How Novelty Effects Ruin Your A-B Test Results

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How Novelty Effects Ruin Your A-B Test Results

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Episode 47 of Conversion Rate Optimization with Fexingo dives into the novelty effect—why users behave differently with a new design simply because it's new, not because it's better. Lucas and Luna use a real-world example from a SaaS company that saw a 25% lift in the first week, only to see it vanish by week three. They explain how to detect novelty effects with time-segmented analysis and holdout groups, and why running tests for at least two full business cycles is critical. The episode includes practical tips on setting up a 'novelty check' in your analytics and interpreting early data without jumping to conclusions. Hosts also touch on listener support via buy me a coffee dot com slash fexingo. #NoveltyEffect #ABTesting #ConversionRateOptimization #CRO #Marketing #FexingoBusiness #BusinessPodcast #SaaS #DataDriven #ExperimentDesign #UserBehavior #StatisticalValidity #DigitalMarketing #Analytics #Experimentation #GrowthHacking #ProductDesign #HoldoutGroup Keep every episode free: buymeacoffee.com/fexingo

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