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Marketing Mix Modeling

See what actually drives your sales.

A privacy-durable model of your whole marketing mix, online and offline, that measures the real contribution of every channel and shows you where the next euro should go.

We build it with Inner Data, calibrate it against real experiments, and run the media it points to. Not a report you file, a model you can steer by, and one you own.

Privacy-durablemodels patterns, not people, so it does not decay
Yoursyou own the model, the code and the data
OpenGoogle Meridian, PyMC and Robyn, on your cloud
ISO27001 certified (PT010101, Bureau Veritas)
The measurement problem

Click attribution now measures less than it used to.

Cookies, consent and platform walls have hollowed out the click-based attribution most companies still trust. MMM answers the question those tools no longer can: across everything you do, what is really working, and what happens if you move the budget?

Click attribution

Sees
The last clicks, usually the cheap digital ones
Needs
User-level tracking that privacy keeps eroding
Covers
Digital only; blind to offline and brand
Over time
Decays as cookies and consent tighten

Marketing Mix Modeling

Sees
The real contribution of every driver to sales
Needs
Aggregate data, no personal data at all
Covers
Online and offline, performance and brand
Over time
Privacy-durable, it does not decay

From "what happened" to "what to do next." A good MMM separates the baseline you would have sold anyway from the incremental sales each driver added, controlling for price, seasonality and promotions, then shows where returns flatten and where the next euro earns most.

What an MMM gives you

It decomposes your results, then turns that into planning power.

The output is a plan for where the money should go, not a number to file away.

Contribution

How much of your sales is baseline demand, and how much each channel, campaign and driver is estimated to have added.

ROI and marginal ROI

Not just the average return on a channel, but the return on the next euro, which is what should actually decide the budget.

Response curves

Where each channel's returns flatten, so you stop overspending what has peaked and feed what still has room to grow.

Halo and cannibalization

The spillover where TV, video or brand lift branded search and organic, plus the negative halo when channels steal from each other. Modeled, not ignored.

Beyond media

It weighs price, promotions, distribution, seasonality and creative too, so media is judged in context, not in a vacuum.

Three effects, captured

Adstock (carryover), saturation (diminishing returns) and halo (spillover). Miss one and you misjudge a whole channel.

Plan the budget before you spend it

Rehearse the decision before it costs you anything.

What-if, before you commit

Scenario planning
  • Move budget between channels and see the modeled effect on sales
  • A reallocation becomes a rehearsed decision, not a gamble

Same budget, better return

Reallocation
  • The model finds spend that has peaked and spend with room left
  • You shift with evidence, not habit or the loudest opinion

Plans you can defend

Into the budget meeting
  • Take a scenario and its assumptions to the board
  • The number has a reason, not just a champion

Scenarios are modeled estimates built on your data. They are there to make better bets, not promises.

How we build it · Wise Mix

Equal parts data engineering, statistics and marketing judgment.

Wise Mix is our MMM framework: the repeatable way we take a client from raw data to budget decisions, built to be understood and inspected, not taken on faith.

Step 1

Data foundation

We gather and clean spend, sales and the context that moves them (price, promotions, seasonality, distribution) into one modeled dataset, on your cloud.

Step 2

The right model, honestly built

We model carryover (adstock) and diminishing returns (saturation), mostly in Bayesian frameworks that report uncertainty ranges instead of false precision.

Step 3

A proven, open toolset

Google Meridian and PyMC-Marketing where uncertainty matters, Meta Robyn where ridge regression fits better. Chosen per project, never from a template.

Step 4

Calibrated with real experiments

Where media and geography allow, we run incrementality and geo-lift tests and feed the results back, so estimates stay consistent with what actually happened.

Step 5

Triangulated, then activated

MMM for the portfolio view, experiments for causal ground truth, attribution for the daily signal, then the plan flows straight into the media we run.

The hard part is closing the loop between the model, the experiments that validate it, and the media that acts on it. Because all three sit with us, we can, and we grade our own homework against controlled experiments, on purpose.

Transparent, and yours to keep

A model you can see inside, and take with you.

Independence is part of the value

  • You own it. The model, its code and your data stay yours, exportable and documented.
  • No black box. Open frameworks and clear assumptions, so your team and ours can challenge why it says what it says.
  • No lock-in. Built on open-source foundations on your own cloud, so switching never means starting from scratch.

From model to budget decisions

  • Budget optimization. How to split spend across channels to maximise the outcome you care about, within real constraints.
  • Living, not one-off. Refreshed on a cadence that matches your business, and moving toward always-on.
  • Connected to activation. The plan flows into Performance Marketing and Campaign Management; the audience value feeds CRM & HVA.
Is MMM right for you now?

No longer just for the giants.

Open-source frameworks and cloud automation have collapsed the cost and the timeline, so a serious growing company can run a proper MMM, not a stripped-down toy. It is more about your data and your decisions than your size.

You are likely ready if

  • You spend meaningfully across several channels, not one or two.
  • You have roughly two or more years of consistent history, or good geo data.
  • Some spend is offline or brand, where click tracking cannot see.
  • You can sometimes hold back spend in a few markets for a clean test.
  • You have a real budget decision better measurement would change.

Not there yet? That is common, and fixable

  • Start with the foundation. Getting the data right in Analytics & Measurement is often step one.
  • Build maturity. We grow your digital and data maturity with Digital & AI Consultancy.
  • Honest about fit. If a model cannot hold its weight yet, we will tell you, rather than sell you one.
Selected work

Same budget, reshaped by the model. Real money found.

