Marketing mix modeling (MMM) has shifted from an enterprise luxury to an essential measurement tool.

Tech giants like Google, Meta, and Uber have released powerful open-source MMM frameworks that anyone can use for free.

The challenge is understanding which tool actually solves your problem and which require a PhD in statistics to implement.

Open-source MMM tools are often grouped together but solve different problems

The landscape can be confusing because these tools serve fundamentally different purposes despite being mentioned together.

Google’s Meridian and Meta’s Robyn are complete, production-ready MMM frameworks that take your marketing data and deliver actionable budget recommendations.

They include everything needed:

• Data transformations that model advertising decay.
• Saturation curves that capture diminishing returns.
• Visualization dashboards and budget optimizers that recommend spend allocation.

Uber’s Orbit and Facebook’s Prophet occupy different niches.

Orbit is a time-series forecasting library that can be adapted for MMM, but it requires months of custom development to build MMM-specific features.

Prophet is a forecasting component used within other frameworks, not a standalone MMM solution.

Think of it like transportation:

• Meridian and Robyn are complete cars you can drive today.
• Orbit is a high-performance engine that requires you to build the transmission, body, and wheels.
• Prophet is the GPS system that goes inside the car.

Dig deeper: Marketing attribution models: The pros and cons

Robyn: The accessible powerhouse

Meta built Robyn specifically to democratize MMM through automation and accessibility.

The framework uses machine learning to handle model building that traditionally required weeks of expert tuning.

Upload your data, specify channels, and Robyn’s evolutionary algorithms explore thousands of configurations automatically.

What makes Robyn distinctive is its approach to model selection.

Rather than claiming one “correct” model, it produces multiple high-quality solutions that show trade-offs between them.

Some fit historical data better but recommend dramatic budget changes.

Others have slightly lower accuracy but suggest more conservative shifts.

Robyn presents this range, allowing decisions based on business context and risk tolerance.

The framework also excels at incorporating real-world experimental results.

If you have run geo-holdout tests or lift studies, you can calibrate Robyn using those results.

This grounds statistical analysis in experiments rather than pure correlation, improving accuracy and giving skeptical executives evidence to trust the outputs.

However, Robyn assumes marketing performance remains constant throughout the analysis period.

In practice, algorithm updates, competitive changes, and optimization efforts mean channel effectiveness often varies over time.

Meridian: The statistical heavyweight

Meridian represents Google’s Bayesian causal inference approach to MMM.

Unlike Robyn’s pragmatic optimization, Meridian models the mechanisms behind advertising effects, including decay, saturation, and confounding variables.

This theoretical rigor allows Meridian to better answer, “What would happen if we changed budget allocation?” rather than simply, “What patterns existed in the past?”

Its standout capability is hierarchical, geo-level modeling.

While most MMMs operate at a national level, Meridian can model more than 50 geographic locations simultaneously using hierarchical structures that share information across regions.

Advertising may perform well in urban coastal markets but struggle in rural areas.

National models average these differences away.

Meridian’s geo-level approach identifies regional variation and delivers market-specific recommendations that national models can’t.

Another distinguishing feature is its paid search methodology, which addresses a fundamental challenge: when users search for your brand, is that demand driven by advertising or independent of it?

Meridian uses Google query volume data as a confounding variable to separate organic brand interest from paid search effects.

If brand searches spike because of viral news or word-of-mouth, Meridian isolates that activity from the impact of search ads.

The technical complexity, however, is significant.

Meridian requires deep knowledge of Bayesian statistics, comfort with Python, and access to GPU infrastructure.

The documentation assumes a level of statistical literacy most marketing teams lack.

Concepts such as MCMC sampling, convergence diagnostics, and posterior predictive checks typically require graduate-level training.

Dig deeper: How Bayesian testing lets Google measure incrementality with $5,000

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Uber Orbit: The time-varying specialist

Orbit is not technically an MMM tool.

It’s a time-series forecasting library from Uber with a notable feature: Bayesian time-varying coefficients, or BTVC, which address a fundamental MMM challenge.

Imagine presenting MMM results to your CEO, who asks, “This assumes Facebook ads had the same ROI in January and December? But iOS 14 hit in April, and we spent months recovering. How can one number represent the whole year?”

That is the credibility-breaking moment practitioners fear because it exposes a simplifying assumption executives correctly recognize as unrealistic.

Traditional MMM frameworks assign one coefficient per channel for the entire analysis period, producing a single ROI or effectiveness estimate.

• For stable channels like TV, this can work.
• For dynamic digital channels, where teams constantly optimize, respond to algorithm changes, and face shifting competition, assuming static performance is clearly flawed.

Orbit’s BTVC allows channel effectiveness to change week by week.

