Insight Report
The Static Scoring Leak
Why assumption-based lead scoring drains pipeline, and what the data says about fixing it
Your scoring model was built once, under pressure, and it's probably still running on those same assumptions today.
Most lean marketing ops teams didn’t choose static lead scoring on purpose; they built it once and never revisited it. That decision quietly costs pipeline: 85% of B2B MQLs never convert to SQLs, and stale scoring logic is a primary reason why. This report breaks down what current benchmark data shows, and what to check first.
It also covers the input problem behind most bad scores: when the model only sees the lead and not the company behind it, a perfect-fit account can score the same as one that will never buy.
Only 15% of B2B MQLs convert to SQLs, the industry’s largest pipeline leak, according to 2026 benchmark research.
For Marketing Ops teams running scoring solo
This report is built for you if your team is running lead scoring without a dedicated data function and hasn’t audited the model in a while.
- The real attrition number: See why 85% of MQLs never reach SQL, and why static scoring makes the leak worse.
- The data decay problem: Learn why your model's inputs decay at roughly 22.5% a year, even if the logic never changes.
- The accuracy gap: See the roughly 35% qualification accuracy difference between static rules and learning-based scoring.
- The revenue math: Find out how a 5-point conversion lift can move revenue 12 to 18%.
- A 5-step action plan: Get a practical checklist for auditing, refreshing, and evaluating your current model.