Retail MMM, built with Inner Data

A retailer's mix, modeled with Meta Robyn and Google Meridian and reallocated within the existing budget, delivered around 250 thousand euros in incremental revenue at zero extra spend, and about 11 percentage points more Google Ads share. Read it at innerdata.ai/cases/mmm-retail.

More measurement and MMM cases

Further MMM, measurement and data work at innerdata.ai/cases. Client results reported by Inner Data, indicative of what good MMM can do, not guaranteed for every business.

Model, decide, act, measure

MMM works best wired into the whole loop.

It is the deep end of our measurement work, and it plugs straight into how we activate.

Analytics & Measurement →The foundation MMM sits on. Data Science →The deeper modeling and prediction layer. Performance Marketing →Where the budget plan becomes media. Campaign Management →The hands-on execution across platforms. CRM & HVA →Audience value that feeds the model. Cloud Services →The cloud the model is built and run on.
Why Wise Pirates and Inner Data

Few build the model, validate it with experiments, run the media, and still leave you owning it all.

Business results, always

The model is judged on the budget decisions and growth it drives, not on its R-squared.

We calibrate, because we run the media

Model, experiments and activation sit with one team, so nothing is lost between an analyst and an agency.

A dedicated data team

Built by Inner Data on a modern, Google-centric stack: BigQuery, Meridian and peers.

A full-stack Google partner

Google Ads Premier Partner and full Google stack, so modeling, cloud and media are one certified team.

Transparent and secure

You own the model and the data, on open foundations, in an ISO 27001 certified company.

Since 2018

A decade across 500+ brands, with measurement at the core of how we work.

Frequently asked questions

The questions we hear most.

What is marketing mix modeling (MMM)?
MMM uses aggregate data to measure how each part of your marketing, plus factors like price and seasonality, contributes to sales. It separates baseline demand from the incremental lift each channel is estimated to drive, estimates ROI, and helps you plan budget, with no personal data and full offline coverage.
How is MMM different from attribution and incrementality testing?
They answer different questions and work best together. Attribution tracks touchpoints for tactical, day-to-day optimization, but privacy and signal loss have weakened it. Incrementality tests prove the causal lift of one channel. MMM gives the whole-business, privacy-durable view across every channel, online and offline. We triangulate all three.
Does MMM replace attribution?
No, it complements it. Attribution stays useful for fast, tactical decisions inside platforms, while MMM gives the privacy-durable, whole-business view for planning. The strongest programmes triangulate MMM, attribution and incrementality experiments rather than pick one.
Why has MMM returned to the center of measurement?
Because the tracking that powered click-based attribution has eroded. Cookies, app-tracking limits and consent removed much of the user-level signal, so marketers needed a method that does not track individuals. MMM fits: it models aggregate patterns, needs no personal data, and open-source tools have lowered the cost of entry.
Are marketing mix models a must-have for 2027 to 2030?
For most brands that spend meaningfully across channels, yes. As user-level tracking keeps fragmenting and privacy rules tighten, aggregate, privacy-durable measurement moves from nice-to-have to core. Across 2027 to 2030, expect MMM, calibrated with experiments, to be the backbone most serious advertisers plan budget on.
Is MMM only for big advertisers, or can a mid-sized company use it?
Mid-sized companies can absolutely use it. It used to be the preserve of the giants, when models were slow and costly to build by hand. Open-source frameworks and cloud automation have made a real MMM viable for growing companies too. What matters more than size is enough consistent history and a real decision to inform.
Do we own the model, or are we locked in?
You own it. The model, its code and your data stay yours and exportable, built on open-source foundations on your own cloud. No black box, and no proprietary platform you cannot leave, so switching never means starting from scratch.
Can MMM measure offline and brand marketing, not just digital?
Yes, and that is one of its strengths. Because MMM works from aggregate data, it can measure TV, print, out-of-home, sponsorships and brand alongside digital, in one model, which click-based attribution simply cannot see.
Can MMM measure halo effects between channels?
Yes. Beyond carryover (adstock) and diminishing returns (saturation), a good MMM models halo, the spillover where one channel lifts others, such as TV, video or brand driving branded search and organic, and across products. It also catches the negative halo, cannibalization, where channels steal from each other. Miss it and you misjudge whole channels.
What data do I need for an MMM?
Ideally two to three years of consistent weekly data on spend and sales, plus context like price, promotions and seasonality. Geo-level data can reduce the history you need. If your data is thin or messy, that is normal, building a clean foundation is part of the work.
How often should an MMM be updated?
On a cadence that matches your business. Fast-moving categories often refresh monthly, with a fuller retrain each quarter; slower ones can go quarterly. We automate the data pipeline so refreshes are routine, not a project each time.
How long does it take to build an MMM?
It depends on data readiness and scope. Once the data is ready, a first usable model typically comes in a few weeks, then improves as we calibrate and refresh it. Traditional hand-built projects often took months, which a modern, cloud-based approach shortens.
Which MMM tools do you use?
We build on leading open-source frameworks and choose per project: mostly Bayesian ones such as Google Meridian and PyMC-Marketing, which quantify uncertainty, plus ridge-regression tools like Meta Robyn where they suit the data better. The point is the right fit for your data and channels, not one template.
How accurate is an MMM, and can I trust it?
An MMM is a model: an estimate with a range, not one exact truth, and any honest provider says so. We build in frameworks that report their uncertainty and, where the setup allows, calibrate against real experiments. That calibration, plus triangulation, is what makes it trustworthy enough to inform budget.
Ready to stop guessing?

Turn spend into decisions you can defend.

Tell us what you are trying to grow. We will tell you honestly whether an MMM is right for you now, and where the next euro should go.

Start a conversation →
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