Why Media Mix Models Need a Prior Year Baseline

Episode 21 of Marketing Analytics with Fexingo digs into why ignoring last year’s data can break your media mix model. Lucas and Luna explore a 2024 case from a mid-sized DTC brand—let’s call it FreshStep—that ran a full MMM without a 2023 baseline. The result? It over-credited social video by 22% and under-credited search by 15%, leading to a $340,000 misallocation in Q1 2025 alone. They walk through how Bayesian priors from prior-year data would have caught the error, why many teams skip this step, and what it means for your 2026 planning cycle. Packed with real numbers and a clear takeaway: your model is only as good as the history you feed it. Perfect for marketers, analysts, and anyone building or buying attribution tools. #MarketingAnalytics #MediaMixModel #MMM #BayesianStatistics #PriorYearBaseline #Attribution #DTCBrand #MarketingBudget #AdSpend #SocialVideo #SearchMarketing #DataScience #MarketingROI #CampaignMeasurement #2026Marketing #MarketingPodcast #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo