Forecasting is a valuable tool to help businesses reduce risk and operate more effectively and efficiently. At FORECASTING 2024, leading practitioners and academics shared how they are building forecasting models to understand purchase journeys, the impact of major events on businesses and how brands can reach buyers amidst the lack of first-party data.
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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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Attendees joined ARF in NYC for two workshops – a morning session tailored for professionals without a background in coding, and an afternoon session for data scientists, analysts, engineers, and other data science professionals – that highlighted how to utilize LLMs to analyze example datasets. Additionally, attendees had the chance to participate in real-time AI tool demonstrations.
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Explore the latest findings from the ARF Attention Measurement Validation Initiative. The phase two report is a comprehensive examination of various attention measurement methods used in creative testing. It concludes with reflections on the challenges of attention measurement, as well as some suggestions for advertisers on how to choose and evaluate attention measurement providers.
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