MARKETING EFFECTIVENESS ACCELERATOR (MEA) : TOPICS 2026

the EARLY MOVER rate ends on Oct. 6

2026 Marketing Effectiveness Accelerator Topics

Enter your work under one of the following topics.

AI-Driven Modeling: Promise and Practicality

AI is influencing how models are developed, validated, and implemented across organizations. Progress is coming through steady refinement rather than sudden transformation.

How can AI be applied in MMM without introducing bias or false precision?
What makes an AI-driven marketing model truly decision-ready, and how should it be validated and benchmarked?
Does AI meaningfully improve core MMM challenges such as multicollinearity, lag effects, limited variation, and sparse geo data?
How can organizations demonstrate real business value from AI beyond faster modeling workflows and automated analysis?
How should experimentation be integrated into AI-driven optimization systems to ensure automated decisions remain grounded in empirical evidence?
How should teams manage reproducibility and consistency when models include stochastic or LLM components?
Where is AI adding value across the modeling workflow, from data preparation to analysis and insight delivery?
What governance and oversight are necessary to ensure transparency, accountability, and bias mitigation in AI-driven marketing decisions?
When is the use of synthetic data and scenario simulations appropriate for forecasting and budget planning?
How should synthetic data be defined, validated, and applied across measurement, forecasting, and content generation?
Can AI help marketers reduce the errors associated with last-click attribution?
How should AI-driven systems be permitted to make decisions, such as bids and budgets, versus simply recommending them?
What guardrails are needed when AI directly controls media spend?
How do we validate AI-driven optimization outcomes in live campaigns (not just model accuracy)?
Where does AI amplify platform bias vs true performance?
What are the initial reports on how transformer models contribute to solving long-standing modeling challenges and improving functionality and actionability?
Can AI-based sequential models deliver path to purchase and consumer journey insights in the face of sparse event-level data?

1A. Agentic AI

How far should agencies allow agentic AI to go: providing advisory support, implementing bounded automation, or executing actual tasks against budgets? Automation and scaling of creative pretesting through AI?
How have agentic AI models performed in the marketplace — programmatic buying, platform buying, media buying outside of the digital world? What are other uses?

1B. AI and Creative

How is AI accomplishing the following:

Transforming advertising
Deconstructing ads and other content to create analyzable data
Evaluating ad features’ impact on creative efficacy
Utilizing other analyses of ad effectiveness
Using agentic AI to create advertising

Data Complexity & Identity Resolution

Measurement challenges are increasingly linked to data integrity, not just the design of the models used to analyze it.

How can marketers reconcile fragmented identities, signal loss, delayed reporting, and missing data across platforms and devices?
Where does multi-touch attribution still remain effective today (e.g., retail media, authenticated environments), and how can potential blind spots caused by walled gardens and modeled conversions be addressed?
How should modeled or “advanced” audiences created from multiple data sources be validated before using them for targeting or measuring effectiveness?
How can organizations balance increased data granularity (such as geo, audience, retail partner, and time) with statistical stability while managing the risk of overfitting?
What transparency and audit standards are necessary for data clean rooms and cross-platform measurement reconciliation?
How are privacy regulations and platform policies transforming measurement methods and data access?
How can teams ensure KPI consistency and long-term comparability as platforms, identifiers, and measurement rules evolve?
What are “future proofing” measurement processes in a constantly evolving landscape of platform attribution methods: continuity plans, stakeholder communications, and benchmarking against independent measurement?
How are AI-powered search interfaces transforming observable intent signals and downstream attribution?
What is the most practical way to manage reach and frequency when cross-platform de-duplication remains so imperfect?
How should marketers respond when identity is incomplete or modeled (versus deterministic)?
How can you reconcile differences between platform and independent identity frameworks (e.g., clean rooms vs providers like LiveRamp/Crossix)?
What level of data fidelity qualifies as “decision-ready”?
How do identity gaps influence budget allocation, targeting, and frequency management—especially in regulated environments like healthcare?

New Methods and Data Sources in Marketing Performance Measurement

The industry is moving from innovation-driven growth to maturity, where credibility and verification matter as much as technical capability.

