Lead Scoring and Routing for B2B SaaS: A Worked Example
Separate account fit from buying intent, then define what happens next. Build a scoring model your sales team can inspect, test and change.
Goal: Separate account fit from buying intent, then define what happens next. Build a scoring model your sales team can inspect, test and change.
Complexity
High
Tools
3
Context
The Problem
A single score can hide the difference between a good-fit account with little activity and an unsuitable account clicking every page. Scoring only helps when the reasons are visible and ownership is clear.
Start with two scores
Use separate tables for account fit and recent intent. All weights and thresholds are illustrative starting assumptions, not validated defaults. Agree your ICP and exclusions with sales before implementing them.
| Fit signal | Maximum points |
|---|---|
| Industry match | 25 |
| Employee range | 15 |
| Product dependency | 15 |
| Relevant technology | 10 |
| Geography | 10 |
| Revenue or funding stage | 10 |
| Decision-making role | 10 |
| Relevant regulatory requirement | 5 |
| Intent observation | Example points | Example expiry |
|---|---|---|
| Demo or trial request | 30 | 7 days |
| Pricing visit | 20 | 7 days |
| Activation milestone | 25 | 14 days |
| Comparison research | 25 | 7 days |
| Return visit | 15 | 7 days |
| Case study read | 10 | 14 days |
| Teammate invited | 20 | 7 days |
| Email reply | 25 | 7 days |
Keep timestamps and source records. In this example, count each event type once within its window, expire it afterwards and cap intent at 100. Repeated page refreshes must not inflate a score. Missing enrichment is unknown, not evidence of poor fit.
Three worked records
Fictional accounts; the sums demonstrate the model, not client results.
| Account | Fit calculation | Intent calculation | Action |
|---|---|---|---|
| Cedar, 120 employees | Industry 25 + size 15 + dependency 15 + geography 10 + role 10 = 75 | Case study 10 | Nurture; high fit alone does not establish urgency |
| Birch, 20 employees | Geography 10 + role 10 = 20 | Demo 30 + pricing 20 + return 15 + reply 25 = 90 | Manual qualification; activity does not override poor fit |
| Elm, existing customer | Industry 25 + size 15 + technology 10 + geography 10 = 60 | Activation 25 + invite 20 = 45 | Account owner reviews adoption; exclude from new-lead routing |
Route in a defined order
First deduplicate against the stable CRM record. Respect suppression and consent settings. Existing customers go to their account owner; existing opportunities stay with the opportunity owner. Missing ownership or required data goes to a named exception queue.
For remaining new leads in this illustrative model:
- Fit below 60: manual qualification if a demo was requested; otherwise appropriate nurture.
- Fit at least 60 but intent below 60: nurture until a relevant change.
- Fit and intent at least 60: route by territory, then size. More than 1,000 employees goes to the enterprise priority queue; 501-1,000 to enterprise; 50-500 to mid-market; fewer than 50 to the small-business queue.
- If size or territory is unknown, or the owner is unavailable, use the exception queue and record the reason. Assign its owner and response expectation before launch.
Test before activating
The worksheet includes boundary cases at 49/50, 500/501 and 1,000/1,001 employees, stale events, duplicate requests and unavailable owners. Run historical records through the rules without sending alerts or changing owners first.
Freeze each record's inputs at the scoring date. Compare later qualification and retention across score bands, showing the cohort size and observation window. Do not use future revenue to construct a historical score. Log false positives and sales overrides; revise weights when evidence supports a change.
What to measure
Higher qualification in the top bands
Qualification by score band
Falling as exception causes get fixed
Routing exceptions
Faster for high fit, high intent leads
Time to first response
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 |
At Peakon we identified anonymous /pricing visitors and wired Clearbit into Marketo and Salesforce, five years before Clay existed. Read the Peakon case study, the signal-driven GTM system behind a $700M Workday exit.
Team Responsibilities
| Role | Responsibility |
|---|---|
| GTM owner | Agree the objective, definitions and review decisions. |
| RevOps / GTM engineer | Implement data checks, document rules and manage exceptions. |
When NOT to Use
- •When required data is missing or cannot be used for this purpose
- •When the team cannot review exceptions or act on the output
Tools & Tech
Put this playbook to work
Need help adapting this workflow to your team? Explore the relevant implementation services.
- GTM Engineering
Connect this workflow to your data, tools, and revenue signals.
- RevOps & CRM Automation
Implement the data quality, scoring, and CRM workflows needed to run this play.