Lead Scoring
Jarvis AI ranks leads by intent so your team works the hottest first.
The problem teams live with
Every lead looks identical in a list. So representatives work the newest one first, or the one with the biggest stated budget, and both are poor predictors. The buyer who replied twice on WhatsApp and asked about possession dates sits at position forty, cooling, while someone chases a tyre-kicker who once typed a big number into a form.
How Sell.Do solves it
Sell.Do scores every lead on intent rather than on what they claimed. The model weighs behaviour that actually precedes a booking — reply latency, site-visit acceptance, budget-to-inventory fit, source quality — and re-scores overnight as the picture changes. Scores drive queues, routing and escalation, so the team spends its finite calling hours on the leads most likely to sign.
Lead Scoring, step by step
- 1AI intent scoring
- 2Custom scoring rules
- 3Recency & engagement weighting
- 4Priority queues for representatives
Leads ranked by modelled booking intent, with the signals behind each score made explicit.
Built for the real workflow
- AI intent scoring
- Custom scoring rules
- Recency & engagement weighting
- Priority queues for representatives
Jarvis shows its working
A score without a reason is a horoscope. Jarvis names the signals behind each number — site visit booked within 26 hours, replied twice, budget matches an available 3BHK — so a representative trusts it, and a manager can argue with it.
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Learn moreFrequently asked questions
No. Every score lists the signals that moved it and by how much. If you disagree with the weighting, the rules are yours to edit — the model proposes, your revenue leadership disposes.
See Lead Scoring on your own pipeline
Book a 30-minute walkthrough tailored to your projects, team and process.