25 Marketing Mix Modeling Tools for Accelerating Growth in 2026

Marketing Mix Modeling (MMM) has become a vital tool for businesses looking to measure and optimize their media spend. In a world where last-click attribution is still broadly used in cross-channel measurement, MMM provides an alternative for measuring media ROI based on true incremental sales impact of each channel, campaign and ad set.

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

What is Marketing Mix Modeling (MMM)?

Let's first briefly cover what MMM is. Marketing Mix Modeling (MMM) is a statistical analysis technique to measure how investments in marketing are driving revenue. 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 spend optimization decisions. Below is an illustration of how MMMs work:

While MMMs traditionally have operated purely on time-series data illustrated above, Next-Gen MMM solutions have evolved to integrate even more data into the analysis: they also leverage incrementality tests and attribution data to uncover the true ROI of each channel, campaign and ad set. 

How to Choose a Marketing Mix Modeling Tool?

Before we dive into the specific MMM tools, let's discuss how to choose an MMM tool. Here's three recommendations questions I would look at when choosing a modern MMM tool today:

1. Optimization use-cases: Choose an MMM tool that has optimization capabilities that help your drive most sales
2. Agentic MMM: Choose an MMM tool with AI Agents that automate your planning and optimization workflows

3. Trust: Choose an MMM tool that produces high quality modeling results

1. Optimization Use-cases: Choose an MMM Tool That Covers Use-Cases that Helps You Drive Most Sales 

Your primary goal is to ensure that the Marketing Mix Modeling solution you choose includes optimization capabilities that help you drive sales growth and improve marketing ROI. In our research Unlock 6.5% More Sales with Marketing Mix Modeling, we found out that there's three large levers for driving sales growth. These are illustrated in the picture below:

Let's walk through each of these growth levers.

1. Optimize spend allocation across channels/tactics (+1.6% more sales). In this lever, MMM recommends optimal spend allocation for each channel/tactic, such as Google Performance Max, Meta Advantage+, Meta prospecting. Being able to provide channel/tactic-level spend optimization requires measuring miROAS (Marginal Incremental ROAS) for each channel /tactic. miROAS tells the return for the next spent dollar on the channel.

Below is an example for cross-channel spend optimization from public Sellforte demo.

2. Optimize budget pacing (+2.0% more sales). Budget pacing ensures that your advertising spend is distributed optimally across weeks, preventing for example overinvestment when demand is naturally low and underinvestment when there’s high level of natural demand. Budget pacing requires that the MMM can produce realistic week-level spend recommendations for each channel/tactic, which are based on understanding business-specific demand seasonality and promotional dynamics.

Below is an example of budget pacing recommendation for the next 5 weeks from the public Sellforte demo.

3. Optimize spend allocation across campaigns and ad sets (+2.9% more sales). Best Marketing Mix Models today provide optimal spend allocation for each campaign and ad set. Being able to do this requires extreme sophistication from MMM: It needs to be able to estimate response curves and miROAS for each campaign and ad set. The most advanced MMMs, like Sellforte, go even beyond this: they translate recommended spend changes to optimal bid values.

Below is an example of a dashboard providing optimal bid values for each campaign and ad set, based on the public Sellforte demo.

2. Agentic MMM: Choose an MMM tool with AI Agents that Automate Your Workflows

Once you've checked that the MMM tool you're evaluating has the optimization capabilities you need, it's time to look at its AI capabilities.

Why is this relevant? We are entering the era of Agentic MMM, where MMM tools have AI Agents that help you find optimal media spend allocations and even execute media buying. They can save your marketing team and agency countless of hours that are typically spent in budget planning or tinkering with bid levels in ad platform tools. Agentic Marketing Mix Modeling (MMM) combines two technologies:

1. MMM's superpower to understand how media affects revenue
2. AI Agents' superpower to help marketers in specific tasks, such as media planning, revenue forecasting or media buying.

For clarity, Agentic MMM is not about changing how the MMM analysis itself is done: we don't have AIs yet that can do the modeling part with the same rigor and quality than we can. Instead, AI Agents are using the MMM results and tools connected to them to help marketers get answers to their questions faster. This ensures that the agentic optimization workflow has the same rigor and quality that a non-agentic workflow would have.

