Future States & AI

• Ad Effectiveness & ROI
• Article

Algorithmic Trade-Offs: How Regulating Recommendations Shapes E-Commerce Markets

• ARF

What happens when governments regulate how platforms recommend products? A new study examining Amazon's compliance with the UK’s Competition and Markets Authority rule—banning the use of Prime eligibility to determine “Featured Offers”—finds both gains and trade-offs. While the regulation did reduce self-preferencing and boosted price competition, it also led to fewer consumer purchases and lower product ratings, especially in niche markets.

• Research & Data Quality
• Article

Alternative Explanations: Can AI Rethink Its Own Reasoning?

• ARF

Can AI challenge its own conclusions rather than merely reinforcing them? In this ARF experiment, researchers explored whether large language models (LLMs) like ChatGPT can go beyond efficiency and exhibit deeper critical thinking skills. By prompting AI to evaluate and compare hypotheses—including its own—this study reveals how LLMs can serve as interpretive collaborators in research and theoretical reasoning.

• Future States & AI
• Article

Designing with Machines: A Co-Creative Model for Generative AI in Advertising Agencies

• ARF
• Journal of Advertising Research

A new study uncovers how advertising agencies are attempting to integrate generative AI into their creative workflows, and the repercussions of that process. Based on interviews and field data, the research presents a four-phase model—readiness, co-creativity, validation and execution. Although the study reveals certain anxieties surrounding this integration, in the end both human and AI elements together are required to boost and enliven the creative process.

• 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.

• Article

Marketing’s Moment: How Resilient Brands can Thrive in Times of Uncertainty

• ARF

Marketing is more than a growth lever—it’s a critical driver of resilience in uncertain times. This Marketing Science Institute (MSI) synthesis presents evidence-based strategies for marketing leaders to help their organizations adapt, protect core assets and unlock long-term value amid volatility. Drawing from leading journals, it outlines how marketing can be reimagined as a flexible, risk-aware and analytics-driven function that steers firms through disruption toward recovery and competitive advantage.

• Research & Data Quality
• Article

Learn About the Benefits, Limitations and Dangers of Using LLMs in Advertising Research

• ARF
• Knowledge at Hand

This report offers an abridged version of the cumulative 2025 AI handbook, exploring the benefits, limitations and dangers of generative AI models when employed in various tasks associated with marketing and advertising research. This report sums up the insights from several experiments where ARF researchers tested AI's capabilities in survey design, sentiment analysis, coding open-ended responses, language translation and addressing AI risks.

• Research & Data Quality
• Article

The 2025 Guide on Using AI for Advertising Research

• ARF ORIGINAL RESEARCH

This update to the ARF’s first comprehensive handbook, released last year, provides an exploration into the burgeoning technology’s transformative role in marketing and advertising research. It covers the integration of AI into marketing strategies, ethical considerations, future trends and practical case studies. This handbook is an essential guide for advertising research professionals looking to leverage any of the latest AI platforms while ensuring ethical and impactful outcomes.

• Targets & Segments
• Article

Maximizing Customer Lifetime Value through Subscription Models

• ARF
• MSI

This research explores the downstream and upstream effects of consumer-to-consumer (C2C) gift subscriptions compared to personal subscriptions in the context of live streaming. It reveals that C2C subscriptions significantly enhance customer lifetime value by encouraging more tips and comments and highlights the importance of creator performance quality in driving subscription behaviors.

• 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.

• Research & Data Quality
• Article

Should Experiments Use LLMs as Human Surrogates? This Study Gives a Resounding No.

This study evaluates the reasoning depth of large language models (LLMs) using the 11-20 Money Request Game, an experimental game designed to test level-k reasoning. Level-k reasoning is a theoretical framework in game theory where individuals operate at varying levels of strategic thinking. The findings of the study reveal significant differences between the responses of LLMs and human participants, highlighting the limitations of using LLMs as human surrogates in behavioral experiments. This research emphasizes the need for caution when interpreting LLM behavior as human-like, as the models often exhibit inconsistent and non-human-like reasoning patterns. The study suggests that while LLMs can provide valuable insights, they should not be relied upon as accurate simulations of human behavior.