Cross-Platform

Discover the latest and most impactful research on audience, media and advertising measurement across platforms and devices here. All the research listed comes from the ARF or one of its subsidiaries: The Journal of Advertising Research (JAR), the Marketing Science Institute (MSI) or the Coalition for Innovative Media Measurement (CIMM). Feel free to bookmark this page, as it will be updated periodically.
Learning Across Marketing Experiments: A Bayesian Approach to Improving Targeting

Learning Across Marketing Experiments: A Bayesian Approach to Improving Targeting

  • ARF, MSI

Companies run many marketing experiments, but most A/B tests are analyzed independently—limiting what firms can learn about how customers respond to interventions over time. This research introduces a hierarchical Bayesian framework that integrates data from many experiments simultaneously to estimate customer-level responsiveness to marketing. Using large-scale field experiments, the model decomposes treatment effects into customer, campaign and timing components and uses these insights to improve targeting decisions. The results show that most variation in marketing effectiveness comes from persistent differences in customer responsiveness, enabling firms to better identify who to target and when.

Member Only Access
Why Don’t Advertisers Experiment More? The Economics Behind Incrementality Testing

Why Don’t Advertisers Experiment More? The Economics Behind Incrementality Testing

  • ARF; MSI

Randomized experiments are widely viewed as the gold standard for measuring advertising incrementality, yet advertisers run them far less often than theory would predict. This MSI working paper develops a game-theoretic model of online advertising to explain why. By explicitly modeling the fixed and opportunity costs of experimentation—and the strategic role of ad platforms in setting reserve prices—the study shows how platforms may rationally discourage experimentation even when learning ad effectiveness could improve allocation efficiency. The findings offer a new economic explanation for persistently low experimentation rates in digital advertising markets.

Member Only Access
Predicting What Moves the Needle: Marketing Mix Modeling After Cookies

Predicting What Moves the Needle: Marketing Mix Modeling After Cookies

  • ARF, MSI

As privacy regulations and the deprecation of third-party cookies limit access to individual-level consumer data, advertisers are increasingly forced to rely on aggregate metrics to evaluate marketing effectiveness. This MSI working paper introduces a novel, two-stage, marketing mix modeling framework designed specifically for cookie-free environments. By combining machine-learning–based directional prediction with classical econometric calibration, the approach demonstrates how firms can extract reliable signals about campaign effectiveness—even from short, noisy, aggregate time series—while maintaining interpretability and practical relevance for marketing decision-making.

Member Only Access
Why Irregular Ad Scheduling Wins: Uncovering the Hidden Dynamics of Skippable Ad Acceptance

Why Irregular Ad Scheduling Wins: Uncovering the Hidden Dynamics of Skippable Ad Acceptance

  • ARF
  • JOURNAL OF ADVERTISING RESEARCH

A large-scale analysis of clickstream data reveals that how often and how regularly pre-roll skippable ads are shown significantly influences whether users accept or skip them. Using advanced modeling, researchers find that less frequent and more irregular exposure increases the hidden “acceptance propensity” underlying viewing behavior. These findings challenge conventional scheduling tactics and offer new strategies for maximizing skippable ad effectiveness.

Member Only Access
Navigating Identity Loss: Measurement and Targeting in a Privacy-First Era

Navigating Identity Loss: Measurement and Targeting in a Privacy-First Era

  • ARF
  • ARF Analytics Council

How is the loss of digital identifiers reshaping advertising research? This guide, by the ARF Analytics Council, offers advertising researchers a deep dive into the privacy-first landscape, covering regulatory impacts, measurement challenges and practical identity solutions—from synthetic IDs to advanced modeling—to enable successful targeting and attribution in a fragmented ecosystem.

Member Only Access
Discover the Current State of Attention Metrics in Today’s Marketplace

Discover the Current State of Attention Metrics in Today’s Marketplace

  • ARF
  • ARF

The ARF Attention Report explores the evolving role of attention metrics in advertising, examining their adoption, effectiveness and challenges across agencies and brand-side advertisers. The data is based on a survey measuring the array of metrics currently used by advertisers and agencies across various platforms and industries. As the advertising industry increasingly values consumer engagement beyond traditional reach-based metrics, attention metrics have gained prominence, though adoption and integration remain uneven. Key findings include that attention metrics are gaining traction, with 64% of agencies and 43% of advertisers reporting frequent or constant use. Engagement rates (58%) and time spent (55%) are the most widely used attention metrics due to their accessibility, while advanced methods like biometric response (16%) and neuro measures (13%) remain relatively niche.

Member Only Access
Dive Deep into Retail Media Networks: One of the Fastest Growing Channels in the US

Dive Deep into Retail Media Networks: One of the Fastest Growing Channels in the US

  • ARF
  • ARF

Retail Media Networks (RMNs) have emerged as one of the fastest-growing advertising channels in the U.S., transforming how brands reach and engage consumers. Leveraging first-party data, RMNs enable targeted, measurable campaigns across retailers’ ecosystems, opening a unique blend of performance-driven and brand-building opportunities. Learn about this emerging channel, including the commonalities and differences in how they are being used between advertisers and agencies, in this deep dive into RMNs.

Member Only Access
Learn How to Train LLMs to Identify Implicit Consumer Needs

Learn How to Train LLMs to Identify Implicit Consumer Needs

This study explores the potential of large language models (LLMs) to revolutionize marketing research. By partnering with a Fortune 500 food company, the authors replicated qualitative and quantitative studies using GPT-4. The findings indicate that LLMs can effectively generate synthetic respondents, moderate in-depth interviews and perform data analysis tasks, matching or even surpassing human performance in certain aspects. The study highlights the benefits of a Human-LLM hybrid approach, where LLMs assist in various stages of the research process, from study design to data analysis. This approach not only enhances efficiency but also uncovers new insights that might be overlooked by human researchers alone.

Member Only Access
Evaluate Identity Resolution Effectively with this Council Guide

Evaluate Identity Resolution Effectively with this Council Guide

  • ARF ORIGINAL RESEARCH

Identity resolution (IDR) is crucial in media measurement and advertising, connecting media messaging to individuals. This guide, produced by the ARF Identity Resolution Working Group (of the ARF Cross-Platform Measurement Council), explores different units of analysis in IDR beyond individuals, such as households, geography and cohorts and their implications for matching quality, targeting and marketing success.  

Member Only Access
 Next »