcomes.
How do you value organic traffic?
By tracking first-touch sessions and organic brand searches to evaluate baseline capture value.
How often should budget allocation be adjusted?
We recommend reviewing and adjusting channel budget limits quarterly.
Pricing: monthly retainers by region
Every engagement is a monthly retainer priced on scope — never a percentage of ad spend. The range depends on the number of channels, markets and the depth of tracking and CRM work involved.
| Region | Monthly retainer |
|---|---|
| United States | $2,500–$6,000 per month |
| India | ₹1,00,000–₹5,00,000 per month |
| United Kingdom | £2,200–£5,000 per month |
| United Arab Emirates | AED 4,000–AED 10,000 per month |
| Australia | A$3,000–A$7,000 per month |
| Canada | C$3,000–C$7,000 per month |
| Singapore | S$3,000–S$7,000 per month |
| Europe | €2,500–€5,000 per month |