The 7 Best Marketing Mix Modeling Software Tools in 2026

Choosing MMM Software Without the Marketing Spin

Every vendor on this list will tell you they have the best Marketing Mix Modeling platform. Your job is to cut through it and find the tool that actually fits your team, your budget, and your data maturity.

We have evaluated seven MMM tools based on real-world use, public documentation, and conversations with marketing teams who use them. We will be honest about strengths and limitations for every tool on this list, including our own.

Quick Comparison Table

The 7 Best MMM Tools in 2026

1. Cassandra

Cassandra is an AI-native MMM platform built for marketing teams that want strategic measurement without a data science department. It connects to your ad platforms and revenue sources, builds the model automatically, and delivers weekly budget optimization recommendations in a marketer-friendly dashboard.

Best for: Growth-stage and mid-market companies ($50K-$500K monthly ad spend) without dedicated data scientists.

Key strengths:

• Speed to value. Most teams go from data connection to first insights in under two weeks. The platform handles data ingestion, cleaning, and model configuration automatically.

• Budget optimizer. Goes beyond reporting to recommend specific dollar amounts to shift between channels, with projected revenue impact for each scenario.

• Designed for marketers. The interface presents results in business language -- ROI curves, channel rankings, and what-if scenarios -- rather than statistical output that requires interpretation.

Limitations:

• Newer brand. Cassandra does not have the multi-year track record of established players like Measured or legacy consulting firms. If your procurement team requires five years of case studies, that may be a sticking point.

• Smaller community. Unlike open-source tools with large developer communities, Cassandra's user community is still growing. You are relying on their support team rather than a broad ecosystem.

• Advanced customization. Power users who want to hand-tune priors or write custom model specifications may find the automated approach limiting compared to open-source frameworks.

Pricing: Starts from approximately $2,000 per month. Scales based on number of channels and data volume.

2. Lifesight

Lifesight is a unified marketing measurement platform combining MMM, attribution, and causal inference. It has built a strong presence in mid-market e-commerce, particularly with brands running significant spend across Meta, Google, and TikTok.

Best for: Mid-market e-commerce brands that want MMM alongside attribution in a single platform.

Key strengths:

• Unified measurement. Having MMM, MTA, and incrementality in one platform means you can cross-reference insights without switching between tools. This triangulation improves confidence in results.

• Strong e-commerce integrations. Native connectors for Shopify, WooCommerce, and major ad platforms make data onboarding straightforward for DTC and e-commerce brands.

• Causal AI approach. Lifesight emphasizes causal inference in its methodology, which can provide more defensible results than pure correlation-based modeling.

Limitations:

• Complexity. Having multiple measurement methodologies in one platform can be overwhelming for teams that just want clean MMM insights. There is a learning curve to understanding when to trust which view.

• B2B fit. The platform is primarily optimized for e-commerce and DTC use cases. B2B companies with longer sales cycles and offline conversions may find less tailored support.

• Pricing transparency. Pricing is not published and requires a sales conversation, making it harder to budget in advance.

Pricing: Custom pricing based on needs. Contact their sales team for a quote.

3. Measured

Measured has been in the measurement space longer than most on this list, building its reputation on incrementality testing. Their MMM offering is part of a broader suite that emphasizes experimental calibration -- making it a strong choice for teams that prioritize causal rigor.

Best for: Enterprise teams with larger budgets that want MMM grounded in incrementality experiments.

Key strengths:

• Incrementality-first philosophy. Measured uses controlled experiments (geo-lift tests, PSA tests) to calibrate their MMM, producing more trustworthy ROI estimates than modeling alone.

• Enterprise credibility. Years in market with major brand clients gives Measured the track record procurement teams want to see.

• Cross-channel coverage. Strong support for harder channels like linear TV, CTV, direct mail, and OOH alongside digital.

Limitations:

• Cost and complexity. The combination of incrementality testing and MMM means higher costs and longer timelines. Not a tool you set up in a week.

• Requires significant spend. Incrementality tests need enough budget for statistical significance. Teams under $100K per month may struggle to run meaningful experiments.

• Not self-serve. Measured is more managed service than pure SaaS. Expect to work closely with their team.

Pricing: Typically $5,000-$10,000+ per month depending on scope. Enterprise contracts with annual commitments.

4. Rockerbox

Rockerbox started as a multi-touch attribution platform and expanded into MMM as the market shifted. Its strength is bridging granular attribution data with top-down modeling. For teams already using Rockerbox for attribution, adding their MMM layer is natural.

Best for: DTC brands that want attribution and MMM in one platform, especially those with complex online-to-offline journeys.

Key strengths:

• Attribution + MMM integration. Rockerbox feeds first-party attribution data into the MMM as a calibration signal, improving model accuracy for digital channels.

• Journey-level data. Touchpoint-level collection (via pixel and server-side tracking) provides richer input data than platforms relying solely on aggregate spend.

• Strong DTC focus. Deep integrations with Shopify, ad platforms, and affiliate networks make it a natural fit for DTC brands.

Limitations:

• MMM is not the core product. Rockerbox's MMM is an extension of their attribution platform. Teams that only want MMM may find it less focused than purpose-built tools.

• Data dependency. The platform works best when you also use their attribution tracking. Without the attribution layer, you lose differentiated value.

• Scaling challenges. Some users report dashboard performance issues as the number of channels and campaigns grows.

Pricing: Custom pricing. Typically sold as part of a broader measurement package including attribution.

5. Prescient AI

Prescient AI has carved out a niche in e-commerce with a fast, AI-driven approach to media mix modeling. The headline feature is daily model refreshes -- significantly faster than most competitors -- making it well-suited for fast-moving brands optimizing paid digital channels.

