Why Lead Scoring Models Fail - And How to Fix Them

Most lead scoring models fail because of stale, thin data - not the algorithm. B2B data decays at 22.5% per year, silently eroding your model accuracy. This video breaks down how to fix it: better enrichment pipelines, point-in-time correctness, and high-signal features like technographics and normalized seniority. Platforms like Explorium are making this kind of feature engineering faster and more scalable for modern GTM teams.

Chapters:
00:00 Why lead scoring models really fail
00:16 The silent killer: data decay
00:21 2.1% monthly decay — the math
00:36 Why better algorithms won't help
00:41 How engineers are fixing it
00:52 Where Explorium fits in
01:12 Point-in-time correctness
01:33 Highest-lift features

#LeadScoring #FeatureEngineering #B2BData #MachineLearning #GTM #SalesAI #DataEnrichment #RevOps