How are experiments (geo tests, lift studies, randomized trials) utilized to calibrate and verify MMM and AI-driven models?
What demonstrates testing principles in action—such as synthetic controls, matching algorithms, quasi-experimental design, and triangulation—along with embeddedexperimental logic in everyday measurement?
Calibration/Triangulation Best Practices:How do MMM + experiments (geo/holdout/lift) + attribution “speak” to each other, automatically or manually? How can we use the tools together to leverage each one’s strengths? How can we communicate effectively and reduce chaos caused by conflicting outputs from different tools?
How are marketing models incorporating emerging channels such as Retail Media Networks, commerce media, in-game environments, and creator activity?
As investment shifts toward commerce media, how can models differentiate short-term ROAS from long-term brand and demand impacts?
Which high-dimensional modeling techniques (regularization, pooling, Bayesian methods, ML) are proving effective when digital variables exceed available observations?
How can models adapt to platform asymmetry, modeled conversions, and uneven data transparency across ecosystems?
Which newer modeling approaches (causal ML, hierarchical Bayesian, ensemble methods) are showing real added value beyond just predictive lift?
How can organizations embed learning from prior MMM, attribution, and experimentation to enhance future marketing decisions?
How can experimentation be incorporated into AI-driven optimization systems to ensure that automated decisions stay based on empirical evidence?
How can synthetic data and digital twins be applied to improve performance measurement?
How can structured and unstructured data (creative, text, retail, behavioral signals) be transformed into reliable inputs for marketing models?
How can large-scale unstructured data such as reviews, social media, search logs, and video/audio be reliably incorporated into marketing models?
Where have newer methods and data sources fallen short of outperforming simpler, well-established norms—and why? What lessons did we learn from these experiences?
Decision Governance & Organizational Design: How should organizations structure decision rights when measurement systems conflict? When should automated recommendations be overridden? What does effective measurement governance look like at scale? What changes, if any, should be made to marketing planning and execution processes to capitalize on opportunities to optimize based on near-real-time performance signals? What operating models, skills, and decision rights are necessary to successfully act on advanced measurement outputs without causing chaos?
What are the current data challenges and solutions involving first-party, location data, privacy concerns, and multicultural audiences?
Balancing speed with credibility and trust – how do we achieve governance around faster models that may require a tolerance for shifting answers?
Impact of democratized access to analytical outputs through AI – what effect does this have on governance, training, human oversight, and the development of workflows that responsibly incorporate AI-driven recommendations?
Where have platform optimization systems (e.g., automated bidding) conflicted with independent measurement results?
How should marketers balance platform “black box” optimization with transparent measurement frameworks?

Impact of Changes on Consumer Behavior and Tools for Media Activation

Marketing measurement isn’t evolving just because new tools exist; it’s evolving because the environment in which marketing operates has fundamentally changed.

How are MMM, experimentation, and attribution outputs informing real-world execution across programmatic, direct, and retail media environments?
How are new analytic and activation tools shaping buyer decisions on how to bid, when to bid, how much to bid, how to allocate between direct and exchange channels, how to budget, and how to target?
How are new analytic and activation tools shaping seller decisions about setting reserves, choosing inventory for direct versus exchange, determining supply quantities, defining bidding audiences, and setting quality scores?
What defines decision-grade ROI, and how can marketing performance be validated before guiding enterprise budget decisions?
How can MMM, experimentation, and attribution be combined into unified cross-channel measurement systems?
How can marketing performance metrics like ROI, ROAS, and incremental lift be linked to enterprise outcomes such as profitability, customer lifetime value, and long-term growth?
How should organizations align real-time attribution signals with the longer-term demand impacts captured in MMM and brand models?
Under what conditions do dynamic, real-time budget reallocations improve campaign performance versus amplify noise?
How can marketers improve incrementality and causal isolation to better measure the contribution of individual channels and touchpoints?
How do we balance the tradeoffs between gaining faster insights and making trustworthy decisions?
As AI agents increasingly mediate search, comparison, and purchase decisions, how should measurement frameworks adapt?
How has AI affected consumer search and brand discovery?
How effective are ads embedded intoAI?
How should measurement adapt as platforms increasingly control both optimization and reporting?
What is the role of independent measurement in a world of platform-owned decisioning systems?

Branding: Macroeconomics, Attribution, and Predictive Analytics

Brand development is moving into the realm of accountable outcomes, alongside—rather than behind—performance media.

How can marketers measure long-term brand impact and distinguish demand creation from short-term demand capture?
How can organizations balance brand investment and performance marketing by using evidence from MMM and experimentation?
How can brand signals from unstructured sources such as social media, reviews, creator content, and videos be measured and integrated into marketing models?
How can attribution, MMM, and customer lifetime value models be integrated to support long-term planning and investment decisions?
Do planners still need a single, unified customer view, or is the real future a triangulated model that uses MMM, lift tests, platform data, and clean rooms?
How are predictive models improving forecasts of customer lifetime value, churn, and retention?
How do macroeconomic factors such as inflation, consumer confidence, and policy shifts affect brand performance and demand forecasts?
How can brand and demand forecasts be stress-tested as platforms, channels, and consumer behavior evolve?

