9 Marketing Mix Modeling Tools for Accelerating Growth in 2025

Marketing Mix Modeling (MMM) has become a vital tool for businesses looking to optimize their marketing investments. In a world where attribution tools are still broadly used in cross-channel budget allocation, MMM provides an alternative for measuring media ROI based on true incremental sales impact of each channel.

Modern MMM tools are more accessible and powerful than ever. But how should one choose which tool to adopt? In this article, we'll revisit our evaluation criteria for choosing an MMM tool, as well as provide alternatives you can start evaluating.

What is Marketing Mix Modeling?

Marketing Mix Modeling (MMM) is a statistical analysis technique to measure how marketing campaigns and channels are driving sales. By analyzing historical data, MMM is able to explain how different channels, such as Google Performance Max and Meta Advantage+, contribute to sales. It quantifies the return on investment (ROI) for media spend, enabling data-driven media budget optimization decisions. Below is an illustration of how MMMs work:

Next Gen MMM solutions have evolved beyond traditional time-series models: they leverage incrementality tests and attribution data to uncover the true ROI of each channel. For an in-depth overview of MMM, check the Sellforte Marketing Mix Modeling guide .

How to Choose a Marketing Mix Modeling Tool?

Sellforte’s Buyers Guide to MMM Tools provides a full in-depth explanation of what to look for when evaluating Marketing Mix Modeling tools or platforms. Below you'll find the summary with icon ✅ indicating a basic feature in most MMMs, and icon 💎 indicating features you typically find from most advanced MMM platforms, Next Gen MMMs.

1. Choose an MMM Tool That Helps You Drive More Sales

Your primary goal in is to ensure that the Marketing Mix Modeling solution includes optimization capabilities that improve marketing ROI and enhance sales performance. Here’s a checklist covering key optimization levers:

Basic MMM features:
✅ Advertising channel-level optimization

Next Gen MMM features:
💎 Campaign-level optimization
💎 Optimal weekly budget pacing

2. Choose an MMM Tool That Produces High Quality Results

Once you've checked that the Marketing Mix Modeling tool you're evaluating has the required optimization capabilities that help you drive more sales, you need to determine whether you can trust the MMM results sufficiently to make data-driven decisions based on its recommendations.

Here’s the reality: many Marketing Mix Models on the market produce low-quality, unreliable results. Why? While generating MMM results has become easier than ever with free-to-use modeling libraries, the real challenge remains: MMM datasets are often noisy and plagued by multicollinearity. This makes building stable, high-quality models that generate accurate results challenging.

Fortunately, there are indicators that can help you assess whether an MMM vendor delivers high-quality results. Here’s a checklist:

Basic features:
✅ Statistical model validation
✅ Bayesian approach

Next Gen MMM features:
💎 MMM is calibrated with informative priors
💎 In-built Causal Attribution Model for generating informative priors
💎 Advanced model validation based on model outputs

3. Choose an MMM Tool That Is Easy to Test & Start with

Now that you’re sure that your MMM provides the right optimization insights and delivers high-quality modeling results, the next step is to assess how easy it is to get started with the platform. At Sellforte, we’re particularly passionate about ensuring that MMMs are both user-friendly and accessible—without sacrificing the accuracy and reliability of the results. Here's your checklist:

Basic features:
✅ Data connectors

Next Gen MMM features:
💎 Delivers high quality results in 1-2 weeks
💎 Provides a Free Trial

Which Marketing Mix Modeling Tool Should I Use?

While choosing a Marketing Mix Modeling tool is ultimately a decision for you to make, the evaluation framework in the previous chapter should provide a good starting point for comparing different MMM tools and companies. To get you started here's a list of 9 Marketing Mix Modeling tools:

1) Sellforte (MMM SaaS)

Sellforte is a Next Gen Marketing Mix Modeling platform for Retailers, eCommerce businesses and DTC brands:

• Drive +6.5% more sales: Sellforte typically unlocks for eCom / DTC brands ( read the full research )
• Onboarding is fast & easy: Users spend 15-30min connecting their data with in-built data connectors, and MMM results are available in 1-2 weeks.
• High Quality MMM results: Sellforte uses Bayesian Marketing Mix Models with in-built causal attribution model that enables model calibration with experiments and attribution data.
• Campaign-level results: Get ROI and recommended budget for each campaign.
• Geo lift analysis is integrated into the platform, for validating MMM results, and calibrating the MMM.

