Why Most Lead Scoring Fails
Most lead scoring models fail for the same reason: they're built by marketers who've never carried a quota. The result is a system that optimizes for marketing activity rather than sales outcomes.
At PatientIQ, we took a different approach. Instead of scoring based on what marketing thought mattered, we reverse-engineered our scoring model from closed-won deals.
The Discovery Process
We started by analyzing our last 50 closed deals and asking:
- What job titles were involved in the buying committee?
- What content did they consume before requesting a demo?
- How many people from the same account engaged?
- What was the typical timeline from first touch to closed-won?
The findings surprised us. Traditional "high-value" actions like downloading whitepapers had almost zero correlation with closed deals. But attending a webinar with a colleague from the same account? That was gold.
The Framework
Our lead scoring model had three components:
1. Fit Score (0-50 points)
- Job title match to ICP: 0-20 points
- Company size: 0-10 points
- Industry vertical: 0-10 points
- Technology stack signals: 0-10 points
2. Engagement Score (0-50 points)
- Multi-stakeholder engagement: 0-20 points
- High-intent actions (demo request, pricing page): 0-15 points
- Content engagement depth: 0-10 points
- Recency multiplier: 0.5x to 2x
3. Intent Score (0-30 points)
- 6Sense intent signals: 0-15 points
- G2 category research: 0-10 points
- Competitor research signals: 0-5 points
The Multi-Stakeholder Multiplier
The biggest insight was the power of multi-stakeholder engagement. When two or more people from the same account engaged within a 30-day window, the account score increased by 50%.
This single change transformed our conversion rates. Sales stopped chasing individual leads and started pursuing engaged accounts.
Sales Alignment
The key to adoption was involving sales from day one. We held weekly calibration sessions where sales reps could challenge scores:
- "This lead is scored 85 but they're not decision-makers"
- "This account only scored 45 but they're clearly in a buying cycle"
Each exception became a learning opportunity. We'd investigate why the model missed and adjust accordingly.
The Results
After six months of iteration:
- Marketing-sourced pipeline increased from 30% to 44%
- Sales accepted 73% of MQLs (up from 45%)
- Average deal cycle decreased by 23 days
- Win rates on scored opportunities increased by 18%
Implementation Tips
Start simple. Our initial model had only 8 scoring criteria. We added complexity only when we had data to support it.
Score accounts, not leads. B2B buying is a team sport. Individual lead scores are less meaningful than account-level engagement.
Build in decay. A whitepaper download from 6 months ago isn't relevant. We decayed engagement scores by 10% per month after 90 days.
Make it visible. Sales should see the score and why it's that number. Transparency builds trust in the system.
Common Mistakes to Avoid
Over-weighting content downloads. We initially gave 15 points for any whitepaper download. Turns out, most downloaders were students or competitors.
Ignoring negative signals. Free email addresses, competitor domains, and certain job titles should subtract points.
Static scoring. Your ICP evolves. Revisit your scoring model quarterly.
Want to see the actual scoring rubric we used? [Reach out](/contact) and I'll share the template.