Analytics & Data Science

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.  

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People’s Performance Goals Shape Their Use of Predictive Algorithms

People’s Performance Goals Shape Their Use of Predictive Algorithms

This study presents a framework for understanding people’s use of predictive algorithms, emphasizing their role as tools designed to support human decision-making. It argues that users’ performance expectations are a primary driver of their decisions to adopt these algorithms. By reviewing and reinterpreting the literature through the lens of laypeople’s performance expectations, the study aims to clarify why some algorithms are accepted and others are rejected. It concludes by suggesting avenues for designing algorithms that better meet users’ expectations, enhancing their usability and acceptance.

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AI for Data Analysis in Marketing and Advertising

AI for Data Analysis in Marketing and Advertising

Attendees joined ARF in NYC for two workshops – a morning session tailored for professionals without a background in coding, and an afternoon session for data scientists, analysts, engineers, and other data science professionals – that highlighted how to utilize LLMs to analyze example datasets. Additionally, attendees had the chance to participate in real-time AI tool demonstrations.

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As the Cookie Crumbles: Perspectives on Cross-Platform Data from Every Corner of the Industry

As the Cookie Crumbles: Perspectives on Cross-Platform Data from Every Corner of the Industry

  • By Margaret Felger (Cint), Young Pros Officer
  • CROSS-PLATFORM MEASUREMENT COUNCIL

On February 5, 2025, the Offline-Online Metrics Working Group of the ARF Cross-Platform Measurement Council hosted a panel of measurement experts from different sectors of the industry to discuss cross-platform measurement challenges and opportunities in today’s evolving data landscape. The session kicked off with a presentation from Rishi Saxena (World Federation of Advertisers) on the WFA’s Findings for Cross Media Measurement and Advertising Needs, which covered issues that marketers face around media fragmentation, frequency, data challenges, and need for new solutions. Following the presentation, the panel members discussed how their respective companies are facing these challenges and how they are preparing for the future. Working Group Chair Charles Buchwalter moderated the engaging conversation with Karen Chisolm (Pernod Ricard), Lee Doyle (Empower Media), Neil Napolitano (DotDash Meredith), and Working Group member Rishi Saxena (WFA).

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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.

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LOLA: Revolutionizing Content Experiments with LLM-Assisted Online Learning

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.

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