Measure the real ROAS of your media with next-generation Marketing Mix Modeling

Know what each channel really returns, and test the next move before you commit to it. Your sources connected, your MMM models up to date, your budget decisions in one place.

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Contributions over time
By day
202420252026
  • Baseline
  • TV
  • Social
  • SEA
  • Influence
Illustrative chart. Sample data.
40+native connectors

Media APIs, data warehouses, Excel and Google Sheets imports.

4update cadences

From annual to monthly, you choose.

1 + 1Marketing Scientist and Data Scientist

Included on every account.

Four questions, four answers in the platform

What each channel really returns

A Bayesian model measures the incremental contribution of every channel and platform, including paid and non-media actions. You get ROAS independent of last click and cookies.

Where to put the next euro

Saturation curves show when a channel starts returning less. Marginal ROAS and the simulator let you test a reallocation before committing it.

How to steer every day

Calibrated attribution applies model results to your attribution data, down to campaign level, every day. An optional module for accounts updated monthly.

What your results mean

mediaAI answers natural-language questions using only your data. Ask it for a mix recommendation and it explains its reasoning.

From collection to recommendation, five steps

  1. Step 1

    Collect

    Data collection is the longest step of an MMM, and the one that stalls the most advertisers. mediaROI has industrialised it: after a kickoff that sets the scope and the data checklist, data arrives through our native connectors (media platforms, data warehouses, files) or through the mediaROI MCP, which fetches it wherever it sits. The platform detects discrepancies, gaps and inconsistencies, and flags them before they reach the model.

  2. Step 2

    Model

    mediaROI trains a Bayesian econometric model specific to your brand, your market and your channels. The engine builds on an open-source standard, enriched with a proprietary business layer (priors, seasonality, adstock, promotions). Training is automated: the platform detects anomalies and a mediaROI marketing scientist validates the results.

  3. Step 3

    Analyse

    Results are read directly in the platform: each channel’s contribution to sales, ROAS and mROAS, saturation curves, the weight of the baseline and of external factors. You see where you are under-invested, where you saturate, and what media really brought in.

  4. Step 4

    Simulate

    The budget simulator lets you test your assumptions: the same budget better allocated, a higher or a lower budget, or the minimum budget needed to reach a target. Every scenario returns a projected result, channel by channel, with the matching saturation level.

  5. Step 5

    Recommend

    Optionally, the mediaROI team works with you to turn those scenarios into actionable recommendations and defend them internally.

Six building blocks, one product

mediaAI

An agent that knows your data, media context and previous questions, and explains its answers.

View mediaAI →

Methodology

Marketing Mix Modeling, calibrated attribution and incrementality: the measurement method the platform runs on, model and diagnostics included.

View the methodology →

Three ways to work with us

Advertisers

You want to know what your media returns

We run the project end to end: collection, modelling, delivery and recommendations. Your teams, management and agency share the same figures.

Data and marketing science teams

You already have an MMM, you need the tool to use it

You train your models and import them into the platform: your marketing teams read the results, test budgets in the simulator and query mediaAI.

Agencies, media owners, consultancies

You run measurement for clients

The platform powers your measurement projects under your brand: collection, models, delivery and simulator for each of your clients, with separate access per account.

FAQ

Frequently asked questions

View all FAQs →
Do you need a data team to use mediaROI?
No. The platform is built for marketing teams, supported by a mediaROI Marketing Scientist and Data Scientist. A data team can go further with settings, training and API or MCP access.
How long before the first results?
It mainly depends on the availability of your data. To speed this step up, we have built 40+ native API connectors to your media, analytics and CRM platforms. Once collection is validated, modelling and delivery take a few weeks, versus several months for a traditional MMM.
Which model powers your MMM?
On an open-source Bayesian model. Its code is public and the settings applied to your data are visible in the platform.
Does it replace my attribution tool?
No. MMM measures true channel performance; your attribution tool remains useful for intra-channel optimisation. Calibrated attribution bridges the two.

Discover the platform

30 minutes to walk through a complete model and ask your questions