Agents are accessed from a ChatGPT-like conversional interface. Below is an example from the public Sellforte demo.

Marketers can ask the AI any questions related to marketing performance or optimization, such as 

• How should I allocate my media budget for the next 4 weeks to maximize sales?
• How should I allocate my current Google Ads budget to maximize sales?
• What were my top performing channels in the most recent weeks?

Underneath the conversational interface, AI agents are doing their work. As an example, Sellforte AI has three agents:

👩‍💻 Media Planner Agent answers all the optimization, planning and forecasting-related questions. Under the hood, it is using Sellforte's optimization tools to answers marketers' questions. 

👨‍💼 Media Buyer Agent execute the plans you are building with Media Planner Agent, byl pushing bid value changes directly Google, Meta etc. You can define how the Media Buyer Agent operates. It can operate in full self-drive mode, automating your media buying against certain objectives, or in an assisted-drive mode, where you are approving the actions it wants to take

🧑‍🔬 Experiments Agent recommends, designs and analyses incrementality tests for you. It also tries to uncover the hidden experiments you have accidentally done in the history, trying to minimize the need for new tests. 

3 Trust: Choose an MMM Tool That Produces High Quality Results

Once you've checked that the MMM tool has the required optimization capabilities that help you drive more sales, and AI Agents that help you use those optimization capabilities, you need to determine whether you can trust the MMM results sufficiently to act on the 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 statistical model validation
• Bayesian approach

• MMM is calibrated with informative priors, based on a robust calibration framework covering geo lift experiments, conversion lift studies, and attribution data
• In-built tools for analyzing Experiments, including Geo Lift tests and Conversion Lift Tests
• Promotions are included in measurement in Retail and eCom/DTC
• Advanced model validation based on model outputs

4. Bonus: Additional features for evaluation

Here's a list of additional features you can evaluate, depending on your needs for the MMM solution.

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 25 Marketing Mix Modeling tools:

1) Sellforte (MMM SaaS)

Sellforte is a Next Gen Marketing Mix Modeling platform for Retailers, eCommerce businesses and Direct-to-Consumer (DTC) brands. Key features:

• Sellforte AI: World's only Agentic MMM solution, including Media Planner Agent, Media Buyer Agent, and Experiments Agent.
• Sellforte Performance: Get optimal spend and optimal bid value for each campaign and ad set.
• Sellforte Optimizer: Plan optimal spend allocation across channels.
• Sellforte Home: One-click view to next month's revenue forecast and key spend change recommendations.
• Sellforte Experiments: Analyze Geo lift studies and Conversion Lift studies, and use the results to calibrate your MMM.
• High Quality MMM results: Sellforte uses Bayesian Marketing Mix Modeling combined with model calibration with incrementality experiments and attribution data.

Sellforte is best for eCommerce businesses, DTC brands and Retailers who want to unlock their growth by optimizing budget allocation across channels, campaigns and ad sets based on true incremental revenue impact of media.

Sellforte offers a free Marketing X-Ray for eCommerce / DTC brands and Retailers to test the platform, and has a no-signup demo that everyone can try. Additionally, Sellforte has transparent pricing available on its website.

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. The image below summarizes the scope difference between Meridian and Sellforte.

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.

Compare Robyn and Sellforte: Comparison of Robyn vs Lightweight vs Marketing Mix Modeling SaaS.

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) Lifesight (MMM SaaS)

Lifesight website

Lifesight is an MMM SaaS alternative, with majority of employees in India (source: LinkedIn).

Lifesight healine: "Turn wasted ad dollars into predictable growth & profit".

7) Incrmntal (MMM SaaS)

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

Incrmntal headline: "Welcome to the Future of Measurement"

8) Recast (MMM SaaS)

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

Recast headline: "Recast is the world’s most rigorous incrementality platform"

9) Cassandra (MMM SaaS)

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

Cassandra headline: "Measure and Optimize your Media Mix with AI"

10) Keen Decision Systems (MMM SaaS)

Keen Decision Systems is an MMM SaaS alternative.

