9 Best Marketing Mix Modeling Software Tools in 2026

Marketing mix modeling doesn't rely on cookies or user-level tracking, which matters more each time privacy rules or browser changes make that tracking less complete. The question it answers is simple to ask and hard to get right: if we moved a dollar from one channel to another, what would happen to sales? The software below ranges from free code libraries to fully managed services.

What marketing mix modeling software does

• Models contribution. Estimates how much each channel, plus baseline demand, seasonality and promotions, drove the outcome you care about.
• Captures carryover and saturation. Accounts for ads working after they run and for spend that stops paying back past a point.
• Calibrates with experiments. Uses geo tests and lift studies to keep the model honest.
• Plans budgets. Turns response curves into recommended spend by channel and what-if scenarios.

MMM works from aggregate spend and outcome data, so it can include offline channels such as TV and out-of-home. The trade-off is granularity: it answers at channel or campaign level, not for each ad. If you need touchpoint-level credit, our roundup of marketing attribution software covers that category, and many teams use both.

How we picked these tools

Each tool had to offer marketing mix modeling as a product or library available in October 2026 and document its method or packaging on its own site. We read the documentation, product pages and pricing pages on the vendor's site or official code repository in October 2026. Most of the commercial tools here do not publish prices, and we say so. We left out vendor accuracy and results figures, because they cannot be checked from outside.

At-a-glance comparison

The best marketing mix modeling software

1. Google Meridian: Google's open-source MMM

Meridian is an open-source MMM built by Google. It can model reach and frequency for video, include search query volume to control for organic demand, and run budget optimization and scenarios. Meridian GeoX converts geo experiment results into priors for the model, and Google's MMM Data Platform helps gather Google Ads and Display & Video 360 data for it.

Pricing (as of October 2026): free under the Apache-2.0 license. Strengths: detailed documentation and a direct route to Google media data. Limits: it needs Python 3.11 to 3.13, and Google recommends at least one GPU, so it is a data science project, not a dashboard.

2. Meta Robyn: automated MMM from Meta

Robyn is an experimental, open-source MMM package from Meta Marketing Science. It uses ridge regression, Meta's Nevergrad library for automated hyperparameter search and Prophet for trend and seasonality, can be calibrated against geo tests, lift studies and multi-touch attribution, and includes a budget allocator. It needs no personal data or user-level logs.

Pricing (as of October 2026): free under the MIT license. Strengths: automation that cuts manual model tuning, and one-pagers for comparing candidate models. Limits: the main package is in R; the Python version is a beta translated with an LLM, and the project describes it as possibly buggy.

3. PyMC-Marketing: Bayesian MMM and CLV in Python

PyMC-Marketing is an open-source Python library built and maintained by PyMC Labs. It implements Bayesian MMM with adstock and saturation components, lift-test calibration and budget optimization, plus customer lifetime value models, all with full uncertainty estimates. Our roundup of customer lifetime value tools covers ready-made CLV products if you don't want to build models.

Pricing (as of October 2026): free under the Apache-2.0 license, installed with pip on Python 3.12 or later. Strengths: flexible for modelers who want to change the model itself. Limits: you need Bayesian modeling and Python skills to use it well.

4. Recast: MMM as planning infrastructure

Recast runs a proprietary Bayesian MMM that handles time-varying ROI, channel interactions, promotional spikes and multi-stage funnels. GeoLift by Recast designs and reads incrementality experiments and feeds the results back into the model, and the platform builds forecasts, plans and budget allocations under real caps and floors. Public APIs and an MCP server connect it to tools such as Looker, Slack and Notion.

Pricing (as of October 2026): not published; book a demo. Strengths: one model used for measurement, forecasting and planning. Limits: pricing is not published, and each deployment is configured with the Recast team or a certified partner rather than set up self-serve.

5. Haus: Causal MMM built on experiments

Haus is an incrementality platform, and its Causal MMM is built on your experiment results, updated weekly, with scenario planning. Architect, its agentic recommendation engine, turns scenarios into action plans.

