predictive analytics

ATTENTION 2025

  • ARF

Industry leaders gathered at ATTENTION 2025 on June 5 to explore the evolving science of attention measurement in advertising. This event featured new research from advertisers, agencies, and academics, including preliminary findings from Phase 3 of the Attention Measurement Validation Initiative. Attendees gained actionable insights into scalable attention metrics, their role in optimizing creative and media strategies, and how brands can better identify and engage audiences to drive results.

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Big Data and Advanced Audiences

  • Insights Studio Series

On March 12, the ARF hosted an Insights Studio exploring actionable strategies to improve audience segmentation, refine measurement practices, and deliver better campaign results. Nielsen shared an update on how data-driven insights enhance advertising effectiveness. DatafuelX and TelevisaUnivision unveiled general and specific findings about the value of Nielsen Panel & Big Data for data-driven linear campaigns.

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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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Improve Marketing Mix Model (MMM) Accuracy by Identifying these Effects

  • MSI

This study explores the identification of nonlinear and time-varying effects in marketing mix models (MMM). It highlights the challenges of conflation in model selection and proposes a framework for simulating and estimating these effects using Gaussian processes. The study emphasizes the importance of accurately identifying the underlying response to optimize marketing spending.

The research provides insights into the complexities of marketing effectiveness and offers practical solutions for improving model accuracy. By addressing the issue of conflation, the study aims to enhance the decision-making process in marketing strategies.

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