Why Media Mix Models Need a Prior Year Baseline

What this episode covers

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

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Frequently Asked Questions

How long is this episode of Marketing Analytics with Fexingo: Data, Attribution, and Measuring Campaign Performance?

This episode is 10 minutes long.

When was this Marketing Analytics with Fexingo: Data, Attribution, and Measuring Campaign Performance episode published?

This episode was published on May 30, 2026.

What is this episode about?

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....

Can I download this Marketing Analytics with Fexingo: Data, Attribution, and Measuring Campaign Performance episode?

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