Automated Lead Classification for Real Estate Platform

Project background 

Overview

A growing real estate platform was struggling with efficiently managing and prioritizing incoming leads. Agents often had to manually review inquiries, leading to delays in follow-ups and lost opportunities. The client wanted an AI-driven solution to automatically classify and prioritize leads based on customer intent, ensuring that high-value prospects were promptly addressed.

To tackle this, we developed an automated lead classification system that seamlessly integrates with the platform’s CRM. By leveraging machine learning and natural language processing (NLP), the system identifies the lead type, determines purchase intent, and assigns inquiries to the appropriate sales representatives.

Project Goals

• Automate lead classification to reduce manual effort and improve response time.
• Identify lead intent and urgency using machine learning models.
• Assign high-priority leads to senior agents for immediate follow-up.
• Integrate the classification system with the platform’s CRM.

Challenges

• Handling unstructured lead inquiries coming from multiple sources (contact forms, emails, phone transcripts).
• The need for high classification accuracy across various lead types and customer intents.
• Integrating AI-driven classification into an existing CRM without disrupting operations.
• Optimizing response times while processing large volumes of incoming leads.

Our approach

Solution

To address these challenges, we developed a machine learning pipeline that classifies leads based on their content and urgency. Whenever a lead is submitted via the platform, an event is triggered to analyze its details—such as property type, location, budget, and specific requirements. The system then classifies the lead as buying, renting, selling, or general inquiry, while also assigning an intent score from 0 to 10 (0 being exploratory, 10 being highly motivated).

We fine-tuned NLP models (BERT / DistilBERT) to extract key information from free-text inquiries. Using API integrations, the system automatically updates the CRM, routing high-priority leads to senior agents and sending automated follow-ups for lower-priority leads. The result is an intelligent lead management system that improves efficiency and sales performance.

Team

Our team consisted of an ML engineer who developed and trained the classification model, a backend developer responsible for API integration with the CRM, and a project manager for smooth execution and alignment with business goals.

Results

The implementation of the automated lead classification system significantly improved operational efficiency for the real estate platform. Response times were reduced by 40%, ensuring faster engagement with high-priority leads. The AI-driven classification improved lead conversion rates by 25% by guaranteeing prompt follow-ups with the most motivated buyers and sellers. Additionally, agent workload decreased by 50% as manual lead processing was minimized.