The Marketing Effectiveness Accelerator is the only event dedicated exclusively to attribution, marketing mix models, and the science of marketing performance measurement. On November 12, leading experts presented empirically grounded case studies that demonstrate how leading brands are solving today’s toughest challenges.

Marketing Effectiveness Accelerator - Call for Content Now Open Through May 30

What Makes a Strong Submission:

Showcasing how insights directly shaped strategic marketing decisions
Transparent methodology
Evidence of validation or triangulation
Acknowledgment of limitations, trade-offs, failed hypotheses, and lessons learned
Generalizable insights or learnings across verticals

Enter your work through the submission portal before May 30.

Sequent Accelerator Award - OPEN FOR ENTRIES THROUGH MAY 30

The Sequent Accelerator Award celebrates technical advances in key aspects of marketing analytics – spanning the modeling processes, metrics, data acquisition, AI applications, insight dissemination and organizational adoption.

In keeping with the spirit of the Marketing Effectiveness Accelerator (The Accelerator), this Award recognizes innovative solutions that deliver meaningful brand impact and advance the discipline of marketing analytics. It celebrates approaches that inspire progress and accelerate measurable effectiveness on brands and across the industry.

All marketing performance analytics innovations from the past five years are eligible.

The winner will be selected based on the strength of their innovative solution, proof of adoption, and impact of the new analytic approach, and will be recognized at the Marketing Effectiveness Accelerator event on November 10.

Download the award template here and send in your nomination through the portal by May 30.

For any questions about content submissions or award nominations, please contact Events Director Sara Serpe at sara@thearf.org.

Speakers

2025 Advisory Committee

We express our gratitude to our Advisory Committee members. This esteemed group of practitioners and academics combined their collective knowledge and curiosity to generate the topics for this year’s MARKETING EFFECTIVENESS ACCELERATOR (MEA) Call for Content.