Pricing (as of October 2026): four plans (Basics for Shopify, Core, Plus and Enterprise), all through a demo; Causal MMM is included on Plus and Enterprise. Strengths: experiments come first, so the model has ground truth to lean on. Limits: MMM is not on the entry plans.

6. Lifesight: MMM inside unified measurement

Lifesight combines causal MMM, geo-lift incrementality testing and causal attribution in one platform, with scenario-based media planning, forecasting and AI agents that also work in Claude and ChatGPT through MCP.

Pricing (as of October 2026): Performance, Precision and Enterprise plans, all through a demo; causal MMM with geo-test calibration is in Precision and Enterprise. Strengths: three measurement methods under one roof. Limits: the entry Performance plan does not include MMM.

7. Mutinex: a commercial mix model for enterprises

Mutinex calls its product an agentic commercial mix model, spread across DataOS for structuring data, GrowthOS for modeling and MAITE, which it describes as an AI consultant for instant analysis and recommendations. It also publishes an open-source framework for validating MMM vendors.

Pricing (as of October 2026): not published; request a demo. Strengths: data preparation, modeling and an AI analysis layer from one vendor. Limits: pricing is not published, and its on-demand GrowthOS option was in early access when we checked.

8. Funnel Measure: MMM on top of your marketing data hub

Funnel Measure is the measurement add-on to Funnel's Data Hub. It combines MMM at channel and campaign level with multi-touch attribution and incrementality testing, calibrating them against each other, and adds budget optimization and scenario planning. Because it runs on Funnel's own data foundation, you do not need a separate warehouse.

Pricing (as of October 2026): an add-on to the Business (from $600 a month billed annually) or Enterprise Data Hub plans, priced by average monthly ad spend; for $500K to $1M a month, Funnel's calculator shows an estimated $2,250 a month billed annually for Digital Measurement. Strengths: data collection and modeling in one vendor. Limits: no ad-set or creative-level insights, which Funnel itself says are statistically improbable.

9. Analytic Partners: enterprise commercial analytics

Analytic Partners combines its GPS Enterprise platform with a team of experts for marketing mix modeling, incrementality testing, brand impact, pricing optimization and customer segmentation. It acquired London-based Magic Numbers in July 2024 and Analyx, based in Germany and Poland, in January 2025.

Pricing (as of October 2026): not published; contact the company. Strengths: models that include factors beyond media, such as pricing. Limits: an enterprise engagement, not a self-serve tool.

How to choose

Start with your people. A team with Python or R modelers can get far with Meridian, Robyn or PyMC-Marketing at no license cost, and should budget the time to validate and refresh the model. A team without them should buy a platform: Haus or Lifesight if you also want to run experiments, Recast if forecasting and planning matter most, Funnel Measure if your data already flows through Funnel, and Mutinex or Analytic Partners for enterprise programs with many brands or markets.

Then check your data. MMM needs a steady history of spend and outcomes by week, ideally by region. If your reporting is still assembled by hand, fix that first with one of the best marketing analytics tools and a clear marketing analytics dashboard. Once the model says where budget should go, the best AI marketing tools can help produce more of what works.

Frequently Asked Questions

What is marketing mix modeling software?
Marketing mix modeling (MMM) software estimates how much each marketing channel, and factors such as seasonality and promotions, contributed to sales or another outcome, using aggregate spend and results data over time rather than tracking individual users. Most tools then use the model to recommend how to reallocate budget.

Is there free marketing mix modeling software?
Yes. Google Meridian, Meta Robyn and PyMC-Marketing are open-source libraries you can use without a license fee. The cost is the data science time to prepare data, build, validate and refresh the models.

What is the difference between MMM and attribution?
Attribution assigns credit for conversions to the touchpoints it can track at user level. MMM works from aggregate data, so it can include channels that cannot be tracked, such as TV or out-of-home, but it answers at channel or campaign level rather than for each ad.

Why do MMM vendors talk about experiments?
A model can fit past data well and still misjudge a channel. Geo experiments and lift tests give a measured result to calibrate against, which is why Meridian GeoX, GeoLift by Recast, Haus and Lifesight all tie experiments to the model.