SaaSSalesLead Scoring

ICP Lead Scoring + Prioritisation Agent - Sales Queue Automation

Every new lead or MQL entering the sales funnel is instantly scored against your custom ICP model (15-point rubric: company size, industry, growth signals, tech stack, intent indicators, budget signals). High-fit leads (80+) auto-assign to available reps with a briefing note highlighting why they're a strong fit. Medium-fit leads queue for nurture or assignment; low-fit leads archive or route to outbound prospecting team.

AI operates via

CRM SyncEmail Automation

Deploy This Template

How It Works

01
ICP Model Setup & Calibration
Sales/marketing define your ideal customer profile: company size (£5M–50M ARR), industries (Tech, Financial Services), employee count (50–500), growth signals (funding, new hire patterns), tech stack (must use Salesforce + Hubspot + Slack), budget threshold (>£50K ACV). Assign weights to each criterion.
02
Real-Time Lead Data Enrichment
Inbound lead arrives (form, import, API). AI auto-enriches with: company size (ZoomInfo), industry classification (Crunchbase), tech stack (Clearbit, Hunter.io), funding status (PitchBook), employee growth (LinkedIn). If any data missing, AI estimates or flags for manual review.
03
Automated Scoring Against ICP
AI evaluates lead on each ICP criterion, assigns points (0–100 scale). Example: Company size £15M ARR (20pts), Tech industry (15pts), 180 employees (18pts), Series B funded (12pts), uses Salesforce + Hubspot (10pts), visited pricing page (15pts) = 90/100 ICP fit score.
04
Intelligent Rep Assignment & Briefing
High-fit leads (80+) auto-assign to next available rep (or specific AE if you've configured territory rules). AI generates 3-sentence briefing: 'Sarah, this lead is a strong fit: £12M ARR Tech startup, 200 employees, Series B, already using Salesforce/Hubspot. Likely budget >£50K. High intent: visited pricing 3x, demo page, opened email.' Assigned rep is notified.
05
Queue Prioritisation & Visibility
Sales funnel auto-sorts by ICP score. Reps see 'High Fit (80+)' leads at top of queue, then 'Medium Fit (60–79)', then 'Low Fit (<60)'. Dashboard shows: conversion probability per lead, recommended next action, and which leads are ready for demo vs. still-qualifying.

See How It Works

The AI Employee executes each step autonomously - no human intervention required unless explicitly configured.

ICP Model Setup & Calibration

Real-Time Lead Data Enrichment

Automated Scoring Against ICP

Intelligent Rep Assignment & Briefing

Queue Prioritisation & Visibility

Step 01

ICP Model Setup & Calibration

Sales/marketing define your ideal customer profile: company size (£5M–50M ARR), industries (Tech, Financial Services), employee count (50–500), growth signals (funding, new hire patterns), tech stack (must use Salesforce + Hubspot + Slack), budget threshold (>£50K ACV). Assign weights to each criterion.

Step 02

Real-Time Lead Data Enrichment

Inbound lead arrives (form, import, API). AI auto-enriches with: company size (ZoomInfo), industry classification (Crunchbase), tech stack (Clearbit, Hunter.io), funding status (PitchBook), employee growth (LinkedIn). If any data missing, AI estimates or flags for manual review.

Step 03

Automated Scoring Against ICP

AI evaluates lead on each ICP criterion, assigns points (0–100 scale). Example: Company size £15M ARR (20pts), Tech industry (15pts), 180 employees (18pts), Series B funded (12pts), uses Salesforce + Hubspot (10pts), visited pricing page (15pts) = 90/100 ICP fit score.

Step 04

Intelligent Rep Assignment & Briefing

High-fit leads (80+) auto-assign to next available rep (or specific AE if you've configured territory rules). AI generates 3-sentence briefing: 'Sarah, this lead is a strong fit: £12M ARR Tech startup, 200 employees, Series B, already using Salesforce/Hubspot. Likely budget >£50K. High intent: visited pricing 3x, demo page, opened email.' Assigned rep is notified.

Step 05

Queue Prioritisation & Visibility

Sales funnel auto-sorts by ICP score. Reps see 'High Fit (80+)' leads at top of queue, then 'Medium Fit (60–79)', then 'Low Fit (<60)'. Dashboard shows: conversion probability per lead, recommended next action, and which leads are ready for demo vs. still-qualifying.

Key Results

–67%

Sales Time Spent on Wrong-Fit Leads

32%

High-Fit Lead Conversion Rate

+41 points

Sales Queue Avg. Lead Quality (ICP Score)

Frequently Asked Questions

Can the AI's scoring be biased if historical data is skewed?

Possible, which is why regular calibration is critical. You review the model quarterly: 'Do high-scoring leads actually convert at higher rates?' If not, weights are adjusted. AI learns from rep feedback (marked deals won/lost) over time.

What if our best customer came in as a 'low-fit' score?

Good catch-it means your ICP definition needs updating. If a low-fit lead converts to a high-value customer, it's feedback to adjust the model weights or add new criteria (e.g., 'Industry exceptions: SaaS startups always high-fit regardless of size').

How granular can assignment rules get?

Very. You can configure: 'Route Tech leads >100 employees to Sarah; Fintech leads to David; <50 employees to nurture team.' Geography, industry, company size, intent signals can all inform assignment.

Does this penalise small startups or early-stage prospects?

Not if those are part of your ICP. You define ICP; AI applies it fairly. If startups are in scope, include 'Seed/Series A funding' in the model.

What percentage of inbound leads typically score as high-fit?

Typically 15–25% of raw inbound (forms, ads, etc.) will be high-fit, 30–40% medium-fit, remainder low-fit. The exact mix depends on your marketing targeting and ICP specificity.