The news: Google is simplifying one of marketing’s most complex measurement methods. 

A new no-code interface for its Meridian marketing mix model (MMM) lets non-technical teams explore performance data, ask questions in natural language, and simulate budget scenarios without relying on data scientists, per Adweek.

MMM analyzes how channels, timing, and external factors drive sales over time, offering a privacy-resilient alternative to user-level tracking. That matters as signal loss spreads beyond privacy regulations and cookie deprecation.

Brands including Asos, Urban Outfitters, and Shopify already use Meridian, indicating commercial traction. Adobe is moving in the same direction with Mix Modeler, combining MMM with scenario planning and optimization in marketer-facing workflows.

Why it’s worth watching: Many organizations still feed MMM insights into active campaigns on weekly or monthly schedules, which limits real-time decision value. That gap is what Google is trying to close with a no-code approach designed to make MMM more operational and timely.

• Only 11.8% of US marketers feed MMM insights into live campaigns weekly and 17.3% monthly, per EMARKETER/Rakuten.
• The largest share (26.4%) updates campaigns quarterly, while 20% do so ad-hoc, demonstrating that MMM often remains episodic rather than embedded in execution.
• 20.9% don’t use MMM at all, showing adoption barriers persist despite growing interest.

MMM is recognized but not yet embedded in daily planning. No-code tools aim to shift it from a periodic analytics exercise to a continuous decision engine.

Implications for marketers: Measurement tools once reserved for data analysts are fast becoming accessible operational tools for everyday marketing decisions. Tools like Meridian and Mix Modeler could keep channel analysis closer to marketers so they can optimize existing campaigns in real time.

• Teams should integrate MMM outputs into budgeting, forecasting, and media allocation cycles. 
• Train non-technical marketers to run scenario simulations, pressure-test channel mixes, and align spend with revenue targets before campaigns launch.

Speeding up predictive measurement through MMM helps counter signal loss and media fragmentation, giving brands a clearer picture of what’s working for their campaigns and what needs to change. 

Dive deeper: For more on what marketers need to know about MMM, read our FAQ.