AI That Knows Which Parents Will Enroll

Our machine learning model analyzes engagement patterns — form completeness, response speed, WhatsApp opens, and call outcomes — to score every lead. Counsellors focus on hot prospects first.

Key Takeaways

  • AI scores every admission enquiry 0-100 based on real engagement data, not guesswork
  • Counsellors focus on the top 20% of leads most likely to convert, saving 60% of their time
  • Transparent score breakdowns show exactly why each lead is rated hot, warm, or cold
  • Configurable scoring rules let you adjust weights based on what matters most at your school

Sound Familiar?

  • Counsellors spend equal time on hot and cold leads: AI surfaces the top 20% of leads that are most likely to convert
  • No way to predict which enquiries will enroll: ML model predicts enrollment likelihood with 85%+ accuracy
  • Manual prioritization based on gut feeling: Data-driven scoring based on 15+ engagement signals

Complete AI Lead Scoring Features

Everything you need to modernize your school's ai lead scoring.

  • ML Scoring Engine: Analyzes 15+ engagement signals including form completeness, response time, WhatsApp opens, email clicks, and call outcomes.
  • Score Breakdown: Transparent score card showing exactly which factors contribute to a lead's score — no black box.
  • Custom Weights: Adjust scoring weights based on your school's priorities — make campus visits worth more, or prioritize response speed.
  • Score Decay: Scores automatically decrease for leads that go inactive. Keeps your pipeline honest and prioritized.
  • Hot Lead Alerts: Instant notifications when a lead's score crosses your threshold. Never miss a high-intent parent.
  • Scoring Analytics: Track average scores by source, grade, and month. See which channels bring the highest-quality leads.
  • Auto-Prioritization: Counsellor dashboards automatically sort leads by score. Hot leads always appear at the top.
  • Scoring Rules Config: Define custom scoring actions: campus visit = +20 points, WhatsApp reply = +10, no response in 7 days = -15.

Get Started in 4 Simple Steps

  1. Define Scoring Rules: Configure point values for each engagement action — form fill, WhatsApp reply, campus visit, etc.
  2. AI Scores Automatically: Every lead gets a real-time 0-100 score based on their engagement across all touchpoints.
  3. Counsellors Act on Insights: Hot leads surface to the top. Counsellors see exactly why a lead scored high with the breakdown tooltip.
  4. Optimize Over Time: Review scoring analytics to refine weights and improve prediction accuracy each admission cycle.

Frequently Asked Questions

What signals does the AI use to score leads?

The ML model considers 15+ signals: form field completeness, response time to first contact, WhatsApp message opens, email clicks, campus visit booking, application progress, fee payment intent, and more.

Can I customize the scoring weights?

Yes! You can adjust how many points each action is worth. If campus visits are most predictive for your school, increase their weight. The config panel makes it easy.

How accurate is the scoring?

Schools typically see 80-90% accuracy in predicting which leads will enroll after 2-3 admission cycles of data. The model improves with each cycle.

What happens when a lead goes inactive?

Score decay kicks in. If a lead doesn't engage for a configurable period (default: 14 days), their score gradually decreases, keeping your pipeline accurately prioritized.

Do counsellors need training to use lead scoring?

No. Scores appear as a simple 0-100 badge next to each lead. A color-coded system (red/yellow/green) makes it instantly obvious. The score breakdown tooltip provides details on hover.