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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Troubling. Bias. Distortion. Why would the ANA use these words in a letter to the U.S. Government expressing concern about the impact of the 2020 Census on marketing quality? Learn about what led them to act, why the ARF supports them, and possibly join them in sharing your views before the feedback period ends on August 7, 2018.