Why Your Attribution Model Needs a Data Feed Quality Audit

Lucas and Luna dig into a surprisingly common marketing analytics failure: attribution models that look sophisticated but are built on flawed data feeds. They use a real example from a mid-market DTC brand that spent six months optimizing toward a channel that, according to its own CRM, had never generated a single qualified lead. The episode walks through what a data feed quality audit actually looks like — field-level completeness checks, deduplication logic, timestamp alignment — and why most marketing teams skip it. Listeners learn a concrete protocol: start with the raw event stream before the model, not the model output. The hosts also touch on how this connects to the broader problem of garbage-in-garbage-out in marketing analytics, especially as teams adopt multi-touch models without verifying the underlying data pipeline. No abstract theory — just a practical, actionable walkthrough of one audit that saved a brand over $200,000 annually in misattributed spend. #MarketingAnalytics #AttributionModeling #DataQuality #FeedAudit #MarketingAnalyticsWithFexingo #DTCBrands #MarketingROI #DataPipeline #GarbageInGarbageOut #CampaignMeasurement #AdSpendOptimization #CRMAnalytics #MultiTouchAttribution #MarketingTechnology #BusinessPodcast #FexingoBusiness #Marketing #DataDrivenMarketing Keep every episode free: buymeacoffee.com/fexingo