Comparing Robyn vs. Meridian: What open source MMM is best for me?

Rise of open source MMM measurement

It's been 2 years since the launch of Meta’s Robyn MMM & Google MMM Meridian. They both provide a free-to-use way for brands to run incremental, focused Marketing Mix modelling.

The aim of these tools is to allow Brands to measure what's driving their sales. With a focus on controlling for key marketing factors, including price, promotions and most importantly, the impact of media.

The rise of these tools has coincided with two key trends:

• Reduction in the accuracy of attribution techniques. Walled gardens, removal of cookies and more users opting out of tracking have reduced the quality of data in attribution techniques
• A need for non-cookie-based tracking. MMM is a statistical approach rather than a deterministic method
• Desire to measure performance media alongside brand media (or mid/upper funnel)

Why use open-source tools like Robyn MMM or Meridian MMM?

Whilst MMM is not a new approach, it has been used for 15 years. Robyn MMM and Meridian MMM are free to use, fully customisable to your needs and come documented with the backing of respected Tech companies in Meta & Google.

To successfully implement an MMM programme you need to solve three things.

X Data set up
✅Run model & analysis
X Take action from measurement

With open source models, solving the “running a model” part. Many MMM providers, like ourselves at Linea, are set up to ensure that a brand can solve all of the areas, including data ingestion & taking action from measurement.

So when working with open source models, teams need to ensure that all of these aspects are solved for.

Detailed comparison of Robyn vs. Meridian:

Group	Sub Group	Robyn MMM	Meridian MMM
Data	Ingestion	Time series data	Time series data with the option to connect to the Google ecosystem
	Ease of set-up	Complex: Requires knowledge of R or Python	Complex: Requires knowledge of R or Python
Modelling Method	Method	Ridge Regression	Bayesian
	What is that modelling type best for?	Aim to get an accurate fit of media variables when multiple channels occur together	Include external knowledge of media performance
Modelling Functionality	Adstocks	✅	✅
	Diminishing Returns	✅	✅
	Custom Inputs from experiments	X	✅
Outputs	Visuals	Decomposition by Seasonality & channel contribution ROI Charts Response Curves	Channel contribution ROI Charts Response Curves
	Scenarios/ Budget Allocation	✅ Code based	✅ Code based
Best for?		Have a spending Bias towards Meta. Prefer a Frequentist approach or want to tap into the strong online community	Have a spending bias towards Google. Prefer a Bayesian approach & want to include external information as priors

Key features of Robyn MMM

Meta’s Robyn uses ridge regression as its core modelling engine. This provides stability and robustness when dealing with many correlated marketing variables. Ridge regression applies a regularisation penalty that can prevent overfitting and ensure more realistic, interpretable media effects.

This makes it particularly effective for marketing mix modelling, where channels often move together and avoids allocating too much credit to correlated spend patterns.

In addition, Robyn integrates time-series decomposition, automatically controlling for underlying business baselines such as long-term trend, seasonality, and holiday effects. This separation ensures that the regression focuses on the incremental contribution of media, not fluctuations caused by recurring calendar or market dynamics, a key feature of MMM.

Key features of Meridian MMM

Google’s Meridian stands out for its explicit integration of reach and frequency modelling, especially for video and YouTube channels. Instead of relying solely on spend or impressions, it measures how many unique users were actually reached and how often they were exposed to an ad. This allows the model to distinguish between incremental reach (new audiences) and repeated exposure (frequency), which offers a unique way of measuring the effectiveness of YouTube. By doing so, Meridian captures diminishing returns more realistically, revealing how additional exposures contribute less marginal impact once audience saturation occurs.

Equally important is Meridian’s Bayesian framework, which allows the incorporation of prior knowledge, such as results from experiments or past campaigns, directly into the model. This approach generates full probability distributions for ROI estimates, rather than single point values. This provides a richer insight into uncertainty and confidence - a crucial aspect of MMM. The Bayesian framework also performs well with limited or noisy data, as priors can guide the model towards plausible results.

Robyn & Meridian Optimisation: Taking action from measurement

One of the key challenges we have seen from teams building MMM in Robyn or Meridian is trusting the results and taking action from the analysis.

Teams, when running an MMM, need to focus on what the core question(s) they are trying to answer. Typically, it's three things:

1. How much am I paying back for each marketing channel (cross-channel effectiveness)
2. How do I allocate my next month, quarter or year budget between channels?
3. How much do I spend to hit my targets?

Having used both Robyn & Meridian optimisation tools, we have found that their functionality is lacking. The existing optimisation features for Robyn & Meridian are slow to run, don’t take into account many “what if” factors that may come up and, crucially, it’s not possible to be run by marketing teams.

That's why we have built the Linea Scenario Tool. It’s built on top of your Robyn or Meridian models and is specifically designed to handle complex Robyn or Meridian optimisation challenges. The tool runs quickly and is easy for data scientists or marketing teams to use. To ensure that teams can take action from there, Robyn or Meridian MMM, it is built with a flexible level of functionality to ensure teams can run multiple “what if” scenarios.

Want to find out more: Try it now with a 7 day free trial.

In Summary

Both Robyn and Meridian MMM tools have changed the game on measurement. In-house teams, Media agencies & many others use these as a gateway into more detailed marketing measurement. This has resulted in increased usage of MMM as a way to measure media effectiveness.

In terms of which one to pick, I would be guided by two questions:

• What's my largest Media spend? If its Google then Meridian will be easier to integrate or if spending on Meta then go with Robyn
• Do you have a preference on Bayesian vs. Ridge regression models? We recommend the Bayesian modelling of Meridian. It gives confidence intervals & allows external information - both crucial aspects of building a strong model

Lastly, setting up an MMM programme is difficult. As such, teams need to focus on how you take action from MMM to make it worthwhile. A crucial use case is through allocating cross-channel budgets or setting your budget to hit a target. Whether it's through Robyn, Meridian or Linea Scenario Tool

FAQ

What other open source MMM tools are there?

There are a number of wider free packages available that run different MMM options. This includes:

• Pre-built packages in R or Python: Are good, but don’t come with the same level of functionality and documentation
• PyMC: A US-focused, well-documented MMM library.

Can I switch between Robyn & Meridian?

Whilst you can switch easily between different tools. We wouldn’t recommend switching (on a regular basis) due to three things:

• Domain knowledge makes switching time-consuming
• The speed of updates will (or should) increase if you stick with a modelling structure
• Switching models will cause a difference in outputs. If not managed and explained, this can impact your confidence and ability to take action

Are Robyn & Meridian accurate?

Any MMM model is not just a function of the tools used but includes the data quality, the model approach, the modeller & how outputs are used. So comparing like-for-like accuracy needs to include all of these factors. We recommend two areas to build confidence in accuracy:

1. Double verification: If unsure of a result, we set up an experiment to test in more detail. Two answers are better than one.
2. Out-of-sample testing: use your model to predict next month's/quarter's sales and compare accuracy.
3. Channel Confidence intervals: Guides the level of confidence with which you can take action. “Tight confidence” then make decisions. “Wide confidence" then needs more data to make a decision.

Assuming all of these factors are executed correctly, then an MMM model will be accurate. Here at Linea, we provide a model review set up. Where we review your modelling framework across:

• Modelling structure & KPIs
• Omitted variables
• Possible tests to improve the model
• Benchmark your adstock & diminishing returns
• Key other questions you could answer