Can multiple agents share one enquiry?

Yes, assignment rules handle round-robin or manual handoffs; broker splits are logged.

Does it track site visit outcomes?

Yes, site visit is a pipeline stage in Leadkaun with booked/no-show/follow-up outcomes.

What if a broker closes the deal outside Leadkaun?

Log it as 'closed-external' with commission tagged; the engine still attributes the originating enquiry.

Is the lead scoring a black-box AI model?

No. The score is three transparent 0–100 components, Fit, Intent and Quality, combined on fixed, published thresholds. The weights are identical for every account, so any rep can see exactly why a lead earned its grade, rather than trusting an opaque AI number.

Can I tune the scoring to my kind of business?

You configure your ICP, the industries, states, business types, decision-maker roles and budget bands that define a good customer, and that drives every Fit score. The underlying weights stay fixed and auditable by design, so the grade remains explainable and comparable across accounts.

Does Leadkaun replace my CRM?

No. Leadkaun runs alongside your CRM. Your CRM stays the system of record for what happened; Leadkaun is the behaviour layer that decides what happens next, grading every lead A–F, ranking each rep's Priority Queue, and surfacing the ₹ at risk. Most teams import a CSV and run both in parallel to measure the difference first.

How long does it take to get started?

You can be set up the same day. A short onboarding wizard captures your ICP industries, states, business types, decision-maker roles and budget bands, then you import a CSV and every lead is graded A–F immediately. No implementation project, no dedicated admin.

How do leads get into Leadkaun?

CSV import or manual entry today, plus a generic webhook. Indian phone formats are normalised and duplicates caught on insert. Native Google Sheets sync and IndiaMART / Facebook Lead Ads connectors are on the roadmap; for now those sources come in via CSV export. Every lead is graded A–F the moment it lands.

Lead Scoring

Lead Scoring for Real Estate Teams in Delhi.

Leadkaun's lead scoring is built for how Delhi-based real estate teams actually sell, Indian phone handling, WhatsApp as a first-class lead signal, ₹ figures in Indian formatting throughout.

In short

Most real estate desks in Delhi run on gut feel and the freshest enquiry. Lead Scoring replaces that with a transparent Grade A–F on every lead, so reps spend their hours on the enquiries most likely to close this week, not just the newest one.

Start free→See pricing →

Quick answer

What is Leadkaun's lead scoring for real estate teams in Delhi?

01What lead scoring does

What it does for real estate teams.

01
Three-dimensional scoring: Fit (ICP), Intent (engagement), Quality (data reliability)
02
Transparent weights. Every rep can see why a lead is Grade A, not a black-box AI score
03
Intent decays automatically when leads go silent, stale grades drop without manager intervention
04
{industry}-specific ICP templates built for Indian market defaults
05
Grades in real time as each lead lands, so the queue is ready before the rep opens it

02On lead scoring

What lead scoring actually solves.

Lead scoring is how a team decides which enquiry to work first when far more arrive than anyone can call. Done well, it replaces gut feel and recency bias with a defensible ranking: the freshest lead isn't automatically the best one, and the lead that's been sitting for a day might be the ₹40-lakh deal about to go cold.

Leadkaun scores every lead on three transparent 0–100 dimensions, Fit against the ICP you configure, Intent from real engagement signals, and Quality from data reliability, then combines them into an A–F grade. The weights are fixed and identical for every account, so it's never a black box: a rep can always see exactly why a lead is Grade A, and intent decays automatically when a lead goes silent so the grade stays honest over time.

Real Estate in Delhi

In Delhi, real estate teams typically work leads from 99acres, MagicBricks, Housing.com, with deal sizes in the ₹5L–₹5Cr GCV range and sales cycles of 2 days to 4 months (enquiry → registration). Leadkaun's lead scoring is calibrated for those realities, not a generic US B2B default.

In Delhi, Delhi, B2B demand concentrates in real estate, bfsi, edtech. Leadkaun grades and queues every enquiry here on fit, intent and quality, so a Delhi rep works the highest-probability leads first.

Capital region, policy-adjacent + large-government buyer base, context Leadkaun's grading accounts for when it ranks a Delhi real estate pipeline.

Commercial activity clusters around Connaught Place, Nehru Place, Okhla, Bhikaji Cama Place, with the local economy built on corporate HQs, IT and electronics markets, and Okhla manufacturing, the areas a Delhi real estate pipeline most often draws from. Leadkaun grades and queues those enquiries so the highest-intent ones surface first, wherever they land.

03How the grade works

How every real estate lead is graded A–F.

No black box. Each grade is three transparent 0–100 scores combined against fixed, auditable thresholds, and stale leads surface as ₹ at risk before they go cold.

Fit

0–100

How closely the lead matches the ICP you set, industry, state, business type, decision-maker role and budget band. This is the part you shape: you configure who a good customer is, not the maths behind it.

Intent

0–100

Engagement and signal events, source strength, WhatsApp replies, pricing-page visits, callbacks. Intent decays as a lead goes silent, so a hot lead that stops responding cools automatically instead of sitting falsely high.

Quality

0–100

Data reliability, completeness, phone and email validity, junk and duplicate detection. If Quality is too low the lead is capped down the grades, so bad data can never masquerade as a good lead.

The threshold

A lead is Grade A when Fit ≥ 65, Intent ≥ 60 and Quality ≥ 60, the rest step down through B–F on the same fixed cut-offs. Because the weights are identical for every account, the grade stays explainable and comparable: no per-customer tuning, no hidden model.

For a real-estate desk, Fit is mostly budget band and locality against your project mix, a ₹2 crore enquiry for a project you don't sell scores low on Fit however keen the buyer sounds.

04Who signs off

Who decides, on a real estate lead in Delhi.

A real estate enquiry in Delhi is rarely decided by one person. Two or three read it, and they are not reading it for the same thing.

Head of Sales

senior

Cares less about the single lead than about whether the team is working the right ones this week.

Broker

mid

Is deciding whether the requirement matches inventory actually available right now.

Sales Manager

mid

Has to defend the call order in Monday's review, so the reason a lead ranks where it does has to survive being questioned.

This is why Fit is scored separately from Intent: how keen someone sounds and whether they are the person you can actually sell to are two different questions. Both are published with their weights.

05What lead scoring touches