On January 15, the L.A. Media Research Council explored key priorities and the most important topics for business in the upcoming year. Attendees gained research-based insights on the ever-evolving media landscape, shifting viewer behaviors, and the challenges that will require new research in 2025.
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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.
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Despite massive growth driven by significant investments, retail media performance measurement still falls short in many areas. On October 15, OptiMine and Best Buy dove deep into the use of incrementality measurement for retail media, how it works and why it is so unique in the RMN space. Attendees explored why (and how) some of the world’s largest brands have embraced it for improved success.
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Going beyond demographics, planning, buying and selling TV with advanced audiences enables a greater bang for the advertising buck and enables sellers to optimize inventory allocation. On October 9, attendees learned about the opportunities and challenges associated with this increasingly used capability. Pete Doe of Nielsen discussed how to apply data-driven insights to enhance the effectiveness of advertising strategies. After, Paul Donato, Chief Research Officer of the ARF, led a follow-up conversation.
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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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The Marketing Analytics Accelerator – the only event focused exclusively on attribution, marketing mix models and the science of marketing performance measurement – returned for its ninth year on November 13. The industry’s boldest and brightest minds joined us in NYC to share their latest innovations and case studies that will improve your business outcomes.
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Attendees joined us on October 23 for our annual OTT conference, offering the latest research on shifts in the TV and video landscape, viewer behavior, and cross-platform measurement. Industry experts discussed trends in viewing habits, advertising innovations, and predictions for 2025. Attendees also had the opportunity to participate in discussions and network with industry peers over breakfast, lunch, and the cocktail reception.
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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.