New paper identifies six practical steps to improve transparency, validation and trust in MMM as it becomes central to media planning and investment
New York, NY July 30, 2026 – The Coalition for Innovative Media Measurement (CIMM) today released a new paper outlining six recommendations to strengthen the foundations of Marketing Mix Modeling (MMM), arguing that as the methodology becomes increasingly embedded in media planning, financial decision-making and AI-powered optimization, improving the quality and transparency of its underlying inputs has become an industry priority.
Once primarily used to analyze historical marketing performance, MMM now plays a growing role in determining future media investment decisions through budget allocation, forecasting and optimization. CIMM’s paper argues that while MMM–particularly through the lens of the TV and video marketplace–has become essential to modern marketing, inconsistent data, opaque modeling assumptions and uneven validation practices create risks that will only become more significant as AI-driven planning and buying become more widespread.
“The industry increasingly relies on Marketing Mix Modeling to guide critical investment decisions, but greater reliance requires greater confidence in the underlying data and evidence,” said Jon Watts, Managing Director, CIMM. “This paper focuses on practical ways to improve the shared inputs and documentation that help advertisers build more transparent, credible and decision-ready MMM systems.”
The paper recommends six practical actions for the industry:
- Develop a MMM-ready media data specification for TV, streaming, CTV, digital video and related media inputs.
- Create a standard MMM model factsheet documenting data sources, transformations, priors, validation methods, uncertainty ranges, refresh cadence and limitations.
- Establish an experimentation and validation playbook for geo tests, lift tests, calibration studies, sensitivity analysis and out-of-sample validation.
- Build an independently overseen premium video evidence library that provides evidence ranges, confidence levels and applicability notes rather than fixed priors.
- Translate these materials into RFP language, contractual clauses, audit rights, data-sharing requirements, change-control expectations and AI safeguards.
- Engage with agentic advertising protocol developers so MMM-informed evidence can be represented in machine-readable form without being treated as deterministic truth.
Rather than advocating for standardized advertiser models or the disclosure of proprietary business data, the paper emphasizes improving the shared evidence, documentation and transparency that private MMM systems depend upon. These improvements can help reduce avoidable distortions, strengthen confidence in model outputs and better prepare the marketplace as automated decision-making becomes more prevalent.
The paper, Models as Masters? Marketing Mix Modeling and AI-Driven Media Decision Making, examines the evolution of MMM from an analytical tool into foundational market infrastructure and explores how incomplete data, inconsistent taxonomies and limited validation can influence media valuation and investment decisions. Co-authored by independent consultant Chris Williams and CIMM’s Watts, it also examines how these challenges may become amplified as MMM outputs increasingly feed AI-enabled planning and optimization systems.
To download the full paper, visit https://cimm-us.org/models-as-masters-marketing-mix-modeling-and-ai-driven-media-decision-making/
About CIMM
The Coalition for Innovative Media Measurement (CIMM) is a non-partisan, pan-industry association of companies from across the media and advertising ecosystem, focused on cultivating and supporting improvements, innovations and best practices in measurement and currency development, the use and application of new metrics, and data collaboration. CIMM’s role is to convene stakeholders, illuminate emerging issues, and help the marketplace make informed decisions. CIMM embraces the entire media and advertising ecosystem and prioritizes effective collaboration to deliver meaningful change.
