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Qualify before you chase.
Six signals, weighted. Get a 0–100 lead score, a conversion probability, a priority tier and a recommended follow-up cadence — so your Tuesday goes to the right person.
The lead
Used for expected revenue
Lead score
62
Warm · B
Worth working — but confirm budget before you invest a demo.
Signal breakdown
The results above are free and ungated, always. An account just saves them.
Budget is your weak point
Budget exists but is not allocated. This is the single most common reason a well-fitting lead stalls for six months.
Recommended follow-up
Book a 20-minute qualification call. Do not demo until budget is confirmed.
All figures are illustrative estimates generated on-device for general guidance — not forecasts, financial advice, or guarantees. Nothing you enter is transmitted or stored unless you choose to save a report. Quotarider and XDQ Labs Private Limited make no representation as to accuracy for any individual circumstance.
One lead is a judgement call. A hundred is a system.
Quotarider scores every inbound lead automatically, ranks them by expected revenue, and tells you which campaign is producing the good ones.
Lead scoring, honestly
Most lead scoring models are built by marketing to justify the volume of leads they generate. They award points for opening emails and visiting the pricing page — activities that correlate with mild curiosity and almost nothing else.
A useful score answers a different question: if I spend the next three hours on this person, what is the probability I get paid?
Authority is the signal everyone underweights
A perfectly-fitting lead with no buying authority is a research project. They'll take your calls, attend your demo, ask excellent questions, and then disappear when they discover that the person who controls the budget has never heard of you and doesn't care.
This is why authority carries the highest weight in the model above. Fit determines whether they should buy. Authority determines whether they can.
Timeline separates a pipeline from a wish list
"Just researching" is an honest answer and you should thank them for it, then stop working the lead. There is no version of the next six weeks in which that person signs a contract. Nurture them, keep them warm, and go spend your Tuesday on someone with a deadline.
The nuance: a timeline driven by a real business event (a contract expiring, a system being decommissioned, a compliance deadline) is worth ten times one they invented to be polite when you asked.
Pain is the accelerant
Budget gets found for painful problems. It never gets found for mild inefficiencies, no matter how compelling your ROI deck. When a lead scores high on pain and low on budget, that's not a disqualification — it's an instruction to build the business case with them. When they score low on both, walk away.
The MQL/SQL argument
Every company with a marketing team eventually has this fight. Marketing says they delivered 400 MQLs; sales says 380 of them were worthless. Both are correct, because they're measuring different things: marketing scores engagement, sales scores buyability.
The fix is agreeing on a single score that weights both, and setting the handoff threshold jointly. A lead scoring 75+ on the model above is genuinely sales-ready. Below 50 it belongs in nurture, and passing it to a rep just burns the rep's time and the marketing team's credibility.
Scoring at scale
Doing this by hand for one lead is a useful discipline. Doing it for four hundred is impossible, which is why most teams give up and work whatever came in most recently.
Quotarider's AI Lead Quality Tracker scores every lead as it arrives, ranks by expected revenue rather than recency, and — critically — feeds the close data back so you learn which campaigns actually produce buyers rather than form fills.
Frequently asked
Is this just BANT with extra steps?
It's BANT plus two signals BANT ignores. Budget, Authority, Need and Timeline are all here. So are ICP fit — because a qualified lead in the wrong segment is still a bad customer — and engagement, which tells you whether they came to you or you dragged them here. The difference is that this produces a rankable number rather than a pass/fail.
Where should I set my MQL threshold?
Most teams land between 55 and 65. Set it too low and reps drown in unqualified leads and stop trusting marketing. Set it too high and you starve the pipeline. The right answer is empirical: score your last fifty closed-won deals retroactively, find the score below which almost none of them sat, and put your threshold just under it.
Should I ever work a low-scoring lead?
Sometimes — but deliberately, not by default. A lead scoring 35 with a $500K deal value may have a higher expected revenue than one scoring 70 at $10K. That's why the calculator shows expected revenue as well as score. Rank on expected revenue; use the score to decide how much effort each one deserves.
Does this store my lead data?
No. Runs entirely in your browser. Nothing transmitted, nothing stored.