Sales · Lead Management & Nurturing

AI Intent-Based Lead Scoring Engine

78Building evidence

Generate Revenue

“I want to stop guessing which leads to call today and instead have a system that watches every signal — page visits, email opens, LinkedIn activity — and automatically tells me who is hot right now.”

At a glance

Cost

$500–2500 /mo

Time saved

2-3 hours per rep per day previously spent deciding who to call; reps focus 100% on leads with proven intent signals.

Difficulty

High

Steps

1 AI · 5 automation

What you end up with

A daily Slack-delivered hot list of the top 5 leads per rep, each with a composite intent score, 'why now' context brief, and a suggested conversation opener — all updated dynamically based on real-time behavioral signals.

The pipeline · 6 steps

STEP 1

6sense

Intent signal aggregator — identifies which accounts are in an active buying cycle based on anonymous web behavior, third-party content consumption, and keyword research patterns across the entire web.

Automation

STEP 2

Clay

Contact discovery layer — maps company-level intent signals down to the specific individuals who should be contacted, preventing reps from reaching the wrong person at a hot account.

Automation

STEP 3

HubSpot

Scoring computation and storage — maintains the running composite score, updates it with each new signal event, and serves as the single source of truth for lead prioritization.

Automation

STEP 4

Claude

Context narration engine — translates raw signal data into a human-readable reason-to-call that the rep can use as their conversation opener, eliminating the 'I saw your score went up but don't know why' problem.

AI decides

STEP 5

HubSpot

Distribution and visibility — ensures reps see their hot list at the start of each day in both their workflow tool (Slack) and their CRM, maximizing the chance they act on the signal.

Automation

STEP 6

HubSpot

Feedback and optimization loop — captures outcome data that allows the scoring model to self-improve over time, increasing the precision of the hot list each month.

Automation

AI decides a model generates or judges hereAutomation runs mechanically, no judgment

How it runsamber = the model makes a judgment call

STEP 1

Automation

6sense executes this mechanically. No judgment call — it runs the same way every time.

STEP 2

Contact discovery layer — maps company-level intent signals down to the specific individuals who should be contacted, preventing reps from reaching the wrong person at a hot account. · Clay

Automation

Clay executes this mechanically. No judgment call — it runs the same way every time.

STEP 3

Scoring computation and storage — maintains the running composite score, updates it with each new signal event, and serves as the single source of truth for lead prioritization. · HubSpot

Automation

HubSpot executes this mechanically. No judgment call — it runs the same way every time.

STEP 4

AI decides

The model generates and decides here — output quality depends on the prompt and context you give Claude.

STEP 5

Distribution and visibility — ensures reps see their hot list at the start of each day in both their workflow tool (Slack) and their CRM, maximizing the chance they act on the signal. · HubSpot

Automation

HubSpot executes this mechanically. No judgment call — it runs the same way every time.

STEP 6

Feedback and optimization loop — captures outcome data that allows the scoring model to self-improve over time, increasing the precision of the hot list each month. · HubSpot

Automation

HubSpot executes this mechanically. No judgment call — it runs the same way every time.

The honest readpros and trade-offs, side by side

What works

Real-time rescoring means reps always have current signal data, not a score from 3 months ago

6sense identifies anonymous website visitors and maps them to known accounts — captures intent you can't see in your CRM

Daily Slack hot list creates a forcing function for rep action at optimal contact moments

Claude's 'why now' briefs give reps confidence and context for cold/warm outreach

Feedback loop makes the model smarter every month without manual reconfiguration

The trade-offs

6sense and Bombora are expensive ($2,000+/month) — this workflow has a high minimum viable budget

Intent data has false positives — a competitor researching your company will look like a hot lead

Requires consistent HubSpot hygiene (contact ownership, activity logging) or scores become unreliable

Where people go wrong

The failure modes builders hit most often. Avoiding these is most of the battle.

01Setting the score threshold too low — a hot list with 50 leads is useless; cap at 10 per rep per day

02Not including a recency decay factor — leads scored hot 2 weeks ago and never contacted should drop off the list

03Treating 6sense company-level intent as contact-level readiness — always enrich to find the right person before outreach

04Skipping the feedback loop step — without outcome tracking, the model never improves and loses rep trust within 60 days

Built for

Revenue Operations ManagerSales Development RepresentativeVP of SalesMarketing Operations Manager

Industries

SaaSEnterprise SoftwareB2B TechnologyFinancial ServicesProfessional Services

Use cases

Enterprise SaaS SDR teams prioritizing outbound prospecting based on live buying signalsRevOps managers replacing static lead scoring with dynamic behavioral modelsSales teams targeting accounts researching competitors — intercept them at the right momentABM programs identifying which target accounts just entered an active buying cycleVP of Sales wanting data-driven daily rep task prioritization without micromanaging

FAQ

how do I know which leads are most ready to buy right now

WHY NOW: TechCorp (Marcus Chen, VP Sales) has been researching 'sales automation' and 'Outreach.io alternatives' on third-party sites for 8 days — they're in the Decision stage on 6sense — and Marcus opened your last email 3 times yesterday without responding. SUGGESTED OPENER: '

how do I use intent data to prioritize sales outreach

Most CRMs have static lead scores that go stale within days. Reps waste 40-60% of their prospecting time on leads who have gone cold while ignoring leads whose behavior shows they are actively researching solutions right now.

how do I build a real-time lead scoring model with behavioral data

Workflows, proven by builders

AI Intent-Based Lead Scoring Engine