Can sanitized AI tools truly capture the nuance required for advertising and brand research? Is a less restrained one more likely to produce skewed results? This comparative deep dive, from ARF and MSI explores how two popular large language models—ChatGPT-4o and Grok 3—respond when prompted with complex topics. The findings highlight how content moderation affects not only tone and specificity, but the very boundaries of inquiry. For advertising researchers navigating sensitive brand perception topics, understanding these model tradeoffs is essential.
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How we use data today looks different given evolving regulations, platform changes, consumers expectations of data transparency, and more. Our Insights Studio on January 30 explored the latest developments in data privacy and how they are impacting marketing strategies. Panelists unveiled strategies to establish consumer trust and effectively market, while aligning with privacy regulations.
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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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This study explores how divergent delivery in A-B testing affects the accuracy of online advertising experiments. It highlights the role of algorithmic targeting and user heterogeneity in confounding test results, offering guidance for marketers to improve their experimental designs.
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