BANT scoring that knows the difference between a Friday root canal and a Tuesday browser
Budget, Authority, Need, Timeline — scored per location with the specific signals that actually predict whether the lead converts in your vertical.
The problem
You run 80 dental locations and generate around 4,200 inbound leads a week across Google Ads, organic search, Yelp, referrals, and paid social. Salesforce auto-scores them on a 1 to 100 scale using generic rules — and it scores 'patient needs a root canal, wants Friday' the same as 'patient is browsing the services page.' Your SDR team wastes about 11 hours a week on the misqualified ones, and high-intent leads sit cold while the SDR is on a tire-kicker. The fundamental problem is that the BANT framework — Budget, Authority, Need, Timeline — needs real signal, and the standard tools do not pull it. HubSpot, Marketo, Pardot, and Drift ship rules engines and generic scoring. 6sense and Demandbase ship account-based intent for B2B. None of them know your insurance-carrier data, your appointment-urgency text patterns, or which of your locations the lead is asking about. The default outcome is a quarterly pipeline review that surfaces lost-deal patterns three months too late.
What success looks like
Every lead is scored on BANT with signals that actually matter in your vertical. Budget comes from insurance carrier, payment method, financing inquiry, and employer size. Authority comes from the decision-maker title, household role, and signer status. Need comes from inquiry urgency, symptom severity, and repair timeline. Timeline comes from appointment urgency, project start date, and contract renewal date. The score is weighted by the LTV cohort that lead matches — a high-LTV-cohort lead with a high BANT score routes differently from a low-LTV-cohort lead with the same score. Per-location attribution adjusts the weighting (paid-search leads score differently from organic-referral leads in the same market). HIPAA, FDA, and FINRA rules apply automatically — no PHI flows into the score, no claim restrictions get violated, no consent gets skipped. Multi-banner operators see one consolidated BANT view. Every score is preserved with a timestamp, the signals, the output, and the routing decision — so a revenue-ops post-mortem can answer why a lead got routed the way it did.
How most operators solve this today
Five categories of tools touch lead qualification today. None of them score BANT with vertical-specific signals at multi-location scale:
CRM platforms with lead-scoring modules (Salesforce Sales Cloud, HubSpot CRM, Pipedrive, Zoho CRM, Microsoft Dynamics 365 Sales, Freshsales)
$14 to $5,000+ per user per month, plus scoring add-ons
Generic rules engines. They do not know your insurance carriers, your appointment urgency text patterns, or your per-location LTV cohorts.
Marketing automation platforms (Marketo Engage, Pardot, Eloqua, ActiveCampaign, Customer.io, Klaviyo)
$15 to $15,000+ per month, plus enterprise tiers
Generic scoring and grading. Not vertical-aware, not per-location attribution-weighted.
AI lead-scoring tools (Drift, Mutiny, Madison Logic, 6sense, Demandbase, Bombora)
$2,500 to $300,000+ per year enterprise tiers
Strong on B2B account-based intent. Not built for multi-location consumer or patient leads.
In-house revenue ops with manual scoring
$80,000 to $150,000 per year per role, plus four to twelve weeks per scoring model
Manual scoring rules in CRM custom fields plus spreadsheets. Falls behind the moment volume scales.
Build it in-house
Engineering plus data-science work, plus ongoing maintenance
The signal definitions, the LTV-cohort weighting, and the per-state compliance overlays all have to stay current as your service mix and channel mix shift.
What changes when this is an agent skill
Every lead is scored on BANT with vertical-specific signals. Budget comes from the insurance carrier, payment method, financing inquiry, and employer size. Authority comes from the decision-maker title, household role, and signer status. Need comes from inquiry urgency, symptom severity, and repair timeline. Timeline comes from appointment urgency, project start date, and contract renewal date. The score is weighted by the LTV cohort that lead matches — so a high-LTV-cohort lead with a high BANT score gets your best SDR attention, while a low-LTV-cohort lead with the same numeric score takes a lower-priority route. Per-location attribution adjusts the weighting based on how leads from each market typically convert. HIPAA, FDA, and FINRA rules apply automatically — no PHI flows into the scoring features, no claim restrictions get violated. Multi-banner operators see one consolidated BANT view. Every score is preserved with a timestamp, the signal inputs, the output, the routing destination, and the compliance attestation.
