Fit scoring
Weighs industry, company size, location and service need against the profile you actually want.
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Automation & CRMAI lead scoring that tells your team who to call first
Combine behavior, source, fit and engagement into a clear score — so hot leads get a fast human touch and low-fit leads get nurtured automatically.
Fit + intent signals
Explainable scores
Routes to the right rep
Illustrative example — a ranked queue
- Tier A Fit 90 · Intent 84 — owns the asset, budget confirmed, asked for a call window.
- Tier B Fit 70 · Intent 55 — right profile, still comparing options, replied once.
- Tier C Fit 35 · Intent 20 — outside target segment, form only, no reply yet.
★★★★★ 4.9/5 rating
1,200+ reviews
8+ years
500+ clients
The problem it solves
A queue where every lead looks the same
When leads land in one undifferentiated list, reps work it top to bottom instead of by likelihood. The result is a team that is busy, not effective — and a high-intent buyer sitting behind a tire-kicker.
- Reps call in order of arrival, not order of intent.
- Form data alone misses the behavior that signals a real buyer.
- “Qualified” is judged differently by every person on the team.
- A spreadsheet score goes stale the day the rules change.
- Genuine opportunities go cold while low-fit enquiries are answered first.
Illustrative example — before scoring
- Mon 9:00 60 new leads in one list.
- Mon 9:05 Rep starts at the top and calls the first form fill.
- Mon 11:40 Still working low-fit leads; no clear rank.
- Tue 08:00 A high-intent lead who viewed pricing twice has not been called.
What the scoring layer does
One number, with the reasons next to it
Scoring is configured to your business and grounded in the signals you approve — never a black box you cannot explain to a rep.
Intent scoring
Reads pages viewed, pricing visits, form answers, replies and message language for buying signals.
Engagement scoring
Tracks how often and how recently a lead has responded, opened or booked.
Dynamic re-scoring
The score moves as new behavior arrives, so a quiet lead that re-engages rises automatically.
Hot-lead alerts
Notifies a rep the moment a lead crosses your hot threshold — on the channel they watch.
Routing & queue order
Sorts the queue by score and sends each lead to the right person or team first.
Score explanation
Every score carries its top reasons, so reps trust the rank instead of ignoring it.
CRM-native field
The score lives on the record inside your existing CRM, not in a separate side tool.
Low-fit handling
Weak leads are routed to nurture instead of a rep’s calendar, so selling time stays protected.
Mockup
What a prioritized list looks like
An illustrative view of how scored tiers read — your thresholds and labels are your own.
Score 88
Owns the property, said the timeline is “this month,” and asked for pricing twice.
- Fit: target segment, right size
- Intent: pricing page + reply
- Routed to the senior rep
Score 61
Right kind of buyer, still comparing options, replied once three days ago.
- Fit: strong, timing unclear
- Intent: one form, no calls
- Entered a nurture sequence
Score 29
Outside the target segment and only submitted a form with no further activity.
- Fit: low for core services
- Intent: no engagement yet
- Kept warm, not queued
Illustrative example Score values and tier rules shown here are samples only — your weights and thresholds are set with you, and are not a claim about any result.
Human in control
What stays human
Scoring decides what to work on first. It does not decide who is worth treating well, and it never replaces your judgement on the edge cases.
- The weighting: which signals count and how much.
- Segment definitions and what “good” means for each service line.
- Manual overrides when a rep knows better than the model.
- Any disqualification that carries policy or fairness weight.
- The interpretation of the dashboards built on top of the score.
We keep signals business-relevant and avoid sensitive personal attributes; the numbers rank effort, not people.
Illustrative example — an override
- 09:02 Lead scores Tier C on rules alone.
- 09:03 A rep recognises a referral partner’s client.
- 09:04 Rep overrides the tier and pins the lead to the top.
- 09:05 The override is logged so the rule can be refined later.
Where it sits
How it fits the full system
AI Lead Scoring is the prioritization layer inside steps 4 and 5 — it ranks the leads qualification captures, and it tells sales and follow-up what to touch next.
1
Traffic
Ads, search and social.
2
Capture
Forms, calls and messages.
3
AI Response
Instant first reply.
4
Qualification
Questions and signals.
5
CRM
Score on the record.
6
Follow-Up
Nurture the low-fit.
7
Appointment
Booked and reminded.
8
Sales
Rep works the top first.
9
Reactivation
Wake dormant leads.
10
Analytics
What produced revenue.
Sibling services
- AI Qualification Agent — asks the questions the score then weighs.
- Sales Pipeline Automation — keeps ranked deals moving through stages.
- CRM Automation — stores the score on a clean, deduplicated record.
Where it works best
- Course Creators — separate buyers from browsers at scale.
- Realtors — rank ready-to-move buyers above researchers.
- Solar — flag homeowners who already own and have budgeted.
Related workflows
- Lead qualification bot — capture the answers a score depends on.
- Sales pipeline hygiene — act on the ranked list consistently.
Integrations
Scoring inside the tools you already run
The score is designed to live where your team works. Common connections include HubSpot, Pipedrive and GoHighLevel for the CRM record; GA4 and PostHog for behavioral signals; and OpenAI or Anthropic for the language layer that reads message intent — by native integration where one exists, and via API or webhook where it does not.
Where a direct connection is missing, an automation layer such as n8n, Zapier or Make usually bridges it. If your stack is not a fit, we will say so honestly before you commit.
HubSpot
Pipedrive
GoHighLevel
GA4
PostHog
n8n / Zapier / Make
OpenAI / Anthropic
FAQ
AI lead scoring questions
How is the lead score built?
From signals you approve — who the lead is (fit), what they have done on your site and in your messages (intent), and how they have engaged so far (engagement). Each signal has a weight, so the score is explainable, not a black box.
Can we tune the scoring rules?
Yes. You decide which signals matter, how much they count and what counts as a hot lead for each segment. Weights are yours to change as you learn what actually converts.
Is the score AI or plain rules?
Usually a blend. Deterministic rules handle clear signals, and an AI layer reads natural-language clues such as message text and intent. You can keep it fully rule-based if you prefer.
Where does the score live?
In a score field on the CRM record, alongside the reasons behind it, so reps see the rank and the why inside the tool they already use — HubSpot, Pipedrive, GoHighLevel and others via API or webhook.
Could scoring be unfair or biased?
Scores should rank effort, not judge people. We keep signals business-relevant, avoid sensitive personal attributes, and let you review and override any rule so decisions stay with your team.
How long does setup take?
It depends on scope. A focused rollout — agree signals, set weights, connect the CRM, test against recent leads — is usually staged rather than a long project. We confirm dates after the audit.
Get started
See your leads ranked by real buying signals
Share a few details and we will show how scoring would order your current pipeline — and which leads your team should call first.
The fastest way in: answer six short questions and we will map your lead journey, find the biggest leak and show the fastest automation win — before we ever get on a call.
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