Why Lead Scoring Fails (And How to Fix It)
Most lead scoring models fail for one of two reasons: they're built without sales input, or they're based on arbitrary assumptions rather than real conversion data. Marketing assigns points based on gut feel, hands "hot" leads to sales, and wonders why reps ignore the score. The fix is building a model collaboratively and grounding it in what actually predicts a closed deal.
Step 1: Define What a "Good Lead" Actually Looks Like
Before assigning a single point, align with sales on the Ideal Customer Profile (ICP). Pull your last several dozen closed-won deals and identify the common characteristics:
- What industries or verticals convert best?
- What company sizes close fastest and at the highest value?
- What job titles or buying roles are most commonly involved?
- What were the first behavioral signals before those deals closed?
This analysis gives you evidence-backed criteria rather than assumptions. If you don't have enough historical data, start with a hypothesis model and commit to revising it after your first full quarter.
Step 2: Separate Fit Scoring from Engagement Scoring
Combine two independent dimensions into your final score:
- Fit Score (Firmographic/Demographic): How well does this lead match your ICP? Score based on company size, industry, geography, job title, and technology usage. This doesn't change based on behavior — it reflects whether they're worth pursuing at all.
- Engagement Score (Behavioral): How actively is this lead interacting with your brand? Score based on website visits, email clicks, content downloads, webinar attendance, and product trials.
A high-fit, high-engagement lead is your priority. A high-fit, low-engagement lead is worth nurturing. A low-fit, high-engagement lead should stay in marketing — they're interested but not the right profile for sales to pursue yet.
Step 3: Assign Point Values Based on Conversion Correlation
Weight each criteria according to how strongly it correlates with closed revenue. Sample scoring framework:
| Criteria | Points |
|---|---|
| Industry matches ICP | +15 |
| Company size matches ICP | +10 |
| Decision-maker title | +20 |
| Visited pricing page | +25 |
| Attended live webinar | +20 |
| Downloaded a case study | +10 |
| Opened 3+ emails in 30 days | +10 |
| No activity in 60+ days | -15 |
| Personal email domain | -10 |
Step 4: Set a Sales-Ready Threshold Collaboratively
Agree with sales on the score at which a lead becomes "Sales Qualified" and triggers an outreach task. Too low and reps get flooded with unready leads. Too high and opportunities stall in marketing. Test your threshold, gather feedback from sales after the first month, and adjust accordingly.
Step 5: Build in Score Decay
Engagement that happened six months ago shouldn't carry the same weight as engagement last week. Configure score decay so that behavioral points reduce over time when there's no fresh activity. This keeps your hot lead list reflective of current intent, not historical interest.
Making Scoring a Living System
A lead scoring model isn't something you build once and forget. Review it quarterly. Compare scored leads to actual conversion outcomes. If high-scoring leads aren't converting, your criteria need adjustment. If sales is finding value in leads marketing considered low-priority, investigate what signals you're missing. The best models improve continuously with every data point your pipeline generates.