Lead Scoring & Routing for B2B SaaS
Most lead scoring is theatre. Sales ignores the scores because they do not trust them. This playbook builds a system that separates fit (who they are) from intent (what they are doing), validates against LTV, and routes leads with context. Top scoring leads convert at 5-6x the rate of bottom scoring leads.
Goal: Build a lead scoring and routing system that produces trustworthy scores, separates fit from intent, and routes leads to the right owner with context so sales trusts the system and marketing gets honest feedback.
Complexity
High
Tools
18
Context
The Problem
What breaks:
- Scores nobody trusts; hot leads turn out unqualified while true buyers are missed
- Fit and intent conflated in one score
- Scoring without routing so no action follows
- No validation against LTV
Why it matters:
Bad lead scoring wastes SDR time on unqualified leads and creates a feedback loop where nobody trusts the system. Strong scoring reduces unqualified handoffs by 30-50% and increases MQL to SQL conversion by 20-40%.
Resolution
The Solution
Level 1: Quick Wins (Week 1-2)
- Separate fit from intent into two CRM fields
- Add negative scoring for competitors, students, bounced emails
- Set whale alerts for 1,000+ employee pricing visitors
- Exclude closed-won customers from scoring
- Document 5-10 ICP criteria
Level 2: Full System
- Build a Fit Score (100 points) using firmographic and role signals
- Build an Intent Score (100 points with decay) using behavioral and product signals
- Combine by GTM motion: Inbound 40/60, Outbound 70/30, PLG 30/70, ABM 60/40
- Route by company size and score with whale bypass logic
IF Company > 1,000 employees AND Score > 70:
-> WHALE: Immediate Slack to AE, skip SDR queue
ELSE IF Company > 500 employees:
-> Enterprise AE (by geography)
ELSE IF Company 50-500:
-> Mid-Market SDR (by vertical)
ELSE IF Company < 50 AND Intent > 60:
-> Self-serve sequence
ELSE:
-> Marketing nurtureExpected Metrics
+20-40%
MQL to SQL conversion rate
-30-50%
Unqualified handoffs to sales
-40-60%
SDR time on unqualified leads
5-6x higher
Top vs bottom tier conversion
<5 minutes
Time to first contact (whales)
Routing Logic Example
| Fit | Intent | Action | Owner | SLA |
|---|---|---|---|---|
| A-fit | High | Route to SDR | SDR | <5 min |
| A-fit | Low | Nurture + alert on spikes | Marketing Ops | 24 hrs |
| B-fit | High | Route to AE | AE | 24 hrs |
| Bad fit | Any | Auto-DQ with reason | RevOps | Instant |
No Scoring vs Basic CRM Scoring vs Mazorda Approach
Fit vs Intent
Traditional
Not separated
Our Approach
Explicitly separated
Signal sources
Traditional
CRM plus email only
Our Approach
CRM + web + product + third-party intent
Routing
Traditional
Basic or first-come routing
Our Approach
Territory + score + whale detection
Validation
Traditional
Closed-won only or none
Our Approach
LTV-based validation
Feedback loop
Traditional
None
Our Approach
Monthly review + quarterly refresh
Trust level
Traditional
Low
Our Approach
High, validated against revenue outcomes
| Aspect | Traditional | Our Approach |
|---|---|---|
| Fit vs Intent | Not separated | Explicitly separated |
| Signal sources | CRM plus email only | CRM + web + product + third-party intent |
| Routing | Basic or first-come routing | Territory + score + whale detection |
| Validation | Closed-won only or none | LTV-based validation |
| Feedback loop | None | Monthly review + quarterly refresh |
| Trust level | Low | High, validated against revenue outcomes |
Industry Benchmarks
| Metric | Benchmark | Source |
|---|---|---|
| MQL to Customer conversion | 2-5% | Forrester (2024) |
| Top vs bottom score conversion | 5-6x higher | MadKudu benchmark (2025) |
| PQL conversion rate | 20-30% | OpenView PLG Report (2025) |
| Predictive vs rules-based lift | 20-40% improvement | Gartner (2024) |
| Response time impact | 5 minutes = 21x higher qualification | InsideSales (2024) |
Team Responsibilities
| Role | Responsibility |
|---|---|
| RevOps Lead | Model design, CRM configuration, validation |
| Growth Manager | Intent signal definition, feedback loops |
| SDR Manager | Routing rules, sales feedback collection |
Failure Patterns
| Pattern | What Happens | Why | Prevention |
|---|---|---|---|
| Behavioral over-indexing | Students who click a lot score high while busy executives score low | Behavioral activity is overweighted before fit qualification | Weight firmographics heavily and require fit threshold before intent dominates |
| No decay | Old pricing-page visits keep leads falsely hot | Intent signals are treated as permanent | Apply 50% decay every 7-14 days on intent signals |
| Scoring without routing | Scores exist but leads still flow into one queue | No action logic tied to thresholds | Implement routing rules that execute immediately by score and segment |
| Validating on closed-won only | Model favors fast-closing churners | Short-term conversion proxy is used instead of durable value | Validate on LTV and retention, not only conversion |
| Model set and forget | Signal quality decays over time and trust drops | No periodic recalibration | Run monthly outcome checks and quarterly weight refreshes |
ICP Fit Notes
Best fit
- •Series A-C B2B SaaS with 100+ leads per month
- •Companies with SDR team and defined sales process
- •Teams frustrated that sales ignores MQL scores
Also works for
- •PLG companies adding a sales-assist layer
- •High-ACV enterprise teams with long sales cycles
- •Companies with rich product usage data
Insight: If sales ignores your lead scores, the issue is usually weak prediction quality. Validate against LTV, not just closed-won volume.
Implementation Checklist
Week 1: Discovery
- Document ICP criteria with sales
- Audit CRM data completeness
- Pull 6+ months closed-won/lost data
- Identify available intent signals
Week 2: Model Design
- Define fit scoring criteria (100 points)
- Define intent signals with decay
- Set weights by GTM motion
- Design routing logic with whale detection
Week 3: Build
- Configure scoring in CRM/tool
- Set up enrichment triggers
- Build routing rules
- Create Slack alerts for whales
Week 4: Validate
- Score historical leads
- Compare predicted vs actual outcomes
- Adjust weights based on backtest
- Get sales feedback
Ongoing
- Monthly: review score-to-outcome correlation
- Quarterly: re-validate model and adjust weights
- Add or remove signals based on performance
FAQ
Sources
- 1. Mazorda operator archive (40+ years combined): patterns from systems we built, fixed, and retired across B2B SaaS GTM.
- 2. Forrester Research (2024) - B2B Lead Qualification Benchmarks
- 3. MadKudu (2025) - Lead Scoring Performance Data
- 4. OpenView (2025) - Product-Led Growth Benchmarks
- 5. Gartner (2024) - Predictive Analytics in B2B Marketing
- 6. InsideSales (2024) - Speed to Lead Research
When NOT to Use
- •<50 leads/month - Not enough volume to validate; use manual qualification
- •No clear ICP - If you cannot define good fit, you cannot score it
- •Sales ignores CRM - Scores only help if surfaced where sales works
- •No conversion data - You need closed-won and closed-lost history
- •Very early stage - ICP is still changing; do not over-engineer
Tools & Tech