An independent comparison of the three families of marketing mix modeling tools - open-source frameworks, enterprise SaaS, and modern decision-econometrics platforms - so you can pick the right MMM tool for your data maturity, refresh cadence and decision tempo.
Examples: Meta Robyn, Google Meridian, LightweightMMM, PyMC-Marketing
Best for: In-house data science teams with engineering capacity who want a model they fully control.
Examples: Nielsen, Analytic Partners, Marketing Evolution, Mass Analytics, Recast
Best for: Large advertisers who want an outsourced model and accept the speed/cost trade-off.
Examples: twenty10 and similar Bayesian, decision-first platforms
Best for: CMO / CFO teams who need a defensible model that drives weekly and monthly decisions, not an annual deck.
Score every candidate against the same five axes:
twenty10 is a modern decision-econometrics platform: Bayesian MMM, always-on data pipes, monthly refresh, scenario simulator, budget optimiser and direct decision recommendations - delivered as a service so the data science work is done for you. Typical engagements deliver 10-30% marketing efficiency uplift, £1m-£20m profit gains per programme and 90%+ forecast accuracy.
See the Services page for the full Decision Econometrics scope, or jump to Case Studies for proven outcomes.
There is no single 'best' MMM tool - it depends on data maturity and how you want to use the output. Open-source frameworks (Meta Robyn, Google Meridian, LightweightMMM, PyMC-Marketing) are flexible and free but need a data science team to operate. Enterprise SaaS (Nielsen, Analytic Partners, Mass Analytics, Recast) is turnkey but slow and expensive. Modern decision-econometrics platforms like twenty10 combine Bayesian MMM with always-on data, scenario simulators and decision layers for monthly refresh cadence.
Yes. Robyn is an open-source MMM package from Meta, released under the MIT licence. It is free to use but requires a data science team to deploy, maintain, refresh and govern. The total cost of ownership for a self-hosted Robyn build typically includes 1-2 senior data scientists plus data engineering support.
Both are open-source Bayesian MMM frameworks. Robyn (from Meta) uses Nevergrad for hyperparameter search and supports ridge regression and Bayesian outputs. Meridian (from Google) is a fully Bayesian framework written in TensorFlow Probability, with stronger geo-hierarchical modelling and built-in incrementality calibration. Choice usually comes down to team familiarity and whether you need geo-level effects.
Enterprise MMM platforms typically cost £100k-£500k+ per year, plus services. Modern decision-econometrics platforms are usually priced per market or model rather than as enterprise licences, and start meaningfully lower. Open-source tools have zero licence cost but require an internal data science team (£150k-£300k loaded cost per FTE).
For open-source frameworks, yes - you need at least one senior data scientist and engineering support. For enterprise SaaS, the vendor runs it but you still need an analyst to interpret and brief stakeholders. Modern decision-econometrics platforms aim to remove the data-science overhead by automating data pipes, refresh and quality checks.
Score each candidate against five axes: (1) refresh cadence achievable in practice, (2) transparency of methodology and posteriors, (3) calibration with experiments, (4) decision support beyond raw model output (scenario simulators, optimisers), (5) total cost of ownership including internal time. The right answer changes as your data maturity grows.
Start with a free Clarity Score - a 10-minute benchmark of your measurement capability and the right next step.
Start your Clarity Score