How to choose a marketing mix modelling vendor

Most marketing mix modelling vendors now use Bayesian models, show saturation curves and promise better budget decisions. So the statistics are rarely what separates them. What separates them is who does the work after the first model is delivered: collecting the data every week, retraining the model, explaining what changed and turning it into a budget decision. This guide covers the questions that expose those differences, and the practical ones about budget, data and time to first results.

The short answer

Pick the vendor whose way of working fits your team, then test them on four things: how they set up the model, how they check it, whether it answers budget questions rather than reporting on the past, and what it takes from you to get started. A framework you run yourself, a consultancy project and a managed platform can all use similar maths and still give you very different results a year later.

1. Start with who will run it

There are four common routes to MMM, and each puts the ongoing work in a different place.

• Open-source frameworks such as Google Meridian, Meta Robyn and PyMC-Marketing. The code is free and good. Your team owns the data pipeline, the model design, the retraining and the interface the marketing team uses. Companies that do this well tend to have several data scientists on it.
• Consultancies and media agencies. A team builds a model and delivers a report, often once or twice a year. Good for a one-off answer, harder to keep current between projects.
• Self-serve software. You get a login and the model runs in the vendor's product. Your team still prepares data, configures the model and interprets the output.
• Managed platforms. The vendor connects the data, builds and maintains the model, and reviews the recommendations before you see them. Odins works this way.

The question to ask yourself is plain: who in our company will maintain this in month 14? A common story is a model built on Robyn or Meridian by one analyst, which nobody updates after that person moves on. If that is where you are, ask each vendor whether they can learn from the existing work (the channel assumptions, the data cleaning, the findings) rather than start from nothing. A lot of useful thinking usually went into that first model.

2. Ask how the model is set up

Bayesian is now standard. What matters is what goes into the model before it sees your data, and whether you can see it.

• How are the priors set? Priors are the starting beliefs about how each channel works. On thin data, they decide much of the result. Ask where they come from: your own past budgets and results, signals from the ad platforms, structured conversations with your team, or defaults from a library. At Odins this typically takes two or three one-hour sessions with the customer.
• Can you see inside it? Ask to see saturation curves per channel, the prior next to the posterior, the baseline, and the uncertainty range on every number. A vendor that only shows point estimates is hiding the most useful information.
• Is it independent? Ask whether the vendor also sells media, and whether the model or its defaults come from an ad platform. Open-source code from Google or Meta can be inspected, so the risk is less in the code than in how it is configured and which data it is given. An independent vendor has no stake in which channel wins.
• Does it change over time? Channel response is not constant. A model that assumes Google Search works the same in July as in November forces the wrong answer on every seasonal decision. Ask whether saturation and seasonality can vary over time. We explain why this matters in time-varying saturation.

3. Ask how the results are checked

Every model fits its own history well. That proves little. Ask how the vendor shows that the model predicts sales it has not seen.

• Is the forecast compared with actual sales every month, and can you see that comparison yourself?
• What accuracy do they expect in steady state, and what happens when the model drifts? Odins aims for forecasts within ten to fifteen percent of actual sales in steady state, checked monthly in the platform.
• Does a person review the output before it reaches you? Automated recommendations from a model that has drifted are worse than none.
• Will someone meet you regularly to explain what changed and why? Ask who that person is, and how often.

4. Insist on decisions, not reports

The point of MMM is not to explain last quarter. It is to answer the next budget question. Two questions matter most:

• How much should we spend in total? The model should show the return on the next part of the budget, not only the average return. That is what tells you whether a larger budget still pays back.
• Where should it go? Given a fixed budget, which channels have room to grow and which are saturated, and what is the expected effect of moving money between them?

Ask the vendor to show a scenario live: "What happens to sales if we move a tenth of the search budget to TV next quarter?" Then ask what it does when it is unsure. A good answer is a structured test: which channel to test, for how long, at what cost, and how much narrower the uncertainty will be afterwards. Reallocation alone typically finds five to fifteen percent more effect from the same budget, so the question is worth asking properly.

5. The practical questions: budget, data, time

• Minimum budget. MMM needs variation in spend to learn from. As a rule of thumb it starts to pay off with a marketing budget above about EUR 1 million a year. Below that, a clean cross-channel dataset may be the better first step.
• Data history. Two years of weekly data is a comfortable floor, and three is better. Six months can work when strong priors carry more of the load, with wider uncertainty as a result. Our guide to data for marketing mix modelling covers what to collect.
• Time to first results. Ask for a realistic plan, including getting data out of your media agency. At Odins the first model is typically live six to eight weeks after the data is connected, and it is refreshed monthly after that.
• Starting small. Ask whether you can start with one market or one brand and add the rest once the first model has proved itself.
• Your data afterwards. Ask whether the cleaned, structured dataset can flow into your own warehouse, and who owns it.

A checklist to take into vendor meetings

• Who maintains the data connections, and what do you need from us each month?
• How are offline channels such as TV, radio and outdoor collected?
• How do you set priors, and can we see them next to the posteriors?
• Can saturation and seasonality change over time in your model?
• How do you show that the model predicts unseen sales, and how often?
• Does a person review recommendations before we receive them?
• Can the model answer how much to spend in total, not only how to split it?
• How do you handle channels where the model is uncertain?
• What can we learn from or reuse from our earlier MMM work?
• How long until the first model, and what is on our side of that plan?
• Can the structured data flow into our own warehouse?
• Do you sell media or have commercial ties to any channel in the model?

Where Odins fits, and where it does not

Odins is a managed marketing investment platform. We connect the data, digital and offline, build and maintain Bayesian models, and deliver monthly recommendations that our team reviews before they reach you. Companies like CDON, Nettbil, Hyre and Megaflis run their marketing budgets on it.

It is not the right choice for everyone. If you have a strong data science team that wants to own the code, an open-source framework is a sensible route, and our build versus buy guide for Meridian covers the trade-offs. If your marketing budget is well below EUR 1 million a year, start with the data. For a wider view of the market, see our overview of MMM providers.

Frequently asked questions

What is the minimum marketing budget for MMM to be worth it?

As a rule of thumb, around EUR 1 million a year in marketing spend. The model needs enough variation between channels and over time to separate their effects. Below that, the uncertainty ranges get wide and a structured cross-channel dataset is often the better first investment.

We built a model with Robyn or Meridian and nobody maintains it. Can a vendor take over?

Ask each vendor directly. At Odins we do not run your old code as it is, but we learn from it: the data cleaning, the channel definitions and what the earlier work found about how your channels behave. The new model is then connected to live data and kept up to date every month.

Consultancy or software?

A consultancy gives you a project and a report. Software gives you a model you can query any time, but self-serve tools leave the data and interpretation work with you. A managed platform sits between the two: software you can use every day, with a team doing the data and model work.

How long does it take to get results?

Plan for six to eight weeks to a first model once the data is connected. The slowest part is usually offline media data held by agencies, so start that request early.

For the full background on how MMM works, read our complete guide to marketing mix modelling. If you are comparing vendors now, book a demo and bring the checklist.