When MMM Moves at the Pace of AI: Speed is Easy. Growth is Everything.
Marketing mix modeling (MMM) is now faster, more cost-efficient, and more accessible than ever before. But generating models at unprecedented speed is meaningless if the models fail to drive business outcomes.
To deliver commercial impact, brands must align the insights the models produce with how their business actually makes decisions.
This report, which features an independent survey of over 100 global senior marketers, outlines how to marry advances in technology with the human judgment required to make data-informed decisions that drive profitable growth.
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What This Report Covers
By downloading When MMM Moves at the Pace of AI: Speed is Easy. Growth is Everything., you will learn:
How to choose the right MMM setup for your brand: Navigate the trade-offs between the three different ways to set up an MMM.
How to harness AI to improve measurement: Discover how to pragmatically deploy AI to eliminate digital bias and replace time-consuming manual tasks.
The four pillars of CFO-Grade credibility: Discover how to build boardroom trust by improving the quality of the insights your measurement generates.
How to close the insight-to-action gap: Learn the three organizational levers you need to deploy to ensure insights are acted upon.
The rise of the “Multidisciplinary Orchestrator”: The skills gaps you need to address when the premium on human “thinking” has never been higher.
The AI Readiness Gap and Boardroom Trust Deficit
The supply side of marketing measurement has been transformed over the past few years. Models that once took 12 weeks to deliver can now be refreshed continuously, and AI agents are rapidly absorbing manual execution. Yet, important gaps remain:
97% say their organization’s infrastructure, compliance, and team skills are not fully optimized today to deploy AI in marketing.
73% reveal their CFO doesn’t fully trust and use marketing measurement outputs.
58% admit to a gap between measurement insights and decisions that are actioned.
55% say analysts spend at least 30% of their time reworking incomplete or incorrect data.
Marketing influence and budget security do not come from developing the fastest model, but from building the most credible and relevant one. If your measurement exists in a methodological vacuum disconnected from the P&L, it will be dismissed under CFO scrutiny.
Listen to Key Takeaways
Our AI-powered podcast, Marketing Effectiveness Unlocked, provides a concise audio overview of the report. Listen below for a snapshot of the core findings.
Who Should Read This Report?
CMOs who need to defend brand equity, secure budgets, and position marketing as a business growth engine in a volatile macro-environment.
Marketing Effectiveness, Measurement, Analytics & Insights Leaders who want to deploy AI strategically, streamline data pipelines, and transition their teams from manual execution to high-value orchestration.
CFOs & Finance Partners who require rigorous, P&L-connected measurement that goes beyond paid media to account for full business context and independent validation.
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"AI is making marketing measurement faster, cheaper, and more accessible, but better models don't automatically lead to better decisions. To turn insights into commercial outcomes, you need to focus on human dynamics."
Thomas Barta
Founder, Marketing Leadership Masterclass
What’s Inside the Report
Chapter 1: How Tech Unlocked MMM for Everyone
How open-source code, cloud-based SaaS, and Agentic AI have democratized modeling, lowering barriers to entry for both mid-market brands and B2B enterprises.
Chapter 2: Selecting the Right MMM Setup for Your Brand
Analysis of the three primary setup options, SaaS/In-house, Consultancy-led, and Hybrid, mapped across seven key dimensions.
Chapter 3: Why “Perfect” Data is a Mirage
Why automated models process whatever data is immediately present, how to address the fact that 55% of analysts waste 30% of their time reworking incorrect data, how data asymmetry structurally under-credits offline media, and how Agentic AI can help.
Chapter 4: How and Where to Deploy AI Effectively
A guide to identifying measurement pain points and targeting high-value, low-effort opportunities that AI can exploit.
Chapter 5: How to Ensure Insights are Trusted and Acted On
The four pillars of trust and the three organizational levers required to ensure your measurement program drives actual commercial decisions, alongside an introduction to the role of the Multidisciplinary Orchestrator.
Turn Faster Insights into Better Decisions
Fill out the form to download your copy of When MMM Moves at the Pace of AI: Speed is Easy. Growth is Everything. The report contains a host of frameworks, action points, and a measurement diagnostic to use in your next stakeholder working session.
How does AI improve marketing mix modeling (MMM)?
AI transforms MMM from a slow, retrospective reporting function into an always-on “decisioning system.” It automates the heavy lifting of data ingestion, cleansing, and normalization, compressing modeling cycles from months to weeks or even days. AI also acts as a “smart buffer” to harmonize unstructured offline data (like TV and OOH) with real-time digital APIs, neutralizing digital bias and enabling marketers to run interactive, real-time scenario simulations to optimize future budgets.
Should my brand build MMM in-house, use a SaaS platform, or work with a consultancy?
The optimal setup depends on your business complexity and strategic ambition:
SaaS/In-house: Best for digital-native brands with standardized data and mature internal analytics teams focused on rapid, tactical digital optimization.
Consultancy-led: Best for brands operating in complex sectors with offline and online drivers, requiring bespoke modeling, high-touch C-suite translation, and organizational change management.
Hybrid Approach: The most widely adopted setup where internal teams operate SaaS platforms for tactical speed, while an external consultancy co-builds, validates, and provides advanced modeling capabilities (e.g. measuring brand equity and pricing).
Why do CFOs discount MMM outputs, and how do we build trust?
CFOs discount measurement when it exists in a methodological vacuum disconnected from the P&L, or when it ignores critical business drivers like pricing, distribution, and macroeconomic conditions. To secure boardroom trust, your insights must be built on four pillars:
Financial Rigor: Using a hierarchy of metrics that ladders directly to business KPIs (sales, profit, market share).
Business Context: Factoring in non-media drivers (pricing, promotions, competitor stockouts, macroeconomics).
Independent Validation: Calibrating the model with an always-on experimentation calendar (e.g. geo-lift tests).
Clear Actionability: Deploying interactive scenario planning and simulation tools to forecast the commercial impact of future decisions.
Does MMM work for B2B brands?
Yes. Traditional MMM was built for B2C brands with high transaction volumes and short purchase cycles. B2B brands, which typically have low transaction volumes, sales cycles lasting up to two years, and complex customer journeys, historically broke these modeling assumptions. By tracking and structuring complex B2B touchpoints and sales conversations, AI can convert previously unusable, unstructured interactions into model-ready data. This allows B2B brands to accurately map and model the entire buying journey and measure the true impact of their marketing investments.
What skills does a marketing team need to successfully manage AI-driven measurement?
As AI absorbs the manual execution layer of data preparation and report formatting, the premium shifts from “doing” to “thinking.” Our survey shows that 54% of marketers identify “algorithmic oversight” as their team’s most critical skills gap. To succeed, brands must cultivate leaders who can govern AI outputs, translate complex statistical insights into boardroom-ready commercial stories, and navigate organizational politics to ensure insights are acted upon.
Why do automated models struggle with offline data, and how does Agentic AI help?
Automated SaaS platforms operate on the assumption that all input data is clean and structured. While digital data meets this bar, offline data (TV, OOH, sponsorships) is highly unstructured and slow to access. This data asymmetry causes automated models to process only what is immediately available, resulting in a structural digital bias that under-credits offline brand investments.
To resolve this, brands can deploy targeted AI agents to act as a “smart buffer.” These agents automatically monitor shared folders, ingest unstructured offline PDFs, extract spend and flighting data, map it to a unified taxonomy, and run QA checks in minutes—ensuring offline media is modeled on an equal footing with real-time digital APIs.
Thomas BartaFounder, Marketing Leadership Masterclass