What you need to know about open-source marketing mix modeling

Every marketer wants to know one thing: what is the true impact of my advertising spend?

Marketing mix modeling (MMM) promises to deliver the answer. But building a reliable MMM model yourself with open-source tools isn’t easy. Nor is it as low-cost as it might appear upfront, as you have to build and manage a reliable data pipeline. This article explores the pros and cons of DIY MMM and when a managed platform becomes the smarter choice.

What is open-source marketing mix modeling?

Think of it as a "do-it-yourself" option. Open-source MMM gives you the freedom to construct something that’s specifically tailored to your marketing needs. The software and tools are free and publicly available for users to access, modify and distribute code.

Basically, it's all out in the open — you’re not limited to a vendor’s design. So, if you have a particularly quirky data setup or a specific modeling approach in mind, you can build exactly what you need. 

Benefits of an open-source MMM data platform

Open-source marketing mix modeling runs on a massive, collaborative ecosystem. Data scientists and developers worldwide contribute to making these tools better and smarter. 

It’s a hotbed of innovation. The constant experimentation and sharing of knowledge are helping push marketing measurement in the industry.

And let's be real, every business has its complexities when it comes to marketing data. Open-source MMM gives you the power to build models that reflect these nuances, whether it's incorporating offline touchpoints, accounting for unusual customer behavior or integrating custom data sources. 

Open-source democratizes access to more advanced modeling capabilities, making them available to a wider range of businesses with the technical expertise to use them.

Why has MMM become so popular?

Marketing mix modeling isn’t a new concept. Actually, it first made waves in the 1970s and 80s. But why is there renewed interest in it more than 50 years later? 

The rise of a privacy-first world

With stricter regulations from the GDPR and CCPA and the phasing out of third-party cookies, it’s becoming increasingly hard to get a complete and accurate view of the entire customer journey. 

Attribution alone just isn't cutting it anymore. Sure, it gives you a granular view of digital touchpoints. But it often misses the bigger picture, including activities that happen before the click and across different channels.

Marketing mix modeling jumps in as a smarter alternative by using aggregated data. It provides a more holistic view across all your marketing efforts, giving you clearer insights into what’s really driving growth. 

The need for a more holistic view 

Customer journeys are not linear. They often go through many online and offline touchpoints before they can get to where you want them. 

Marketers need to understand how different elements of their marketing mix interact and contribute to their overall business outcomes. And channel-specific marketing analytics don’t show the impact and relationship between channels. 

Marketing mix modeling is an advanced marketing measurement tool that provides a comprehensive look into all marketing tactics, including offline marketing channels and external factors such as seasonality, competition and consumer confidence. 

A better way to optimize spend 

Companies may be wasting between 40% to 60% of their digital advertising budgets, according to a Proxima Group study. That’s what happens when decisions are made with incomplete or misleading data. The report highlights widespread inefficiencies driven by fragmented data, poor measurement and overly complex media ecosystems. 

You might be throwing money at channels that aren't delivering, or underspending in areas with massive potential. That’s why having a clear understanding of your ROI and ROAS is absolutely important for making smarter, more profitable marketing decisions. 

With MMM, you can quantify the impact each marketing activity has on your business outcomes. This means you can confidently allocate your marketing investments based on what’s driving better results. 

Open source MMM vs. SaaS solutions

How does open-source MMM stack up against a ready-made SaaS solution? Let's unpack how these options differ and the potential impact on your business.

Open-source MMM 

The core appeal of open-source marketing mix modeling is that you get to be in the driver's seat. You can get directly into the code to tailor models made for your business and data needs.

For example, if your team relies on Python and uses libraries like Pandas to organize data and Matplotlib to visualize it, then an open-source MMM tool like Pymc-Marketing, also a Python native, will be a natural fit. 

Your team can weave the MMM code directly into existing scripts. This means they can leverage the tools and languages they already know and trust to create a more streamlined and efficient analysis process.

Plus, you get complete visibility into how the model is working, and that’s just not possible with black-box solutions. 

But there’s a catch — while the software itself is free, free doesn’t mean easy. 

You'll need your in-house data science, statistics and programming expertise to make it work. Open-source MMM doesn’t magically solve those inherent upstream and downstream challenges.

You're still responsible for ensuring clean and consistent data feeds, validating the model's outputs and communicating those insights to your stakeholders. And that’s where the majority of the work lies. 

