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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The 2016 U.S. presidential election served a big blow to the polling community, as pre-election predictions proved to be dramatically wrong. But is there really a crisis in pre-election polling? Mark Blumenthal, Head of Election Polling at SurveyMonkey, summarizes the AAPOR’s post-election investigation that demonstrated the accuracy of national polls, but weaknesses in state-level polls. Part of the challenge going forward – correcting for sample bias in an age of lower survey response rates.
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Should the marketing community support repeating a study, when so much value is placed on new research? The answer may be a resounding “Yes.” Learn why repeating a study matters, especially when marketers seek transparency and data-based support for recommendations — and how advertising research might need to change.
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