On November 20, the ARF held a workshop exploring prompts, personas and how to use AI responsibly. This dynamic event, designed for advertising and marketing professionals looking to explore the evolving landscape of AI-powered research, provided insights into prompt crafting. Participants also gained a deeper understanding of the promises and pitfalls of using personas in AI-powered advertising and marketing research.
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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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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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