SaaS solutions 

A SaaS solution is pretty much plug-and-play. SaaS providers take care of the infrastructure, software updates and ongoing maintenance, and give you pre-built workflows with a user-friendly interface. 

This ease of use makes marketing mix modeling more accessible to marketing teams, even if they aren’t fluent in Python or R. Your team can focus on understanding and using the insights, rather than getting lost in the technical details. 

SaaS platforms come equipped with integrated data connectors that integrate with a wide range of marketing platforms. Your data flows in automatically, without complex coding or manual wrangling, which means you can start generating actionable insights much faster. 

And, as your marketing efforts and data volume grow, SaaS solutions are designed to scale seamlessly without requiring you to overhaul your systems or manage complex infrastructure upgrades. But this convenience often means less flexibility and fewer options for highly customized modeling. 

So, are there smarter MMM solutions that understand the need for customization and long-term viability? Modern data integration solutions like Funnel offer advanced marketing measurement tools so you can optimize your media mix for more accurate, reliable insights. 

Even if a managed solution is a better fit for your business in the long run, you might still opt to explore open-source marketing mix modeling software. So, which platform is worth trying?

Key players in open-source MMM software

Here are some of the most popular open-source MMM software options in 2025.

Robyn

Developed by Meta, Robyn MMM is great for analyzing campaigns across Facebook, Instagram and other Meta platforms. It’s also built for cross-channel analysis. 

It brings serious AI smarts to the table, using automated hyperparameter optimization powered by Nevergrad — Meta's AI-powered framework— to minimize manual tweaking and human biases.

Under the hood, it juggles multi-objective evolutionary algorithms, time-series decomposition and Ridge Regression, all within the R language framework. This combo works together to analyze data and optimize marketing spend. There are even automated budget allocation suggestions. 

Instead of relying solely on historical trends, Robyn allows you to calibrate model predictions using results from real-world experiments, such as lift tests or geo experiments. This helps produce more accurate insights grounded in actual, measurable impact.

Limitations:

• R-based: If you're a Python team, expect a dual-language environment.
• Stable data needed: Predictive power hinges on consistent marketing and good data.
• Data prep essential: Requires well-structured historical data and clear KPIs.

Pymc-Marketing 

Pymc-Marketing is built using the probabilistic programming framework PyMC (now PyMC3/PyMC). It uses the Bayesian approach to MMM, meaning you can include your existing marketing knowledge and beliefs directly into the model.

Instead of providing a single estimate for the impact of a marketing activity, it offers a range of likely outcomes, each with a probability of occurring.

Where Pymc-Marketing really shines is its ability to model how marketing effectiveness evolves. Whether it's accounting for market saturation or shifts in consumer behavior, it can capture those changes. 

Plus, as part of the broader PyMC ecosystem, new features and functions are added by both the PyMC Labs team and the open-source community. 

Limitations:

• Python-based: If your team speaks fluent R, they’ll need to brush up on Python. 
• Bayesian understanding needed: Requires knowledge of Bayesian statistics and programming to understand insights.
• Hands-on configuration: Expect manual model tuning and longer runs for large datasets and complex models. 

Meridian 

Fresh on the scene and backed by Google, Meridian is their more advanced, actively evolving open-source MMM framework, a significant step up from their earlier, more basic model.

Meridian uses a Bayesian approach but with Google's specific methodologies. For instance, it’s able to handle geo-level data, giving you more granular insights at a regional or local level. 

If videos are a big part of your marketing strategy, it can also incorporate reach and frequency data, especially for YouTube campaigns. So, you can see the impact beyond just simple impressions. 

For paid search marketing campaigns, Meridian uses Google Query Volume as a control variable for a more accurate read. It's powered by Google's TensorFlow Probability library, meaning it can crunch numbers fast, especially if you have GPUs at your disposal.

Limitations: 

• BYO Data Pipeline: You’re responsible for data integration, cleaning, and preparation. 
• Lack of reporting tools: No built-in, user-friendly reporting interface.
• Channel-focused: Optimization is broader, not campaign-specific.

Orbit 

Uber is also a player in the game of open-source software with Orbit. Built in Python and leveraging the probabilistic modeling of Stan, Orbit takes a time-centric approach to understanding marketing impact. While primarily a time-series forecasting library, its functionalities can be applied to MMM.