Agenda

8:00 – 9:00am	Breakfast & Registration
9:00 – 9:10am	Opening Remarks Scott McDonald, Ph.D. – President & CEO, ARF
	New Tools and Data Sources Using advanced attribution, clean rooms, and next-gen MMM tools to improve marketing performance and revenue results.
9:10-9:30am	Advancing MMM Best Practices for Strategic Budget Allocation Traditional MMM methodologies are challenged under the weight of media complexity, demanding ROI proof, and privacy changes. The industry requires new approaches based on open-source transparency, advanced causal inference, and richer data signals. Google will share perspectives on this necessary methodological evolution, highlighting progress in actionability, calibration, and media-specific challenges. The session culminates in a fireside chat with TransUnion focused on advanced data utilization and the path to optimal budget allocation to unlock confident, strategic investment decisions. Mike Finnerty – SVP Marketing Solutions Services, TransUnion Harikesh Nair – Senior Director, Data Science & Engineering, Google
9:30 – 9:50am	From Fragmented Analytics to Unified Revenue Decisions: Large Causal Models for Enterprise Growth Discover how POEM365—a Large Causal Model trained on $5 trillion in transaction data across 15,000 brands—delivered a unified revenue growth decision-making platform for one of the world’s largest advertisers. Learn how counterfactual causal reasoning, rather than correlation-based attribution, enables the Decision AI framework to simultaneously predict, prescribe, and optimize across all revenue drivers in one integrated model—solving the fundamental limitation of siloed MMM approaches and enabling enterprise-wide revenue optimization at scale. Bharath Gaddam – CEO & Founder, DATA POEM
9:50 – 10:10am	Intelligent Digital Attribution Intelligent Digital Attribution (IDA) is an automated, rapid-turnaround measurement system that delivers ROI within five weeks of a campaign’s conclusion. Unlike typical MMM, it provides highly granular insights by platform, format, audience, objectives, placements, and creative themes—helping to enhance Kenvue’s media efficiency and effectiveness continuously. Ross Link – CEO, Marketing Attribution LLC Aleks Petkovski – Senior Director – Data Science and AI, Kenvue
10:10 – 10:30am	Unlocking $100M Revenue Potential: Alaska Airlines with Google Meridian MMM This case study examines Adswerve’s three-month implementation of Google’s open-source Meridian marketing mix model (MMM) for Alaska Airlines. Using advanced analytics and granular data (including GQV), the model projected an 11% increase in addressable advertising revenue (about $100M) and a 3% ROI improvement. The study highlights how modern MMMs can be applied to quantify business outcomes and inform media strategy in complex data environments. Luka Cempre – Head of Data Modernization and Cloud Strategy, Adswerve Owen Bickford – Paid Search Program Manager, Alaska Airlines
10:30 – 11:00am	Morning Break
11:00 – 11:20am	From Audience to Impact: Nielsen’s Framework for Modern Measurement In this session, Nielsen shares their outcome measurement principles: 1) Audience and outcome measurements should be connected and seamless, with measurement and outcomes linked to planning as an end-to-end capability; 2) Collaboration is essential, and no one provider has all the answers; 3) Context is key. How does the use of AI with normative data enable outcome measurements to be estimated where measurement is unavailable or unaffordable? 4) Independent and consistent measurement of outcomes benefits both the buy and sell sides, and independent measurement is crucial for transparency and comparability; 5) Methodology matters. The data and methods used should be transparent, validated, and consistently applied. An assessment of the likely margin of error should also be available. Additionally, measurement continues to evolve with techniques such as Bayesian modeling, data pooling, and synthetic data models. Pete Doe – Chief Research Officer, Nielsen Shweta Shah – VP of Data Science, Nielsen
	Journey-Driven Media Optimization Integrating user journey signals to inform media planning decisions.
11:20 – 11:40am	Unmixed Signals: Connecting Brand Equity and Long-Term Sales First of its kind, global research by Circana and Meta reveals the link between brand equity and long-term sales, providing regional and vertical insights to inform smarter brand investments. It also provides marketers the confidence to make cross-channel budget optimizations based on validated brand equity KPIs. Matthew Sharp – Marketing Science Partner, Meta Tsvetan “T.” Tsvetkov – SVP, Head of Global MMM Consulting, Circana
11:40am – 12:00pm	Beyond Legacy MTA: Using AI-Transformer Based Intelligence to Drive Marketing Prioritization Traditional multi-touch attribution models struggle with dependencies among sequences of touchpoints and fail to integrate marketing channel attribution with on-site clickstream behavior. MTAi™, developed for Memorial Sloan Kettering, integrates marketing channel attribution with on-site behavior, capturing sequential touchpoint dependencies that legacy models miss. The approach enables unified insights to optimize spend, site experience, and conversions. Grant West, Ph.D. – Senior Director, Marketing Science Client Services, in4mation insights Adam Graves – Senior Director, Marketing Measurement, Analytics & Insights, Memorial Sloan Kettering Cancer Center
12:00 – 12:20pm	Positivity Performs: Ad Environments’ Critical Role in Media Planning Historically, little attention has been paid to how customers feel on a platform. This research marks the next step in the industry’s evolving considerations of ad environments, moving beyond avoiding negative environments to exploring what brands should actively seek out in ad environments to drive better outcomes. This research–using survey-based impact analysis, neuroscience testing and marketing mix modelling–explored links between a range of ad environment attributes and brand outcomes. The findings show positive environments are a measurable driver of brand favorability, preference, and purchase intent. Aligning media placements with positive, emotionally resonant and cognitively optimal environments can significantly enhance consumer engagement and sales outcomes-without altering creative or increasing spend. Jenna Landi – Director of Global Brand Research, Pinterest Jonathan Jusczyk – SVP, Science & Engineering Intelligence Solutions, MAGNA
