Leadkaun

EdTech · Delhi

EdTech Lead Management in Delhi.

Delhi-based edtech teams use Leadkaun to grade every lead A–F, build each rep's Priority Queue, and surface missed revenue in rupees. Setup the same day.

Quick answer

How does Leadkaun help edtech teams in Delhi manage leads?

Leadkaun grades every edtech lead in Delhi A–F across fit, intent, and quality in real time, then builds each rep a live Priority Queue so the highest-intent enquiries get worked first. WhatsApp is a first-class 3-tap signal, and missed revenue surfaces in rupees. Setup happens the same day, at flat INR pricing.

Typical deal size

₹15k–₹15L annual fee

Sales cycle

3 days to 120 days (admissions cycle-bound)

Primary channels in Delhi

Facebook Ads · Google Ads · referrals

01Why Delhi edtech teams lose deals

The patterns we see every week.

01

400 enquiries, one counsellor

Admissions counsellors carry 200–500 leads each. With no triage, 30 minutes every morning goes to deciding who to call first, instead of calling.

02

Parent threads drop mid-conversation

Parent says 'discuss with my spouse', disappears, and nobody follows up at the right moment. Most qualified conversations move to WhatsApp, and most CRMs don't see any of it.

03

Admissions cycles are ₹-sensitive

A Grade A lead in April is worthless in September. Counsellors who miss the 5-day follow-up window lose a full year's fee (₹50k–₹5L) per missed enrolment.

02How Leadkaun helps edtech teams in Delhi

What we configure on day one.

  1. 01

    Every enquiry is graded A–F based on course fit, programme match, and engagement signals from both student and parent

  2. 02

    3-tap WhatsApp logging captures every parent reply, stage, intent, outcome, feeding the Intent Score in real time

  3. 03

    Priority Queue surfaces the Grade A parents who replied overnight, so the first call by 11 AM hits the hot leads

  4. 04

    Intent decay respects admissions cycles: a silent Grade A drops to Grade B over a week, so the queue stays fresh

  5. 05

    Morning Brief: '8 Grade A enquiries replied overnight. ₹6L in admissions at risk if not called by 11 AM'

Delhi context

EdTech teams in Delhi, Delhi typically source leads from Facebook Ads, Google Ads, referrals. Capital region, policy-adjacent + large-government buyer base. All of those channels flow into Leadkaun via CSV upload or manual entry in minutes (a Google Sheets connector is on the roadmap), where every enquiry is scored and graded A–F before it reaches a rep.

Commercial activity in Delhi 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 edtech pipeline here most often draws from.

What this looks like in practice

With overnight enquiries pre-ranked by 9 AM, a counsellor's morning starts with the Grade A parents to call first, not thirty minutes spent deciding who to call.

Illustrative scenario

03How the grade works

How every edtech 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 admissions, Fit is programme match and the decision-maker: a parent enquiring for a course you run scores differently from a student browsing three institutes.

04Who signs off

Who decides, on a EdTech lead in Delhi.

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

Is answerable for what the branch did with the enquiry, which makes a written trail worth more than a recollection.

Founder

senior

Wants to know whether this enquiry is worth anyone's afternoon before the team spends one.

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

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.

07FAQ

Questions for edtech teams.

Ready when you are

Your reps open their queue tomorrow.

Setup the same day. Free forever on 1 user and 100 active leads. No card.

Free forever · 1 user · 100 leads · No card