Orbit is great for modeling how your marketing effectiveness and other factors change over time. This is especially useful for capturing changes in market response or the long-term effects of campaigns.

For example, you might see how the impact of a big brand awareness campaign gradually builds sales over several months, rather than providing an immediate spike.

Its Kernel-based Time-Varying Regression (KTR) feature lets you adjust the impact of marketing variables as time progresses. You can even throw in external factors like your ad dollars to understand their direct effect on your business metrics, like sales or conversions.

Limitations: 

• Not full-service MMM: Primarily a forecasting tool. 
• Future-focused: Prioritizes future trends over deep historical analysis.
• DIY MMM features: Requires DIY implementation of key MMM functions like adstock and saturation.

What to consider before using open-source MMM software

Open-source marketing mix modeling might sound like a promising option, but the costs in time and resources of “free” open-source software can quickly add up. 

Data Infrastructure and ETL

Before even thinking about MMM models, take a hard look at your data. The quality of your MMM output is only as good as your data. 

János Moldvay, VP of Measurement at Funnel, says that the model is only 20% of the work. The other 80% is consolidating and transforming data. ETL pipelines are the real bottleneck.

Marketing mix modeling thrives on historical trends, so patchy or incomplete data will lead to unreliable insights. You can build the most powerful model, but it’s useless if you don’t have a trustworthy and consistent flow of data. 

And let's not forget data integration challenges. Ad platforms, social media analytics, CRM, sales databases, just to name a few. Getting all these different data sources to speak the same language and play nicely together can be challenging. 

Imagine you’re part of an analytics team at a rapidly growing ecommerce company. Initially, the freedom to add custom variables like influencer engagement metrics or pop-up foot traffic was attractive, but soon you realize you’re in over your heads when your ETL pipelines keep breaking with API changes. 

Open-source also doesn't come with built-in scalability. As your marketing efforts change and data grows, you'll need to make sure your DIY infrastructure can handle the pressure without crashing. 

In-house technical team

Open-source marketing mix modeling may be license-free, but there are a lot of hidden costs — a skilled in-house technical team is at the top of the list. 

Implementing and understanding open-source MMM demands that you have an in-house team that is deeply knowledgeable in data science. You need folks who can not only run the code but also understand the assumptions, diagnose issues and validate the results. 

They need to be proficient in either R or Python and comfortable writing and debugging code and manipulating data within these environments. And they’ll also be in charge of getting the right data in the right format since the success of your model hinges on it. 

This is where many teams struggle. Maybe despite strong Python skills, your ecommerce analytics team underestimated the time and effort required for pipeline maintenance and data validation. So, they end up being pulled away from other critical analytical projects just to keep the data flowing. 

Model validation and biases 

Just because an open-source MMM model spits out numbers doesn't mean they're 100% accurate. It can produce misleading results if it’s not properly validated. So you need to test its accuracy and reliability to avoid making costly decisions.

A classic example is diminishing returns. The first dollar you spend on a channel might bring in a lot of conversions, but keep throwing money at it, and eventually, it’ll become less effective. If your model isn't validated to catch this saturation point, it might keep telling you to invest more into an already maxed-out channel. 

Let’s say your open-source model suggests your analytics team double the budget on Meta Ads. Despite seeing conversion rates flatline, you start pushing more budget into the platform, only to see your ROAS plummet. What was supposed to be a strategic move drains your budget because your model's insights weren't properly validated for channel saturation. 

MMM can sometimes over-attribute sales to branded search, too. It might give too much credit to the last click without fully appreciating the upper-funnel work that got your customers searching in the first place.

How do you make sure your open-source MMM is giving you accurate and trustworthy insights? 

• Parameter recovery exercises: Give your model a pop quiz. Can it correctly identify the impact of different marketing levers on simulated data? Red flag if not.
• Out-of-sample forecast accuracy: Hold back some real data and see if the model's predictions match what actually happened.
• Comparison with incrementality tests: Compare model results to real lift from experiments (A/B, geo). 

The DIY approach 

The trade-off for the flexibility and cost savings of open-source is that you're largely your own support system.

Open-source marketing mix modeling relies heavily on community forums, online discussions and documentation for support. While it can be a valuable resource, it’s not the same as having a dedicated vendor.