12:20 – 1:20pm	Lunch
	New Approaches for Traditional Channels Maximizing growth beyond traditional media signals in particular channels.
1:20 – 1:40pm	Digitas + KitchenAid: Creative Optimization For KitchenAid Smalls, Digitas combined creative analysis, MMM, and sentiment testing to separate media and creative impact. By assessing 2024 media for wear-out across themes like Durability, Performance, and Café Anarchy, the project pinpointed when creative stopped driving incremental lift—helping optimize spend, refresh timing, and messaging strategy. Mitch Cooper – Brand Manager, KitchenAid Smalls; Whirlpool Corporation Meghan Dimas – Director of Data & Analysis, Digitas Brett Rustin – VP/Group Director of Data & Analysis, Digitas
1:40 – 2:00pm	Optimizing Price Floors and Audiences to Maximize Monetization in Commerce Media McKinsey projects that Commerce Media spending will quickly increase from $40 billion in 2023 to $100 billion in 2027, accounting for nearly a quarter of all ad expenditure. One of McKinsey’s main recommendations is for commerce media ad networks to “use data-driven strategies to maximize ad revenue across different types of inventory.” This project fits within this area by applying auction theory and advertiser data to help retailers set optimal price floors and monetize first-party audiences across display and search inventory, supporting efficiency in a rapidly expanding commerce media landscape. Carl Mela, Ph.D. – Professor, Duke University Jason Pratt – General Manager, Koddi
2:00 – 2:20pm	Improving Media Efficiency with Frequency-Based YouTube Modeling A Circana pilot with Google and MolsonCoors found that adjusting YouTube reach and frequency improves ROAS. Using Google’s Reach & Frequency metrics in Circana’s MMM, the study highlights a scalable, data-driven method for optimizing digital video investment. Yeimy Garcia Smith – SVP of Global Measurement, Circana Madison McDonough – Food & Beverage Analytical Lead, Big Box Retail, Google Michelle Wojnowski – Senior Manager, Marketing Analytics and Optimization, Molson Coors
	Next-Gen Statistical Modeling Applying advanced statistical methods and granular data to analyze marketing performance.
2:20 – 2:40pm	Flipping the Script: How Supply-Side Modeling Can Deepen Client Relationships Traditionally, measurement data modeling has focused on the demand side, largely due to the availability of vast datasets. But what if publishers and media companies could offer their clients a broader view of performance? How might that shift the partnerships with clients? Discover how People Inc. embarked on this journey—using advanced modeling techniques to highlight the “why” behind performance—and how it has strengthened client relationships and demonstrated greater value. Mike Lichter – Sr. Director, Campaign Insights, People Inc. Samantha Powers – VP, Measurement Innovation, People Inc.
2:40 – 3:10pm	Afternoon Break
3:10 – 3:30pm	Leveraging Large-Scale Granular Single-Source Data for TV Advertising This research introduces a novel method to estimate the causal impact of linear TV advertising using large-scale, single-source data that connect household-level TV viewing and ad exposure to daily purchasing. Results demonstrate that baseline purchase propensity and ad responsiveness vary systematically with both the frequency and recency of past purchases, yielding actionable insights for more effective behavioral targeting of TV ads. Rex Du – Professor of Marketing, University of Texas at Austin
3:30 – 3:50pm	The Future of IP Address as a Measurement Signal New research from CIMM highlights a potential canary in the coal mine for video measurement: the growing instability of IP addresses as identifiers for viewers. As the foundation for countless measurement products and platforms, this instability could signal fundamental challenges ahead for the entire video measurement ecosystem. Evan Cohen – SVP, Projects and Strategic Initiatives, CIMM Gregg Galletta – President, Truthset
3:50 – 4:10pm	Creative That Converts: A Neuroscientific Approach to Image Effectiveness Creative assets, especially images, play a critical role in driving marketing performance. But quantifying their impact has long been a challenge. This talk introduces a scalable NeuroAI framework that models how the human brain processes product images, offering a new way to predict which visuals will convert attention into action. Validated in the travel sector, the findings show that images with clear visual pathways and spatial structure drive more consumer action, while cluttered or cognitively demanding visuals suppress action. Crucially, the framework is industry-agnostic and can be applied across verticals and channels to help marketers and measurement teams better evaluate the creative elements that influence outcomes, informing attribution models, MMM inputs, and creative optimization at scale. Gijs Overgoor – Assistant Professor of Marketing, Cox School of Business, Southern Methodist University
4:10 – 4:30pm	Refresh to Impress: Cadence Counts When Refreshing Your Marketing Mix Model Most Marketing Mix Models refresh infrequently and are slow to adapt to changing market, competitive, and consumer conditions. OptiMine conducted research to identify the optimal refresh cadence for models and has identified the lift potential of increasing model refreshes for brands that struggle with model sluggishness. Matt Voda – CEO, OptiMine
4:30 – 4:35pm	Analytic Partners Remarks Closing reflections on commercial imperatives shaping the future of marketing measurement. Mike Menkes – Group Senior Vice President, Analytic Partners
4:35 – 4:55pm	New this year! Sequent Accelerator Award Celebrates technical advances in key aspects of marketing analytics and recognizes innovative solutions that deliver meaningful brand impact and advance the discipline of marketing analytics. View the winners.
4:55 – 5:00pm	Closing Remarks Scott McDonald, Ph.D. – President & CEO, ARF
5:00 – 6:30pm	Cocktail Reception

PRICING

IN-PERSON	Early Mover 10/5/2026	Standard 11/10/2026
MEMBER	$599	$799
NON-MEMBER	$799	$999

LIVESTREAM	10/5/2026	11/10/2026
MEMBER	$299	$449
NON-MEMBER	$449	$599