Marketing Mix Modeling (MMM) is a statistical analysis technique that quantifies how your marketing efforts drive business results. It uses historical data to measure the impact of advertising, promotions, pricing, and external factors on sales and other performance outcomes. Unlike user-level tracking tools, MMM works with aggregate data, making it a powerful, privacy-compliant solution in today's evolving digital environment.

Why is MMM making a comeback?

Privacy regulations are mounting. Signal loss is accelerating. Omnichannel advertising has become impossibly complex. These forces are driving marketers back to MMM for holistic, privacy-safe measurement.

• 49% of marketers worldwide currently use MMM, according to September 2024 data from Supermetrics.
• 56% of US ad buyers will focus at least somewhat more on MMM in 2025, according to December 2024 data from the Interactive Advertising Bureau (IAB).
• 61.4% of US marketers want better/faster MMM and 30.1% believe it's the type of measurement best at identifying drivers of business value or outcomes, according to July 2024 data from EMARKETER and Snap Inc.

Platforms like Meta and Google have embraced this resurgence by launching open-source MMM tools (Robyn and Meridian, respectively) to democratize access to advanced measurement techniques​.

How does MMM differ from other measurement solutions?

MMM is macro-level and privacy-friendly, analyzing aggregate data over time to uncover correlations between marketing activities and outcomes. It differs from:

• Multi-touch attribution (MTA), which tracks individual-level behaviors across touchpoints to assign fractional credit to each interaction.
• Incrementality testing, which uses controlled experiments (e.g., geo testing) to isolate the effect of a specific tactic.
• Platform reporting, which provides performance insights within individual platforms but often lacks cross-channel comparability.

Marketers increasingly combine MMM with these other solutions to balance accuracy, granularity, and scalability​.

What are the current trends in MMM?

1. Open-source tools empower customization. Open-source platforms like Google’s Meridian and Meta’s Robyn are making MMM more transparent and accessible. These tools let marketers customize models to fit their business needs, moving away from black-box vendor solutions. They also promote collaboration and allow for continuous model improvement over time.

2. AI and automation drive agile MMM. Advances in AI are speeding up MMM by automating data ingestion, feature engineering, and modeling. This enables agile MMMs that deliver weekly or bi-weekly insights, helping marketers make faster, more informed decisions. Always-on modeling is becoming standard for brands that need real-time adaptability.

3. Unified measurement stacks provide complete visibility. MMM is now part of a three-tiered measurement strategy, alongside incrementality testing and platform reporting. This layered approach combines long-term modeling, causal validation, and real-time analytics, giving marketers a fuller, more reliable picture of performance across channels.

4. Retail media integration expands MMM’s reach. Retail media networks are embedding MMM capabilities to help brands measure performance across digital and in-store retail environments. With more media spend flowing to RMNs, brands need MMM to account for digital shelf dynamics, promotions, and full-funnel retail impact using retailer-supplied data.

What are the key benefits of MMM?

• A holistic view of marketing impact. MMM considers both media and non-media factors, giving a full picture of what drives business outcomes. This broader perspective helps marketers understand true channel performance beyond just digital touchpoints.
• Budget optimization. By quantifying the ROI of each channel, MMM helps marketers allocate budgets more efficiently. It guides spend decisions toward the most impactful tactics and channels, maximizing return on investment.
• Privacy compliance. MMM relies on aggregated data, not user-level tracking, making it inherently privacy-friendly. It’s well-suited for a post-cookie world where personal data is increasingly restricted.
• Strategic planning. MMM supports big-picture planning by modeling scenarios and forecasting outcomes. It informs annual budgeting and cross-channel strategies with data-backed insights.

Marketers see MMM as the most effective tool for identifying business drivers compared to attribution or web analytics​.

What are the limitations of MMM?

• Lack of granularity. MMM aggregates data over time and channels, making it difficult to assess the impact of individual tactics like specific creatives or audience segments. It's less useful for day-to-day optimization or A/B testing.
• Time- and resource-intensive. Building an MMM requires significant resources. You need extensive historical data and specialized statistical expertise. The process can take weeks or months to complete, requiring patience and commitment from stakeholders.
• Delayed results. MMM doesn’t deliver real-time insights. Results often lag behind campaign changes, making it harder to adapt quickly in fast-moving environments.

While it excels in strategic insight, MMM works best when calibrated with faster, more granular tools like MTA or platform data​.

How Are Marketers Using MMM Today?

• Media planning. Marketers rely on MMM to guide strategic decisions around channel mix and investment levels. It helps determine how to allocate budgets across TV, digital, print, and other media to maximize overall impact.
• ROI analysis. MMM quantifies the return on investment for each marketing channel or tactic. This allows marketers to identify which efforts are driving the most business value and justify spending decisions.
• Scenario planning. Brands use MMM to simulate the outcomes of different budget scenarios. This helps anticipate the effects of shifting spend across channels before making real-world changes.
• Validating platform claims. MMM provides an independent view of performance, enabling marketers to verify or challenge results from platforms like Meta and Google. It helps cut through the noise of platform-driven self-attribution.
• Complementing other measurement tools. Brands like CP Skin Health use MMM to guide high-level budget allocation, then layer on tools like Multi-Touch Attribution (MTA) for more granular, in-flight optimizations. This combined approach offers both strategic direction and tactical agility.

