Why Marketing Models Need Cross-Validation

In this episode, Lucas and Luna explore why cross-validation is a missing ingredient in many marketing attribution and media mix models. They walk through a concrete example: a DTC brand that trusted its default model's ROAS estimates, only to discover the model overfit to a noisy holiday period. When the brand ran a simple k-fold cross-validation, it found the model's predictions were 30% less reliable than advertised. The hosts explain how cross-validation works, why it's rarely used in marketing analytics, and how it can prevent budget misallocations. They also touch on the trade-off between model complexity and generalizability, and why a model that looks great on training data can fail in the real world. No math overload — just a practical case for making models robust before betting the marketing budget on them. #CrossValidation #MarketingModels #Attribution #MediaMixModeling #ROAS #DataScience #MarketingAnalytics #FexingoBusiness #BusinessPodcast #DTCBrand #ModelValidation #Overfitting #KfoldValidation #MarketingROI #CampaignMeasurement #DataDrivenMarketing #MarketingOptimization #AnalyticsBestPractices Keep every episode free: buymeacoffee.com/fexingo