Cross-Platform

• Future States & AI
• Article

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.

• Future States & AI
• Article

Tracking the Why Over Time: A New Era of AI-Powered Advertising Research

• ARF
• Journal of Advertising Research

A new framework for measuring advertising impact over time—“intensive longitudinal mediation”—uses AI and big data to reveal when and why ads work. This approach helps marketers capture psychological shifts in real-time, going beyond static analysis to uncover how advertising drives behavior over days or weeks.

• Targets & Segments
• Article

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.

• Audience & Media Measurement
• Article

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.  

• Research & Data Quality
• Article

LOLA: Revolutionizing Content Experiments with LLM-Assisted Online Learning

In the rapidly evolving digital content landscape, media firms and news publishers require automated and efficient methods to enhance user engagement. This study introduces the LLM-Assisted Online Learning Algorithm (LOLA), a novel framework that integrates Large Language Models (LLMs) with adaptive experimentation to optimize content delivery. Leveraging a large-scale dataset from Upworthy, which includes 17,681 headline A/B tests, the study investigates three pure-LLM approaches and finds that prompt-based methods perform poorly, while embedding-based classification models and fine-tuned open-source LLMs achieve higher accuracy.

LOLA combines the best pure-LLM approach with the Upper Confidence Bound (UCB) algorithm to allocate traffic and maximize clicks adaptively. Numerical experiments on data from the website Upworthy show that LOLA outperforms the standard A/B test method, pure bandit algorithms and pure-LLM approaches, particularly in scenarios with limited experimental traffic. This scalable approach is applicable to content experiments across various settings where firms seek to optimize user engagement, including digital advertising and social media recommendations.

• Research & Data Quality
• Article

An Introduction to Robyn’s Open-Source Approach to Media Mix Modeling

• MSI

As privacy-centric changes reshape the digital advertising landscape, deterministic attribution and measurement of advertising-related user behavior are increasingly constrained. In response, there has been a resurgence in the use of traditional probabilistic measurement techniques, such as media and marketing mix modeling (m/MMM), particularly among digital-first advertisers. To address the gap for small and midsize businesses, marketing data scientists at Meta have developed the open-source computational package Robyn, designed to facilitate the adoption of m/MMM for digital advertising measurement.

Robyn is a widely adopted and actively maintained open-source tool that continually evolves. This article explores the computational components and design choices that underpin Robyn, emphasizing how it “packages up” m/MMM to promote organizational acceptance and mitigate common biases. The solutions described are not definitive but outline the pathways that the Robyn community has embarked on.

• Audience & Media Measurement
• Article

Using AI to Revolutionize Generational Consumer Research

• MSI

This study explores the potential of generative AI, specifically large language models (LLMs) and multimodal models (LMMs), to revolutionize generational study within consumer research. It examines how these advanced AI tools can enhance various stages of the research process, from idea generation to reporting.

• Research & Data Quality
• Article

Overcoming the Limitations of Online A/B Testing

• MSI

This study explores how divergent delivery in A-B testing affects the accuracy of online advertising experiments. It highlights the role of algorithmic targeting and user heterogeneity in confounding test results, offering guidance for marketers to improve their experimental designs.

• Research & Data Quality
• Article

ARF Attention Measurement Validation Initiative: Phase 2 Report (2nd Edition)

• ARF ORIGINAL RESEARCH

Explore the latest findings from the ARF Attention Measurement Validation Initiative. The phase two report is a comprehensive examination of various attention measurement methods used in creative testing. It concludes with reflections on the challenges of attention measurement, as well as some suggestions for advertisers on how to choose and evaluate attention measurement providers.

• Audience & Media Measurement
• Article

Predicting Attention to Advertising Through Machine Learning

Privacy regulations have served as the impetus for a renewed interest in contextual targeting. To be effective, an ad must be related to its context but different enough to stand out. This working paper from the Marketing Science Institute (MSI) at the ARF presents a comprehensive model leveraging eye-tracking data and XGBoost algorithms to forecast the effectiveness of ad placements in real time.