Real-Time Platinum Lead Scoring Model
Overview
Our client, a leading real estate services company, wanted a platinum lead scoring model to identify high-conversion-potential leads early and help sales teams focus on the most promising opportunities, improve booking conversion efficiency, enable project-level lead prioritisation and support real-time deployment.
Solution
- Developed the lead scoring model on a dataset of ~16.6 lakh leads with a 1.17% booking rate
- Conducted Exploratory Data Analysis (EDA) that established booking conversion varied significantly across source, geography, budget and lead timing behavior, and project characteristics
- Engineered features and built machine learning models using Logistic Regression, Random Forest, and XGBoost, iterating the framework from a single overall model to separate Direct, Walk-in, and CP models for more granular and accurate conversion prediction
- Implemented dynamic project-level Platinum tagging to maintain stable Platinum allocation across the portfolio and improve real-time usability of model outputs
Impacts Delivered
The final lead scoring model created a focused opportunity pool where a small subset of leads contributed disproportionately towards bookings:
- Direct: ~8–9% Platinum leads capturing ~56% of bookings
- Walk-in: ~20% Platinum leads capturing ~48% of bookings
- CP: ~18–20% Platinum leads capturing ~45%+ bookings
