Lead Scoring for Agents

AI-powered lead qualification system to help agents focus on high-intent buyers.

Published February 20, 2024

1 min read

Technologies Used

Python

HubSpot API

Scikit-learn

React

AWS Lambda

CRM dashboard showing ranked leads with AI-generated scores

Focus on the Closers, Not the Browsers

In a competitive real estate market, speed to lead is critical. However, agents often spend hours chasing unresponsive prospects. We built an AI Lead Scoring system that analyzes a lead's entire digital footprint—website visits, email opens, property preferences—to assign a dynamic 'Intent Score'. This allows agents to prioritize their day and focus 100% of their energy on buyers ready to transact.

The Challenge

Agents overwhelmed by a high volume of unverified internet leads.

Low conversion rates due to delayed follow-up on hot leads.

Lack of insight into what specific properties a lead is interested in.

Manual data entry errors in the CRM.

The Solution

Aggregated data from website, email marketing, and third-party portals.

Trained a predictive model to identify behaviors correlated with closing.

Automated the scoring process within the existing HubSpot CRM.

Set up instant Slack notifications for 'Hot Lead' alerts.

Key Features

Dynamic Scoring

Scores update in real-time based on new interactions.

Website activity
Email engagement
Property saves

Automated Routing

Route the best leads to top-performing agents instantly.

Round-robin assignment
Skill-based routing
Availability check

Behavioral Insights

Give agents a 'cheat sheet' of the lead's interests before they call.

Favorite neighborhoods
Price range analysis
Time on site

Technology Stack

Python

HubSpot API

Scikit-learn

React

AWS Lambda

Project Timeline & Results

Week 1-3

Data Integration

Unified lead data from 5 different sources.

Connected the HubSpot API, IDX website feed, and email marketing platform. Cleaned and normalized the data to create a single 'Lead Profile' view.

Week 4-6

Model Training

Identified key signals that predict a sale with 80% accuracy.

Analyzed historical closed deals to find common patterns. Discovered that 'viewing 3+ properties in same zip code' was a high-intent signal. Tuned the scoring algorithm weights.

Week 7-8

CRM Deployment

Rolled out to 50+ agents with zero downtime.

Updated the CRM interface to display the new Score field. Created automated workflows to trigger tasks for high-score leads. Conducted training sessions for the sales team.