Facebook ROI in January can differ from December, while the model keeps estimates stable unless the data shows clear evidence of real change.

The reality, however, is that while time-varying coefficients are powerful, Orbit lacks the other components required for a complete MMM solution.

Orbit makes sense only for data science teams building proprietary frameworks that require advanced capabilities and have the resources for significant custom development.

For most organizations, the cost-benefit tradeoff does not justify that investment.

Teams are better served using Robyn or Meridian while acknowledging their limitations, or working with commercial MMM vendors that have already built time-varying capabilities into production-ready systems.

Facebook Prophet: The misunderstood component

Prophet is Meta’s time-series forecasting tool.

It’s highly effective at its intended purpose but is often misrepresented as an MMM solution, which it is not.

Prophet decomposes time-series data into trend, seasonality, and holiday effects.

It answers questions, such as:

• “What will our revenue be next quarter?”
• “How do Black Friday spikes affect baseline performance?”

This is forecasting, or predicting future values based on historical patterns, which is fundamentally different from attribution.

Prophet can’t identify which marketing channels drove results or provide guidance on budget optimization.

It detects patterns but has no concept of marketing cause and effect.

Prophet’s primary role is as a preprocessing component within larger systems.

Robyn uses Prophet to remove seasonal patterns and holiday effects before applying regression to isolate media impact.

Revenue often rises in December because of holiday shopping rather than advertising.

Prophet identifies and removes that seasonal effect, making it easier for regression models to detect true media impact.

This preprocessing is valuable, but Prophet addresses only one part of the overall attribution problem.

Marketing teams should use Prophet for standalone KPI forecasting or as a component within custom MMM frameworks, not as a complete attribution or budget optimization solution.

Dig deeper: MTA vs. MMM: Which marketing attribution model is right for you?

Making the right choice for your team

Making the right choice for your team

Choosing between these tools requires an honest assessment of your organization’s capabilities, resources, and needs.

• Do you have data scientists comfortable with Bayesian statistics and complex Python?
• Or marketing analysts whose statistical training ended with basic regression?

The answer determines which tools are viable options and which are aspirational.

For about 80% of organizations, Meta’s Robyn is the right choice.

This includes:

• Teams without deep data science resources but still need rigorous MMM insights.
• Digital-heavy advertisers seeking attribution without lengthy implementations.
• Organizations that require insights in weeks rather than quarters.

The learning curve is manageable, implementation takes weeks rather than months, and outputs are presentation-ready.

A large, active user community also shares solutions when challenges arise.

Google’s Meridian suits:

• Small and midsize businesses and enterprise organizations with dedicated data science teams comfortable working in Bayesian frameworks.
• Multi-regional operations where geo-level insights would meaningfully influence budget decisions.
• Complex paid search programs requiring more precise attribution.
• Stakeholders who prioritize causal inference over pragmatic correlations can justify Meridian’s added complexity.

Uber Orbit is appropriate only for data science teams building proprietary frameworks with requirements that Robyn and Meridian can’t meet.

The opportunity cost of spending months on custom infrastructure rather than using existing tools is substantial unless proprietary measurement itself provides a competitive advantage.

Facebook Prophet should be used for KPI forecasting or as a preprocessing component within larger systems, never as a complete attribution solution.

Matching MMM tools to real-world team capabilities

The most advanced tool delivers little value if it can’t be implemented effectively.

A well-executed Robyn implementation running consistently provides more value than an abandoned Meridian project that never progressed beyond a pilot.

Tools should be chosen based on what teams can realistically use and maintain, not on the most impressive feature set.

For most marketing teams, Robyn and Meridian represent pragmatic choices that balance performance with accessibility.

Automation handles much of the statistical work, allowing analysts to focus on insights rather than debugging code.

Strong community support and documentation reduce friction, and teams can move from zero to actionable insights in weeks instead of months, which matters when executives want answers quickly.

For enterprises with substantial technical resources and multi-regional operations, Google Meridian can deliver returns through more reliable causal estimates and geo-level granularity that materially improve budget allocation.

The investment in infrastructure, expertise, and implementation time is significant, but at a sufficient scale, better decision-making can justify the cost.

Uber Orbit offers advanced capabilities for organizations that truly need time-varying performance measurement and have the resources to build complete MMM systems around it.

For most teams, commercial vendors that have already incorporated time-varying capabilities into production-ready platforms are more cost-effective than extended custom development.

These open-source frameworks have made marketing measurement accessible beyond Fortune 500 companies.

The priority is choosing the tool that fits current capabilities, implementing it well to earn stakeholder trust, and using insights to make better decisions.

Competitive advantage comes from allocating budgets more effectively and faster than competitors, not from maintaining a technically impressive system that is too complex to sustain.

Dig deeper: How to avoid marketing mix modeling mistakes that derail results

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