# Lead Scoring & Routing for B2B SaaS

**Category:** GTM Engineering · RevOps  
**Channels:** Sales Automation  
**Complexity:** High  
**Time to implement:** 4-6 weeks  
**Strategic 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.

> 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.

## 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%.

## 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 nurture
```

## Tools

- HubSpot
- Salesforce
- Clay
- Clearbit
- Apollo
- GA4
- Octave
- MadKudu
- LeanData
- Chili Piper
- Bombora
- G2
- 6sense
- Slack
- Segment
- RudderStack
- Pocus
- Correlated

## Expected metrics

- **MQL to SQL conversion rate:** +20-40%
- **Unqualified handoffs to sales:** -30-50%
- **SDR time on unqualified leads:** -40-60%
- **Top vs bottom tier conversion:** 5-6x higher
- **Time to first contact (whales):** <5 minutes

## Team required

- RevOps Lead
- Growth Manager
- SDR Team

## Prerequisites

- Clear ICP definition documented with sales
- At least 6 months of closed-won and closed-lost CRM data
- Basic tracking hygiene (UTMs and form mapping)
- Sales buy-in on priority signals
- Enrichment in place (see play_008)

## 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

## 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

## Failure patterns

### Behavioral over-indexing
**What happens:** Students who click a lot score high while busy executives score low

**Why:** Behavioral activity is overweighted before fit qualification

**Prevention:** Weight firmographics heavily and require fit threshold before intent dominates

### No decay
**What happens:** Old pricing-page visits keep leads falsely hot

**Why:** Intent signals are treated as permanent

**Prevention:** Apply 50% decay every 7-14 days on intent signals

### Scoring without routing
**What happens:** Scores exist but leads still flow into one queue

**Why:** No action logic tied to thresholds

**Prevention:** Implement routing rules that execute immediately by score and segment

### Validating on closed-won only
**What happens:** Model favors fast-closing churners

**Why:** Short-term conversion proxy is used instead of durable value

**Prevention:** Validate on LTV and retention, not only conversion

### Model set and forget
**What happens:** Signal quality decays over time and trust drops

**Why:** No periodic recalibration

**Prevention:** Run monthly outcome checks and quarterly weight refreshes

## Industry benchmarks

- **MQL to Customer conversion:** 2-5% _(source: Forrester (2024))_
- **Top vs bottom score conversion:** 5-6x higher _(source: MadKudu benchmark (2025))_
- **PQL conversion rate:** 20-30% _(source: OpenView PLG Report (2025))_
- **Predictive vs rules-based lift:** 20-40% improvement _(source: Gartner (2024))_
- **Response time impact:** 5 minutes = 21x higher qualification _(source: InsideSales (2024))_

## FAQ

**Q: What is lead scoring in B2B SaaS?**

Lead scoring assigns value using fit (ICP alignment) and intent (buying behavior). Strong systems keep these separate, then combine with motion-specific weights. Top-tier leads should convert several times better than low-tier leads.

**Q: How do you build a lead scoring model?**

Start with fit criteria (industry, company size, role, stack), then add intent signals with decay (pricing, demo, product actions). Combine scores by motion and validate against at least 6 months of historical outcomes before rollout.

**Q: What is the difference between fit scoring and intent scoring?**

Fit scoring measures who they are and whether they could be a strong customer. Intent scoring measures what they are doing now and how ready they are. Routing and outreach should use both together.

**Q: What are the best lead scoring tools for SaaS?**

For early maturity, CRM-native scoring with enrichment is enough. At higher scale, tools like Octave or MadKudu help predictive scoring, while LeanData or Chili Piper improve routing. PLG teams often add Pocus or Correlated for product signal orchestration.

**Q: What is negative lead scoring?**

Negative scoring subtracts points for disqualifying behavior or attributes such as competitor domains, student profiles, bounces, and long inactivity. It reduces false positives and keeps SDR focus on true opportunities.

**Q: How do you know if lead scoring is working?**

Watch MQL-to-SQL lift, unqualified handoff reduction, and score tier separation. If top-tier leads do not materially outperform low-tier leads, recalibrate model weights and inputs.

**Q: What is a Product Qualified Lead (PQL)?**

A PQL is a lead showing buying potential through product behavior, not just marketing engagement. For PLG motions, these signals are often more predictive and should route faster to sales-assist paths.

**Q: What is predictive lead scoring?**

Predictive scoring uses historical outcomes to estimate conversion likelihood. It can outperform rules-based approaches when data quality and volume are sufficient, but still requires ongoing monitoring and retraining.

**Tags:** RevOps, Lead Scoring, Lead Routing, Intent Data, PQL, Predictive Scoring

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Source: https://mazorda.com/playbooks/ai-powered-lead-scoring-and-routing
Canonical: https://mazorda.com/playbooks/ai-powered-lead-scoring-and-routing
Last updated: 2025-11-03

_From Mazorda — B2B GTM engineering. Explore https://mazorda.com/playbooks for the full library._

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