1 · Problem
Every lead is treated the same, so the best ones wait behind the weakest.
Fields & signals: arrival time, response order, conversion history
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Workflow · QualificationScore every lead the same way, every time
When every lead is treated equally, the best ones wait and the weakest ones take the most time. This workflow scores each lead on fit and intent, sorts them into Hot, Warm and Cold, and routes the right ones to a human — with the reasoning always shown.
Fit + intent scoring
Hot, Warm, Cold
Human routing
★★★★★ 4.9/5 rating
1,200+ reviews
8+ years
500+ clients
The problem it solves
Treating every lead the same wastes your best people
If a ready-to-buy lead and a three-years-away enquiry look identical in the inbox, the team works in arrival order and the strongest leads wait. Consistent scoring puts attention where it belongs.
- High-intent leads waiting behind low-intent ones.
- Reps spending equal time on leads that will never convert.
- No shared definition of what “qualified” actually means.
- Decisions about priority made by gut feel, differently every day.
Illustrative example — the ready buyer who waited
- 09:10 A ready-to-buy lead and a browsing lead arrive together
- 09:10 Both look the same, so both wait in the queue
- 11:00 The team works the browsing lead first
- 11:20 The ready buyer, unanswered, books elsewhere
The nine-stage workflow
How Lead Qualification Bot runs, stage by stage
Nine stages from a new lead to a tiered, routed record with its reasoning attached.
1
Problem
Every lead is treated the same, so the best ones wait behind the weakest.
2
Trigger
A new lead arrives from any channel, form, call, chat or DM.
3
AI Agent
The agent asks qualification questions and gathers visible signals.
4
Decision / Qualification
Fit and intent are scored and thresholds assign Hot, Warm or Cold.
5
CRM Update
The score, tier and the reasons behind them are written to the lead.
6
Message / Call
Hot leads route to a human now; Warm and Cold follow their own paths.
7
Next Step
Hot leads are booked or assigned; Warm leads enter a nurturing sequence.
8
Follow-Up
Leads are re-scored as new signals arrive, so the tier can improve.
9
Reporting
Tier distribution, conversion by tier and override rate are reported.
2 · Trigger
A new lead arrives from any channel, form, call, chat or DM.
Fields & signals: source, channel, form data, campaign
3 · AI Agent
The agent asks qualification questions and gathers visible signals.
Fields & signals: questions, answers, timing, service match
4 · Decision / Qualification
Fit and intent are scored and thresholds assign Hot, Warm or Cold.
Fields & signals: fit score, intent score, tier, threshold rule
5 · CRM Update
The score, tier and the reasons behind them are written to the lead.
Fields & signals: score, tier, reasons, owner, next action
6 · Message / Call
Hot leads route to a human now; Warm and Cold follow their own paths.
Fields & signals: routing rule, handoff flag, owner, sequence
7 · Next Step
Hot leads are booked or assigned; Warm leads enter a nurturing sequence.
Fields & signals: booking, assignment, nurture entry, due date
8 · Follow-Up
Leads are re-scored as new signals arrive, so the tier can improve.
Fields & signals: new signals, re-score, tier change, history
9 · Reporting
Tier distribution, conversion by tier and override rate are reported.
Fields & signals: tier mix, conversion by tier, override rate
What it looks like in practice
The score that shows its work
A tier is only useful if people trust it — so every score comes with its reasoning, and it can be overridden.
The scoring model blends two families of signal: fit (does this match what you sell, where you sell it, and at what budget) and intent (how soon and how seriously the lead is engaging). Neither alone tells the whole story.
- Fit signals: service match, location, budget range, business type.
- Intent signals: timing, urgency, question depth, engagement speed.
- The tier is always shown alongside the reasons, so a rep can agree or override.
- Overrides and outcomes feed back, and the thresholds are tuned with you.
New lead scored: Hot.
Fit — strong: service matches your core offer, in your service area, budget range aligned.
Intent — strong: wants to start within two weeks, asked about availability twice, replied within minutes.
Routed to Dana with the summary attached. If you disagree with the tier, one click re-scores it and I’ll learn from the change.
Agreed, this is a good one. Keeping it Hot.
Illustrative example
Illustrative example. Signal weights, thresholds and tiers are configured with you, and we make no fixed accuracy claims — the model is tuned with your team’s feedback.
Same workflow, different industries
How it flexes for three very different businesses
The nine stages stay the same. The signals that matter and the thresholds change completely.
Job type and urgency
Fit is about service area and job type; intent is about urgency and whether they are ready to book. A burst pipe scores differently from a quote request for next year.
Company and role signals
Fit uses company size, industry and budget indicators; intent uses timeline and engagement depth, so a decision-maker with a live project rises above a student researching.
Pre-approval and timing
Fit uses financing readiness and area; intent uses viewing requests and urgency, so a pre-approved buyer ready to view is scored far above a curious browser.
Failure modes & safeguards
What happens when things are not tidy
Scoring is a judgement aid, not a verdict. These guardrails are built in before go-live.
No accuracy claims
We do not claim a fixed accuracy percentage. The model is tuned with your feedback and its performance is judged on real conversion, not a marketing number.
Fully explainable
Every score shows the signals behind it, so a rep can see why a lead was tiered as it was — and override it when they know better.
Humans stay in charge
A low score never means a lead is discarded. Cold leads still receive a light, respectful touch, and any override is recorded and fed back.
Bias-aware signals
Signals are chosen to reflect genuine fit and intent rather than incidental characteristics, and the model is reviewed with you as it is tuned.
Metrics to watch
What we measure for you
We report on outcomes from your own data. We do not publish invented benchmarks — these are the numbers we track and explain with you.
Tier distribution
The share of leads landing in each tier, which shows whether the thresholds match your market.
Conversion by tier
How each tier actually converts, the truest test of whether the scoring is useful.
Hot-lead response time
How fast Hot leads reach a human, since speed is where scoring pays off.
Override rate
How often reps change a tier, which guides where the model needs tuning.
Where it fits
Connect it to the rest of your system
Related workflows
- AI Sales Qualification — the conversational layer.
- Speed-to-Lead Response — acts on the score instantly.
- Sales Pipeline Hygiene — keeps qualified deals moving.
Services this uses
- AI Lead Scoring — the scoring engine.
- AI Qualification Agent — asks the questions.
- CRM Automation — writes scores and routes.
Where it works best
- Home Services — separating urgent jobs from browsers.
- B2B Services — mixed-quality inbound.
- Real Estate — ready buyers versus curious browsers.
FAQ
Lead Qualification Bot questions
How accurate is the scoring?
We do not claim a fixed accuracy figure. The model is tuned with your team’s feedback over time, and every score is shown with its reasoning so a person can always override it.
What signals does it use?
Two families: fit signals such as service match, location and budget range, and intent signals such as timing, urgency and how the lead engages. The weighting is set with you.
Can we change the thresholds?
Yes. Hot, Warm and Cold boundaries are configured with you and adjusted as you learn what converts. Nothing about the model is fixed in stone.
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