How this compares
salesforce.com
Generic rules engines. They do not know your insurance carriers, your appointment urgency text patterns, or your per-location LTV cohorts.
hubspot.com
Generic rules engines. They do not know your insurance carriers, your appointment urgency text patterns, or your per-location LTV cohorts.
marketo.com
Generic scoring and grading. Not vertical-aware, not per-location attribution-weighted.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Lead Scoring + Routing Agent
Enriches, scores, qualifies, and routes inbound leads from forms, chat, email, phone, and ad-clicks within 30 seconds.
Related skills
Customer LTV per location, per channel, per brand — at runtime
LTV cut by location, channel, brand, and cohort — historical and predictive — exposed as a real-time signal your save-flow, your ads, and your loyalty program can all act on.
Customer cohorts every AI agent reads at runtime, not weekly
Cohorts like "high-LTV", "at-risk", and "lapsed" computed on your unified customer record and exposed as a real-time signal — so your save-flow, your email, your ads, and your loyalty program all act on the same up-to-date answer.
One customer record across every device, channel, and location
The same customer on desktop Tuesday, in-app Wednesday, in-store Friday, and on the phone Saturday gets unified into one record — so cohort, LTV, and decisioning all see one person, not five.
One attribution model for the whole brand misses what each market is actually doing
Per-location attribution that reflects how each market actually buys — Google Ads in Denver, walk-in foot traffic in Austin, direct mail in Tampa — joined to the same customer across every touchpoint.
Marketing compliance for regulated multi-location operators — every output checked before it ships
Every AI-generated post, page, ad, and email checked against the regulatory rules that apply to your verticals and your states — before it goes live, not after.
One master record per location — reconciled across every source, propagated everywhere automatically
One trusted record per location, reconciled across POS, Google Business Profile, Yext, HR, and your CMS — and when anything changes, every marketing surface updates automatically.
Audit trail for regulated multi-location operators — evidence packages that build themselves
Every change to every location record, every shipped marketing artifact, every applicable rule — versioned, linked, queryable at any point in time, retained for the window your regulator requires.
FAQ
- What does BANT scoring actually score?
- Each lead on Budget, Authority, Need, and Timeline, using vertical-specific signals — insurance carrier, payment method, financing inquiry, decision-maker role, inquiry urgency, symptom severity, appointment urgency, contract-renewal date. The score is then weighted by the LTV cohort that lead matches and the per-location attribution patterns for that market.
- How is this different from Salesforce, HubSpot, Pipedrive, Zoho, Microsoft Dynamics 365, or Freshsales?
- Those are generic rules engines. They will score 'wants Friday root canal' the same as 'browsing the services page' unless you write the rules yourself. This is built around the signals that actually predict conversion in your vertical.
- How is this different from Marketo, Pardot, Eloqua, ActiveCampaign, Customer.io, or Klaviyo?
- Those are generic marketing-automation scoring. Not vertical-aware, not per-location attribution-weighted, not LTV-cohort-weighted.
- How is this different from 6sense, Demandbase, Drift, Mutiny, Madison Logic, or Bombora?
- Those are strong on B2B account-based intent. This is built for multi-location consumer and patient leads — which is a different signal set.
- What signals get scored?
- Budget: insurance carrier, payment method, financing inquiry, employer size. Authority: decision-maker title, household role, signer status. Need: inquiry urgency, symptom severity, repair timeline. Timeline: appointment urgency, project start date, contract renewal date.
- How does the LTV cohort weighting work?
- A lead with a high BANT score whose profile matches your high-LTV cohort routes to your best SDR. A lead with the same BANT score whose profile matches a low-LTV cohort takes a lower-priority route. The score is the same number; the routing reflects the difference.
- How does per-location attribution affect the score?
- Paid-search leads in Denver convert differently from organic-referral leads in Phoenix. The score adjusts for the conversion pattern of leads from that source in that market.
- Can a revenue-ops post-mortem trace why a lead got the score and route it got?
- Yes. Every score is preserved with a timestamp, the signal inputs, the output, the routing destination, and the compliance attestation.