Why Attribution Models Lie About Your Best Channels

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Why Attribution Models Lie About Your Best Channels

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Most marketers trust their attribution reports to tell them which channels drive conversions. But Lucas and Luna reveal why those reports are often dangerously misleading. They examine a 2024 case study from a mid-market DTC skincare brand that found its highest-converting channel, Instagram ads, was actually its worst-performing when measured incrementally. Using a meta-analysis of 178 A/B tests from Skai and a real example from the brand Pai Skincare, they show why last-click, first-click, and even multi-touch attribution models routinely overcredit 'closing' channels like search and undercredit 'opening' channels like podcasts and TV. The episode walks through the concept of incremental lift, explains why a channel with a lower last-click conversion rate can be your most profitable bet, and offers a practical framework for running holdout tests without a massive budget. By the end, you'll understand why one brand that shifted 40% of its budget from paid search to podcast sponsorships saw a 22% increase in overall revenue — even though its attribution dashboard showed search as the 'winner.' #AttributionModels #MarketingAttribution #IncrementalLift #CustomerJourney #DTCBrands #PaiSkincare #Skai #MultiChannelMarketing #LastClickAttribution #FirstClickAttribution #HoldoutTests #AdvertisingROI #PodcastAdvertising #PaidSearch #MarketingStrategy #FexingoBusiness #BusinessPodcast #Marketing Keep every episode free: buymeacoffee.com/fexingo

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