Why Marketing Attribution Models Miss Platform Effects

In episode 59 of Marketing Analytics with Fexingo, hosts Lucas and Luna unpack a blind spot most attribution models share: platform effects. Lucas explains how ad platforms like Google and Meta optimize delivery based on their own models, creating a feedback loop that inflates performance metrics. He illustrates with a real example from a DTC brand that saw Meta attribute 60% of conversions to its ads, yet a controlled experiment showed only 30% incremental lift. The episode explores how platform bidding algorithms target users already likely to convert, how attribution models double-count overlapping audiences, and why incrementality testing is the only reliable check. Luna challenges Lucas on whether small teams can afford this rigor, and they discuss practical compromises like holdout groups and geo tests. The conversation includes a sincere listener-support pitch for buy me a coffee dot com slash fexingo. No hot takes, just a clear explanation of a structural problem in marketing measurement. #MarketingAnalytics #AttributionModeling #PlatformEffects #IncrementalityTesting #DTCBrand #MetaAds #GoogleAds #MarketingMeasurement #AdWaste #ConversionAttribution #BiddingAlgorithms #DataDrivenMarketing #MarketingROI #BusinessPodcast #FexingoBusiness #PodcastEpisode #MarketingInsights #Analytics Keep every episode free: buymeacoffee.com/fexingo