Why Lead Scoring Models Fail Without Intent Data

Episode notes

In this episode, Lucas and Luna dive into a pervasive marketing operations problem: lead scoring models that look good in theory but fail to predict real pipeline. They use a specific case study of a B2B SaaS company that saw a 40% increase in SQL-to-opportunity conversion after layering third-party intent signals onto its traditional fit-and-behavior scoring. The discussion covers the difference between explicit and implicit intent, why most CRM fields are noise, and how to set up a simple intent-weighted model without buying expensive platforms. Listeners walk away with one concrete metric - intent score correlation to deal velocity - that they can test in their own instance this quarter. #LeadScoring #IntentData #B2BMarketing #MarketingAutomation #DemandGeneration #SalesDevelopment #PipelineManagement #ABM #MarTech #PredictiveAnalytics #MarketingOperations #ConversationIntelligence #RevOps #DataQuality #CRM #Business #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

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