Robyn vs Meridian vs PyMC-Marketing: how the three open-source MMM libraries compare
Robyn (Meta), Meridian (Google) and PyMC-Marketing (PyMC Labs) are free open-source marketing mix modeling libraries that need a data team to run. Robyn uses ridge regression and returns point estimates; Meridian and PyMC-Marketing are Bayesian and return a probability distribution for each channel's effect.
The choice usually comes down to three questions: do you need to report uncertainty, which language and data stack does your team use, and do you model many markets?
Robyn, from Meta
- In its own words: an experimental, AI/ML-powered and open-sourced MMM package from Meta Marketing Science.
- Method: ridge regression, with automated adstock and saturation search (Nevergrad) and Prophet for seasonality.
- Workflow: many candidate models to choose from; experiment results as calibration inputs.
- Output: point estimates per channel, with a budget allocator.
- Language: R and Python, its repository states.
Meridian, from Google
- In its own words: an open-source MMM built by Google, designed for causal inference.
- Method: Bayesian, in Python on TensorFlow Probability and JAX, with calibration from experiment results.
- Geo: its repository describes large-scale geo-level data, national-level use, and reach and frequency data.
- Output: a posterior distribution per channel, with credible intervals and budget optimisation.
PyMC-Marketing, from PyMC Labs
- In its own words: an open-source Python library for Bayesian marketing analytics, maintained by PyMC Labs.
- Built on PyMC, a general probabilistic programming library: wide control over structure and priors.
- Geo and calibration: a geo-level example in its documentation; lift-test results as calibration inputs.
- Output: posterior distributions, alongside customer lifetime value and customer choice models.
The three compared, dimension by dimension
- Approach: Robyn, ridge regression with point estimates; Meridian and PyMC-Marketing, Bayesian with credible intervals.
- Language: Robyn, R and Python, per its repository; Meridian and PyMC-Marketing, Python.
- Prior knowledge: Robyn, calibration constraints and hyperparameter ranges; the other two, priors on each parameter.
- Flexibility: most room in PyMC-Marketing; Meridian, a defined model with many options; Robyn, automated search.
- Speed: Robyn fits quickly; Bayesian sampling takes longer with many channels and regions.
Why uncertainty is usually the deciding difference
- The planning-meeting question: how confident is the model?
- Robyn: the answer rests on the spread across candidate models and the analyst's choice.
- Meridian and PyMC-Marketing: each effect is a distribution, so a plan can show a range of outcomes.
- Outside review by finance or a client's data team often decides the choice before language does.
- Reporting changes too: ranges replace single numbers, which often takes longer than the modeling.
How each takes in experiment results
- All three accept experiment results, which help separate cause from coincidence.
- Robyn: calibration constraints that favour candidate models agreeing with the tests.
- Meridian and PyMC-Marketing: a measured lift becomes a prior or likelihood term, shaping the fit.
- The team plans and runs the tests, then translates the result.
- Errors creep in there: match the test's units, period and spend level to the model's.
If you built on LightweightMMM
- Status: its repository notes that as of 29 January 2025 Google released Meridian as its official Bayesian MMM.
- No longer supported: the repository says so, and highly recommends switching.
- Where to go: Meridian, the direct successor, has a migration guide; PyMC-Marketing is the other Bayesian option.
What none of the three provides
- None ships an interface, connectors or support contract: pipelines, refreshes and reporting are the team's work.
- Experiments too: choosing regions, protecting the test window and reading the outcome.
- None is causal on its own: all three learn from history.
How to choose
- You need to report uncertainty, or results face outside review: Meridian or PyMC-Marketing.
- You work in R and want fast, automated model search: Robyn.
- You model many regions in the Google data stack: Meridian.
- You want the most control over model structure and priors: PyMC-Marketing.
- You have no spare modeling capacity: a commercial platform, with a library as an optional independent check.
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Moving between libraries
- Robyn to Bayesian is the most common switch, usually for uncertainty in reports; the cleaned data carries over.
- Expect wider ranges for thin channels, and explain them before the first review.
- Between Meridian and PyMC-Marketing: re-express structure and priors, then compare channel by channel.
- Run both for one full planning cycle, and keep experiment records independent of any library.
Questions
Is Robyn Bayesian?
No. Robyn uses ridge regression with an automated hyperparameter search and returns point estimates. Meridian and PyMC-Marketing are Bayesian.
What is the difference between Meridian and PyMC-Marketing?
Both are Bayesian Python libraries. Google's Meridian, on TensorFlow Probability, is designed for geo-level data. PyMC Labs' PyMC-Marketing, on PyMC, gives more control over model structure.
Is LightweightMMM still supported?
No. Its repository states that LightweightMMM is no longer supported and recommends switching to Meridian.
Which open-source MMM library is best?
It depends on the team. Robyn suits R teams wanting fast model search, Meridian suits geo-level modeling in the Google stack, and PyMC-Marketing suits teams wanting maximum control over a Bayesian model.
Do open-source MMM libraries run experiments?
No. All three can use experiment results for calibration, but designing and running the tests is left to the team.