Sellforte offers a free trial for qualifying eCom / DTC brands and retailers to test the platform.

2) Meridian (Build-it-yourself MMM)

Meridian enables organizations to develop customized Marketing Mix Models. Like Sellforte, Meridian leverages Bayesian MMM approach. It incorporates several advancements studied by Google Research teams over the years, providing a powerful toolkit for skilled analysts and data scientists. With Meridian, users can:

• Build models using Python
• Analyze key outputs, such as Marketing ROI and diminishing return curves.
• Optimize media budget allocation by creating scenarios in Python and reviewing the results as plotted graphs.

Meridian accelerates the process of developing Bayesian Marketing Mix Models, saving data scientists the effort of manually implementing essential MMM components like adstock effects and diminishing returns.

This solution is best suited for companies with experienced data science and software development teams looking to build a highly customized MMM framework almost from scratch. However, compared to a comprehensive MMM platform like Sellforte, Meridian lacks a user-friendly interface for marketers to conduct analyses and optimize media budgets. Additionally, it does not include built-in data connectors for ad platforms, automated data processing pipelines, or automated model calibration workflows.

3) Robyn MMM (Build-it-yourself MMM)

Robyn , developed by Meta, is an open-source Marketing Mix Modeling (MMM) library that allows businesses to create customized marketing mix models. It leverages Facebook’s Nevergrad optimization library along with the Prophet time-series forecasting library. Nevergrad applies advanced machine learning techniques—such as Bayesian optimization and genetic algorithms—to fine-tune model parameters. Robyn also utilizes hyperparameter optimization, running thousands of model variations with different parameter settings to identify the most accurate configuration.

Like Meridian, Robyn is best suited for companies with experienced data science and engineering teams looking to build a fully customized MMM solution from the ground up. However, compared to a complete MMM platform like Sellforte, Robyn lacks a user-friendly interface for marketers to analyze results and optimize scenarios. Additionally, it does not include built-in data connectors to ad platforms, automated data processing pipelines, or automated model calibration workflows.

4) PyMC-Marketing (Build-it-yourself MMM)

PyMC-Marketing is an open-source library built on PyMC. Like Sellforte and Meridian, PyMC-Marketing leverages a Bayesian Marketing Mix Modeling (MMM) approach.

PyMC-Marketing is best suited for companies with experienced data science and engineering teams looking to build a fully customized MMM solution from the ground up. However, it lacks features typically found from full MMM solutions, like built-in data connectors, automated data processing pipelines, automated model calibration workflows, or a user-interface for analysis and optimization.

5) Uber Orbit (Build-it-yourself MMM)

Uber’s Orbit library is a lightweight, scalable framework designed for time-series forecasting, making it useful for Marketing Mix Modeling (MMM) applications. Like Sellforte, Orbit leverage Bayesian MMM approach. Orbit's use in MMM was illustrated in Orbit's paper “Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling“

Since Orbit is primarily a forecasting library, it lacks some critical MMM features, such as modeling diminishing returns or adstock. It also lacks features typically found from full MMM solutions, like built-in data connectors, automated data processing pipelines, automated model calibration workflows, or a user-interface for analysis and optimization.

6) Incrmntal (MMM SaaS)

Israeli-based Incrmntal is a 2020-founded MMM Saas startup, with several prominent gaming companies mentioned as their reference customers, such as Sega, Supercell, Gameloft.

7) Recast (MMM SaaS)

Recast is an MMM SaaS that highlights rigorous modeling and model validation as its strength.

8) Cassandra (MMM SaaS)

Cassandra is an Italian MMM SaaS startup, founded in 2022.

9) Circana (MMM consulting)

Circana is a marketing analyitcs consulting company, who also provide Marketing Mix Modeling consulting services.

Is Sellforte the Marketing Mix Modeling tool you’re looking for?

Marketing Mix Modeling reveals the true ROI of all of your channels, and enables you to optimize your media spend based on true incremental impact of each media.

If you are ready to unlock the full potential of your media budget, start your free trial with Sellforte today .