Should I choose an open-source or proprietary MMM tool?

The choice between open-source and proprietary MMM tools depends on your resources and objectives. Each path offers distinct advantages and challenges.

Proprietary MMM: Easy-to-use, scalable, and vendor-dependent

Proprietary MMM solutions are those that are offered and controlled by a specific vendor, like an analytics firm, measurement provider, or adtech company.

• For example, Nielsen, Ipsos, Analytic Partners and Adobe all offer MMM tools for their clients.
• These platforms tend to be out-of-the-box, featuring automation, intuitive user interfaces, and professional support—making them accessible even for teams without deep technical expertise.

Key benefits include:

• Ease of implementation. Vendors handle the heavy lifting of data integration, model building, and validation.
• Scalability. Enterprise solutions are optimized to handle large datasets across multiple channels and markets.
• Ongoing support. Dedicated support teams ensure that models remain up-to-date and relevant as business conditions change.

However, these advantages come at a cost—literally. Many paid MMM platforms require a significant financial investment, which can be a barrier for small or mid-sized businesses.

Proprietary MMM solutions also come with:

• Limited flexibility. Customizing models to fit unique business needs may be difficult or require additional fees.
• Potential lack of transparency. Some vendors operate as "black boxes," making it hard to understand how results are derived.
• Vendor dependency. Brands must rely on external providers for updates and modifications, potentially limiting agility.

Open-source MMM: Cost-effective, transparent, difficult to implement

For brands with the right resources and technology, open-source tools can be an effective way to experiment with MMM without having to commit to a vendor or fee structure.

• Open-source platforms include Meta’s Robyn or Google’s Meridian.
• These tools offer brands more flexibility, transparency, and independence.

Here are some of the key benefits of open-source MMM tools:

• Free to use. No licensing fees make open-source solutions cost-effective.
• More transparency. Users can see and modify the code, ensuring complete visibility into the model’s mechanics.
• Customizable. Businesses can tweak algorithms, include unique variables, and tailor insights to their exact needs.

However, the DIY approach comes with some hurdles. Open-source tools require dedicated technical resources, and implementation can be complex and time-consuming.

Key drawbacks include:

• A learning curve. These models may require expertise in data science, statistics, and coding.
• Time-intensive implementation. Data must be manually collected, cleaned, and formatted before modeling can begin.
• Ongoing maintenance burden. Without vendor support, brands must allocate internal resources for troubleshooting, updates, and optimization.
• It’s also more difficult to scale open-source models.

How does MMM support digital ad measurement?

MMM plays a vital role in building a privacy-forward measurement stack, providing advertisers with:

• Data resilience. Unlike many digital measurement tools, MMM doesn’t depend on individual-level identifiers. This makes it inherently resilient to evolving browser restrictions and stricter privacy regulations, ensuring consistent insights as data availability shifts.
• Cross-channel integration. MMM excels at capturing the impact of media across both online and offline channels—from paid social and CTV to in-store promotions and linear TV. This holistic perspective goes far beyond the limitations of siloed, platform-specific reporting.
• CTV and upper-funnel value. With the rise of shoppable ads and immersive upper-funnel formats like CTV, traditional last-click attribution often underrepresents their value. MMM helps quantify their contribution to long-term business outcomes, enabling more balanced and strategic investment.

How should marketers get started with MMM?

Building an effective measurement strategy with MMM requires careful planning and a layered approach. Here are key steps to guide the process:

1. Align on objectives. Start by clearly defining what success looks like. Are you trying to understand brand lift, optimize ROI, measure incremental reach, or assess media mix effectiveness? Establishing focused objectives will shape the data inputs, model design, and how you interpret the results.

2. Assess resources. Consider the capabilities of your internal team. Do you have the analytical expertise, clean data, and time to take on a DIY MMM approach? Or would partnering with a vendor provide more scalability, speed, or sophistication? Honest evaluation here can set you up for success.

3. Start small. Before rolling out a full-scale model, run a pilot. Focus on a single product line, region, or campaign to validate your data sources, understand model outputs, and build stakeholder confidence. This phased approach helps manage risk and sets the foundation for broader adoption.

4. Integrate other tools. MMM is most powerful when paired with complementary methodologies. Use it alongside Multi-Touch Attribution (MTA) for digital granularity, incrementality testing for causal insights, and platform reporting for real-time metrics. Each method offers a piece of the puzzle—together, they provide a fuller picture.

Success requires accepting that no single measurement tool provides all the answers. Layer methodologies to triangulate insights. Start small to build confidence. Invest in the capabilities you need for long-term success. Most importantly, remember that perfect measurement doesn't exist, but better measurement is always possible.