When Marketing Analytics Confuses Correlation With Causation

Lucas and Luna explore how marketing analytics teams routinely confuse correlation with causation—and why it costs millions in wasted ad spend. They unpack a 2025 experiment from a mid-size e-commerce brand that ran a geo-lift test on its highest-performing Facebook ads. The ads looked brilliant in the attribution dashboard: 12x return on ad spend. But the geo test revealed the campaign drove zero incremental revenue; buyers were converting anyway through organic search. The hosts explain why attribution models blind you to counterfactual reality, the difference between incremental and absolute lift, and how a simple holdout structure can save your budget. They also discuss what happens when machine learning optimization algorithms optimize for spurious correlations. This is episode 29 of Marketing Analytics with Fexingo: Data, Attribution, and Measuring Campaign Performance. #MarketingAnalytics #CorrelationVsCausation #GeoTesting #Incrementality #AttributionFailure #AdSpendWaste #HoldoutGroups #Counterfactual #CausalInference #FacebookAds #Ecommerce #MachineLearningBias #ROIMyth #AttributionModel #LiftTest #Business #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo