Leveraging Data

Discover the latest and most impactful research on leveraging data analytics to generate business insights 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.
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.

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When AI Takes the Survey: Evaluating LLMs as a New Tool for Consumer Insight

When AI Takes the Survey: Evaluating LLMs as a New Tool for Consumer Insight

  • ARF
  • MSI

The study in this MSI working paper evaluates whether large language models (LLMs) can serve as a reliable source of consumer preference data—potentially transforming how market research is conducted. Using conjoint-style survey questions, the researchers compared LLM-generated choices with human responses to estimate willingness-to-pay (WTP) for a variety of product attributes. They find that LLMs often approximate human preferences surprisingly well, especially when fine-tuned with prior survey data, though important limitations remain. For marketers, the research highlights both the promise and the boundaries of using AI-generated insights to accelerate testing, concept screening and early-stage innovation work

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Traversing Data Silos: A Practical Framework for Identity Crosswalks in Advertising

Traversing Data Silos: A Practical Framework for Identity Crosswalks in Advertising

  • ARF | Cross-Platform Council
  • ARF ORIGINAL RESEARCH

Advertisers rely on identity crosswalks as a critical tool for linking identifiers across data sets and platforms without exposing personal information. This white paper from the Identity Resolution Working Group of the Cross-Platform Measurement Council provides a brief practical introduction to crosswalks and how to implement them effectively. It outlines common operational models, covers use cases for brands, agencies and publishers, and addresses accuracy, privacy and match rate considerations. The guide offers advertising researchers and data practitioners clear, actionable steps for navigating the complex identity landscape.

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How AI and Privacy Regulations are Changing Media Planning Practices

How AI and Privacy Regulations are Changing Media Planning Practices

  • ARF; MSI; CIMM

The Advertising Research Foundation (ARF), in collaboration with CIMM and MSI, presents The Future of Media Planning in an AI-Powered, Data-Driven World. This report explores how AI, privacy-first strategies and outcome-based metrics are transforming the discipline of media planning. Designed for researchers and practitioners, the paper provides a roadmap for navigating audience fragmentation, evolving regulations and new measurement frontiers—while keeping business outcomes at the center of strategy.

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The ARF Metrics Survey: Insights into Advertising Strategies

The ARF Metrics Survey: Insights into Advertising Strategies

  • ARF

The ARF Metrics Survey explores critical trends in advertising strategies, including budget allocation, metric usage and targeting solutions. Drawing on insights from agencies and brand-side advertisers, the findings reflect shared priorities, notable differences and challenges in evaluating and optimizing campaign performance. The study highlights the dominance of digital channels, with high-priority channels such as digital video, social media, SEM and television receiving significant budget allocations.

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

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.

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Using AI Assistants to Predict Purchase Intent

Using AI Assistants to Predict Purchase Intent

  • MSI

This study examines user interactions with AI assistants to infer purchase intent. By analyzing the text of user-initiated interactions, researchers build a bipartite network of nouns and verbs and measure the distance of specific words to "golden" purchasing words like "purchase," "buy" or "order." The study uses large language models, specifically Chat-GPT4, to annotate data with a measure of purchase intent and validates this method by comparing the results with cost-per-click (CPC) for keywords in Google Ads. The findings suggest that words used in an exchange with an AI assistant can predict purchase intent without customer tracking across interactions.

These findings have implications for using customized small versus large language models and can potentially inform advertising decisions. The study highlights the importance of understanding consumer behaviors in interactions with AI assistants. It provides a method to predict purchase intent based solely on the textual content of these interactions.

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Retail Media Networks: Growth, Opportunities and Challenges

Retail Media Networks: Growth, Opportunities and Challenges

  • Knowledge at Hand; CMO Briefs

Retail media networks (RMNs) are rapidly becoming one of the largest and fastest-growing ad verticals in the U.S., offering personalized ads across retailers’ ecosystems. These networks also provide brands with targeted ads and higher conversion rates, while retailers gain new revenue streams and enhanced shopping experiences.

Despite staggering growth, RMNs face many challenges as they continue to grow globally, including a lack of standardization across networks, measurement difficulties, rising costs and complexity. Agencies face additional issues, such as teams lacking the necessary expertise to help clients navigate RMNs.

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How Social Media Platforms Affect Music Consumption

How Social Media Platforms Affect Music Consumption

  • MSI

Are social media platforms compensating musical artists enough for their IP? Although music companies claim social platforms cannibalize sales, the platforms say they give artists much needed exposure. Who is correct? The study examines the impact of TikTok on music consumption and revenue. It highlights what TikTok pays studios and discusses the concerns from Universal Music Group (UMG) regarding inadequate compensation for its artists.

Additionally, it mentions that videos on TikTok function as promotional materials or trailers for full-length productions. The study uses a  difference-in-difference model to analyze the data. It concludes that music labels can sharpen their licensing agreements based on the findings.

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