Marketers want AI to explain their data, not just crunch it
Marketers want clarity, fast. 60.9% of US marketers say generative insight summaries are the top AI enhancement for next-gen marketing mix modeling (MMM)-nearly double the share prioritizing dynamic learning features, per an October 2025 EMARKETER and Rakuten survey.
The signal is simple: insight translation beats black-box upgrades. If AI can't explain the "why" behind performance, it won't earn trust-or budget.
Why this matters right now
MMM is moving up the priority list. 46.9% of US marketers plan to invest in MMM in the next 12 months (July 2025 EMARKETER and TransUnion), putting it among the top measurement bets.
It's also winning on credibility: 27.6% call MMM the most reliable measurement approach, ahead of multitouch attribution (19.4%) and unified measurement (18.9%), per the same TransUnion survey. Translation: leaders want stable, explainable models-and AI that can narrate the story in plain language.
What to prioritize on your roadmap
- Ship the explanation layer first. Invest in AI-driven summaries, narrative dashboards, and natural-language Q&A on top of your MMM outputs before you chase exotic model features.
- Standardize inputs. Align taxonomy, channel naming, and experiment metadata so summaries are consistent and defensible.
- Create "why it moved" templates. Pre-format weekly and monthly readouts that decompose impact by channel, creative, promo, and macro context.
- Wire MMM into decision systems. Push AI summaries to executive briefs, BI, and planning docs so actions happen in the same week insights land.
- Set review guardrails. Require source links for every claim, confidence bands on effect sizes, and a human sign-off for high-stakes decisions.
How to use the chart in planning
Bring this chart to your next martech or budget review. It shows the market's tilt toward AI explanations over heavier modeling complexity-useful air cover for spending on reporting, narrative generation, and Q&A features.
- Budget talk track: "Our peers prioritize AI summaries (60.9%). We'll fund explainability to speed decisions and adoption, then phase in more advanced learning features."
- Benchmark: Compare your roadmap to where investment intent sits (nearly half investing in MMM) and adjust timing for pilots vs. enterprise rollouts.
Questions to ask MMM and analytics vendors
- Explainability: Can the system generate audit-ready summaries that cite model runs, data windows, and variables used?
- Scenario logic: How does it describe assumptions behind simulations (price, promo, distribution, seasonality) in plain language?
- Latency and refresh: What's the typical cadence for model updates, and how are updates highlighted in the summaries?
- Data provenance and privacy: How are sensitive fields masked, and can summaries exclude restricted data by role?
- Integrations: Do summaries post to Slack/Teams, BI, and planning tools with live links to evidence?
- Total cost and time to value: What's included (modeling, QA, enablement), and how fast to first executive-ready readout?
Trust signals you can bring to leadership
- Adoption: 46.9% plan to invest in MMM within a year-momentum is real.
- Reliability: MMM leads on perceived trust (27.6%) over MTA (19.4%) and unified solutions (18.9%).
- Clarity demand: Generative insight summaries are the top AI ask (60.9%).
Quick refresher on MMM
Marketing mix modeling uses historical data to estimate how channels, pricing, promotions, and external factors drive outcomes like sales or leads. It's channel-agnostic, privacy-resilient, and built for budget planning-especially useful as third-party signals fade.
Related research (subscription required)
- Ad Measurement Trends H1 2025
- Influencer Marketing Measurement 2025
Methodology
Findings on AI enhancement priorities come from the December 2025 EMARKETER and Rakuten report "MMM in Affiliate Marketing," based on a survey of 110 US marketers in October 2025. All respondents were manager level and above, across agencies and B2B/B2C brands.
Investment and reliability stats reference a July 2025 EMARKETER and TransUnion survey of US marketers.
Next steps for your team
- Pick one brand or region and pilot AI-generated MMM readouts for 6 weeks. Measure time-to-insight, adoption, and decisions made.
- Codify a summary style guide (metrics, time window, drivers, risks, actions) so outputs feel consistent across teams.
- Upskill your analysts and PMMs on prompt patterns for better summaries and follow-up questions.
If you want structured upskilling built for marketers, see this certification: AI Certification for Marketing Specialists.
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