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.

This was originally featured in the EMARKETER Daily newsletter. For more marketing insights, statistics, and trends, subscribe here.