Why Media Mix Models Need Seasonality Adjustments

In this episode of Marketing Analytics with Fexingo, Lucas and Luna dive into a common pitfall in marketing mix modeling: failing to adjust for seasonality. Using the example of a mid-size e-commerce brand that saw a 20% sales lift in Q4 and wrongly attributed it to Facebook ads, they explain how naive models conflate seasonal demand with campaign effectiveness. Lucas walks through a concrete case where a simple additive seasonality factor saved the client $2 million in misallocated budget. They discuss practical ways to incorporate seasonality—through dummy variables, Fourier terms, or external data like retail calendars—and why ignoring it leads to inflated ROAS estimates. The hosts also touch on recent trends in marketing analytics, including Bayesian structural time series and the rise of automated seasonality detection tools. Tune in to learn how to spot seasonality blind spots in your own models and avoid costly attribution errors. #MarketingAnalytics #MarketingMixModeling #Seasonality #Attribution #ROAS #Bayesian #Ecommerce #DataScience #AdSpend #CampaignMeasurement #FexingoBusiness #BusinessPodcast #Marketing #Analytics #MediaMix #LucasAndLuna #Podcast #Episode45 Keep every episode free: buymeacoffee.com/fexingo