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AI & Intelligence

AI Lead Scoring

Jarvis ranks every lead by intent and likelihood to convert, and re-scores overnight as behaviour changes.

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AI Lead ScoringIntent, not guessworkModel settingsModel accuracy87%booking predictionLeads re-scored41overnightHot → booked4.2×vs coldRepresentative time saved6.4hper weekSignals the model useslearned, not hard-codedSite-visit bookedhighReply latencyhighBudget ↔ inventory fitmedSource qualitymedTime of enquirylowScore → outcomelast 12 months30405060708090100% of leads at each score that eventually booked.✦Jarvis — model driftPortal-lead conversion dropped 18% this quarter. The model has re-weighted source quality — review the XYZ Portal spend.

The problem teams live with

Lead scores in most CRMs are a rules sheet somebody wrote in 2019 and nobody has revisited. They reward stated budget, which buyers inflate, and recency, which says nothing. Representatives stop trusting the number and go back to working the list top to bottom.

How Sell.Do solves it

Sell.Do learns which behaviours precede a booking in your projects, and re-scores nightly as behaviour changes. Reply latency, site-visit acceptance, budget-to-inventory fit and source quality carry the weight that your own history says they should. Every score names the signals behind it, so a representative can trust it and a manager can argue with it.

How it works

AI Lead Scoring, step by step

  1. 1Intent-based scoring
  2. 2Custom scoring rules
  3. 3Overnight re-scoring
  4. 4Priority queues for representatives

Booking intent modelled from behaviour, re-scored nightly, with the signals behind every score.

What you get

Built for the real workflow

  • Intent-based scoring
  • Custom scoring rules
  • Overnight re-scoring
  • Priority queues for representatives
✦ Jarvis AI

Jarvis explains the number, or it is worthless

Booked a site visit within 26 hours, replied twice on WhatsApp, budget matches an available three-bedroom. Comparable leads booked 71% of the time. That is why she is an 89.

87%
Booking-prediction accuracy
Nightly
Re-scored as behaviour shifts
Explained
Signals, not a black box

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Frequently asked questions

No. It begins from real-estate defaults learned across the platform and adapts to your projects and cycle as your bookings accumulate. It is useful on day one and better in month three.

See AI Lead Scoring on your own pipeline

Book a 30-minute walkthrough tailored to your projects, team and process.

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