Keen Decision Systems headline: "Forecast, optimize, and analyze your marketing investment"

11) Prescient AI (MMM SaaS)

Prescient AI is an MMM SaaS alternative.

Prescient AI headline: "The world’s smartest marketing decision engine, powered by the most dynamic MMM."

12) Measured (MMM SaaS)

Measured is an MMM SaaS company.

Measured headline: "If it's not growing your business, it's waste"

13) Liftlab (MMM SaaS)

LiftLab is an MMM SaaS alternative.

LiftLab headline: "Maximize Your Marketing P&L for Growth and Profitability."

14) Paramark (MMM SaaS)

Paramark is 2023-founded MMM SaaS alternative.

Paramark headline: "Invest in marketing with confidence & predictability"

15) Mutinex (MMM SaaS)

Mutinex is an Australian MMM SaaS company.

Mutinex headline: "Mutinex is your business growth co-pilot. Your best decisions start here."

16) DoubleVerify / Rockerbox (MMM SaaS)

Rockerbox is originally a multi-touch-attribution company, who has later expanded to providing MMM SaaS. Rockerbox was originally an independent MMM vendor, but was acquired by DoubleVerify in 2025.

Rockerbox headline: "Rockerbox unifies your marketing measurement, marketing data, marketing decisions making, entire business"

17) Objective Platform (MMM Consultancy services / SaaS)

Objective Platform is a Neatherlands-based company offering MMM consultancy services and an MMM platform.

Objective Platform headline: "Elevate your marketing with data-driven insights, cross-channel optimization, holistic data overview, next gen attribution modeling, advanced budget forecasting, customizable reporting"

18) Seeda (MMM SaaS)

Seeda is a 10-person (Linkedin 2025 July) Australian startup with a headline "Predict Your Perfect Mix".

19) WorkMagic (MMM SaaS)

WorkMagic is an MMM startup with 25 employees (LinkedIn 2025 July) with a headline "Growth by science".

20) Circana (MMM consulting)

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

21) Analytic Partners (MMM Consulting)

Analytic Partners is a marketing analytics consulting company with a broad offering, which also includes Marketing Mix Modeling consulting services.

Analytics Partners headline: "We help global brands maximize success with impactful marketing decisions"

22) Kantar / Blackwood Seven (MMM Consulting)

Kantar provides Marketing Mix Modeling consulting services through a sub-brand called Blackwood Seven, which it aquired in 2022.

Blackwood Seven headline: "LIFTROI x HamiltonAI. Unified Marketing Measurement & Optimization."

23) Aryma Labs (MMM Consulting)

Aryma Labs is an India-based company offering Marketing Mix Modeling projects. They are active in MMM-related publications, emphasize the frequentist MMM approach (instead of Bayesian).

Aryma Labs headline: "Marketing Measurement For The Privacy First Era."

24) Proof Analytics (MMM Consulting)

Proof Analytics headline: "Proof-as-a-Service. Make smarter decisions in a volatile marketplace with Proof CausalAI™"

25) Nepa (MMM Consulting)

Nepa is a Sweden-based marketing analytics consultancy, also offering Marketing Mix Modeling.

Nepa headline: "Big data, human touch. At the nexus of data analysis and marketing mastery, Nepa translates complex information into clear direction."

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.

Further Reading

• What is Marketing Mix Modeling? A Complete Guide. Link

• Ultimate Buyer's Guide to MMM: How to Choose the Best Platform in 2025. Link

• From Last-click to Marketing Mix Modeling (MMM): Unlock +6.5% more sales. Link

Authors

Lauri Potka is the Chief Operating Officer at Sellforte, with over 15 years of experience in Marketing Mix Modeling, marketing measurement, and media spend optimization. Before joining Sellforte, he worked as a management consultant at the Boston Consulting Group, advising some of the world’s largest advertisers on data-driven marketing optimization. Follow Lauri in LinkedIn, where he is one of the leading voices in MMM and marketing measurement.