You might not get immediate help when facing critical issues, since help depends on the availability and willingness of community members. And there’s no guarantee of the quality or accuracy of the advice you do receive.

Your ecommerce analytics team might hit a cryptic error deep in your open-source model's code. You spend days sifting through forum posts, hoping for a community member to offer a solution. But that doesn’t happen, so you have to delay an important quarterly report until you can find a consultant to help. 

Even with a dedicated data science team, diagnosing and resolving complex issues is time-consuming. It demands a deep understanding of the code and statistical methodologies to even begin to identify the problem. 

Why Funnel is a smarter MMM solution

Open-source MMM tools are powerful — but they’re also narrow. Most offer just one type of model (usually MMM), require heavy lifting to integrate and stop short of turning insights into action.

But, Funnel goes beyond that. We handle the MMM work that most teams underestimate: data integration, model validation, optimization and reporting.

Essentially, we’re a full-stack measurement engine designed to work with your entire business and evolve with your needs.

Triangulated measurement, not siloed models

Funnel doesn’t just give you single-method answers that can mislead in isolation. Instead, our advanced measurement integrates MMM, multi-touch attribution (MTA) and incrementality testing. 

Marketing mix modeling is used for long-term strategic budget planning across online and offline channels. Multi-touch attribution is powered by LSTM models for granular click-level attribution. And, incrementality testing is used to validate causality and deliver trustworthy results.

This triangulation method is how Funnel delivers robust, cross-validated insights and pinpoints what’s really driving growth.

Built-in automation and AI optimization

Open-source tools require manual model tuning, offer no visual scenario planning and stop at historical insight — they don’t help you optimize future spend. 

Funnel’s built-in automation and AI does all that. It provides daily-updated AI models that refresh automatically with your newest data. 

You also get powerful scenario planning tools and saturation curve visualizations that go beyond historical insights. This means you can actively forecast returns and predict media saturation. 

Plus, we deliver campaign-level ROAS and CPA predictions, coupled with budget reallocation recommendations to help guide decision-making. 

True end-to-end integration and activation

As many teams moving from open-source tools like Robyn have discovered, most of the work lies in consolidating and transforming scattered marketing data.

Funnel’s Data Hub connects all your different data sources, including GA4, BigQuery, ad platforms, CRMs and CDPs, and even offline data sources. We automate everything from data ingestion and transformation to model deployment. The result is a unified and harmonized dataset that generates trustworthy MMM outputs. 

But the real magic happens with reverse ETL, which pushes your insights directly back into your ad platforms for smarter bidding and optimization. 

While open-source solutions typically require custom ETL scripts, manual updates and engineering support to stay functional, Funnel has all that handled. 

Strategy to execution, all in one place

Funnel doesn’t just explain the past — it helps you shape the future, too. We provide the tools to turn insight into action so you can make confident, data-driven decisions that lead to better business outcomes. 

You can run "what-if" simulations to test your media strategy and see how different budget allocations might impact sales before you commit a single dollar. 

Predictive insights help you avoid overspending and media saturation. And intuitive dashboards that bridge marketing, data science and leadership make collaboration effortless. 

You can confidently show stakeholders exactly which marketing channels are delivering real results and why you made specific decisions. 

Enterprise-ready: support, scalability and reliability

Funnel evolves with your business and helps you build and implement MMM models that are exactly what you need — you’re never on your own.

Open-source is free to start, but it’s costly to maintain. You’ll need internal experts in Python, Bayesian stats and data engineering to keep things running, with no guaranteed support or updates.

Funnel, on the other hand, evolves with your business. As your data explodes, your channels multiply and your team grows, we scale with you. Our platform is built for scalability and reliability and handles increasing data volume without a hitch, which means less workload for your engineering teams. 

Funnel works hand-in-hand with your team to understand your specific marketing goals, data landscape and analytical requirements. You'll receive ongoing support and onboarding to ensure a smooth start and continuous success. 

Our robust foundation ensures your marketing mix modeling processes remain efficient and reliable, no matter how big you get.

The open-source MMM reality

Open-source MMM tools are great for experimentation. But when you’re ready to scale measurement across your organization, drive business decisions in real-time and unify siloed methods into one trusted source of truth, Funnel delivers.

For a simpler path to powerful and reliable MMM, schedule a Funnel demo today.