Best for: E-commerce brands spending $50K-$300K per month on paid digital who want fast, frequent model updates.

Key strengths:

• Daily updates. Prescient refreshes models daily, which gives brands a much more current view of channel performance than weekly or bi-weekly alternatives. For fast-moving e-commerce, this matters.

• Quick setup. The platform emphasizes rapid onboarding -- typically under two weeks -- with automated data connectors and minimal configuration required.

• Accessible pricing. Starting around $1,500 per month, Prescient is one of the more affordable dedicated MMM tools on the market, making it accessible to earlier-stage brands.

Limitations:

• Digital-heavy focus. Prescient is optimized for paid digital channels (Meta, Google, TikTok, etc.). If you have significant spend in offline channels like TV, radio, or direct mail, the coverage may be insufficient.

• Less methodological depth. The emphasis on speed and simplicity means fewer options for model calibration, custom priors, or incrementality integration compared to more rigorous platforms.

• Newer methodology validation. Daily model refreshes are appealing, but some practitioners question whether daily data provides enough signal for stable MMM results, especially for channels with longer lag effects.

Pricing: From approximately $1,500 per month. Scales with spend volume and number of channels.

6. Recast

Recast takes a Bayesian approach to MMM and has built a reputation for methodological transparency. The team publishes detailed explanations of their modeling choices and is active in the measurement community -- a solid choice for teams that want rigorous modeling without hiring a full data science team.

Best for: Mid-market brands that value methodological rigor, particularly those with technically curious marketing leaders.

Key strengths:

• Methodological transparency. Recast publishes detailed documentation about their Bayesian modeling approach. You can understand exactly how the model works, which builds trust in the outputs.

• Strong Bayesian framework. Their modeling approach handles uncertainty well, providing credible intervals rather than point estimates. This gives you a more honest view of what the model does and does not know.

• Calibration support. Recast supports calibrating models with incrementality test results, which grounds the statistical model in experimental evidence.

Limitations:

• Steeper learning curve. The emphasis on statistical transparency means the output is more detailed and nuanced than some competitors. Teams wanting simple "spend more here, less there" answers may need to invest time in understanding the model.

• Smaller scale. As a smaller company, Recast may have less bandwidth for dedicated customer support compared to larger competitors. Response times and account management may vary.

• Integration depth. While improving, the number of native data connectors is smaller than some competitors. You may need to export data from some platforms manually or through an intermediary.

Pricing: From approximately $3,000 per month. Custom quotes for larger implementations.

7. Google Meridian (Open Source)

Meridian is Google's open-source MMM framework, released in 2024 as a successor to LightweightMMM. Built on Bayesian principles using JAX, it is a serious statistical toolkit -- but a framework, not a product.

Best for: Data science teams that want full control over methodology and prefer not to depend on a vendor.

Key strengths:

• Complete control. You own the model, the code, and the output. No black boxes, no vendor lock-in.

• Free to use. The software costs nothing. For teams with existing data science resources, this means significant savings on licensing.

• Strong statistical foundation. Modern Bayesian methods with JAX-based computation. Supports geo-level modeling for brands with regional variation.

• Google's backing. Active development, good documentation, and a growing practitioner community.

Limitations:

• Requires data science expertise. You need someone fluent in Python, Bayesian statistics, and MMM methodology to implement, maintain, and interpret results.

• No UI or automation. No dashboard, no automated connectors, no budget optimizer. Everything beyond core modeling must be built in-house.

• Total cost of ownership. "Free" is misleading. Factor in 20-40 hours per month of data science time. At $80-$120 per hour loaded cost, that is $2K-$5K monthly in labor alone.

• Google's perspective. It is built by an advertising platform. Some practitioners question whether default priors may subtly favor Google's channels.

Pricing: Free (open source). Budget $100K-$200K+ annually for the data science headcount required to operate it effectively.

How to Choose: A Decision Framework

Choosing the right MMM tool is less about features and more about fit. Here is how to narrow it down.

Start with Your Team

Ask one question: who will own and use this tool day-to-day?

• If the answer is a data scientist who wants full control and enjoys modeling, evaluate Google Meridian first. The cost savings are real if you have the talent.

• If the answer is a marketing leader or growth marketer who needs answers but will not write code, focus on SaaS platforms: Cassandra, Prescient AI, Recast, or Lifesight.

• If the answer is "we need someone to do it for us," look at managed-service options like Measured.

Then Consider Your Channels

• Primarily digital (paid social, search, programmatic): Prescient AI and Cassandra both handle this well with fast setup.

• Digital + significant offline (TV, radio, OOH, direct mail): Measured, Recast, and Meridian have stronger support for offline channels and longer lag effects.

• Complex DTC with affiliate and influencer: Rockerbox's attribution layer adds unique value here.

Then Factor in Budget

• Under $2K per month for the tool: Consider Prescient AI or Google Meridian (if you have data science resources).

• $2K-$5K per month: Cassandra, Recast, and Lifesight are all in this range for most mid-market companies.

• $5K+ per month and want enterprise-grade measurement: Measured provides the deepest incrementality integration. Rockerbox if you also need attribution.

Finally, Test Before You Commit

Most MMM vendors offer a pilot or proof of concept. Take advantage of this. Run the tool against a period where you already know what happened (a big campaign launch, a seasonal spike, a channel pause) and check whether the output matches reality.

Final Thoughts

The MMM market in 2026 is more competitive and accessible than ever. Whether you are a $50K-per-month startup or a $5M-per-month enterprise, there is a tool that fits.

The worst decision is not picking the wrong tool -- it is continuing to fly blind with fragmented attribution data. Any credible MMM is better than no MMM. Start where you are and upgrade as your measurement maturity grows.