Challenge
A small real estate agency was spending over 20 hours weekly manually qualifying and prioritizing leads. Their process involved agents individually reviewing each inquiry, researching property history, and making subjective decisions about lead quality. This led to inconsistent follow-up, missed opportunities, and significant time spent on low-potential inquiries.
Solution
An AI lead scoring system using GPT-4 and Make.com that:
- Automatically analyzed inquiry content to identify key buying signals
- Scored leads based on 15+ data points including inquiry language, property interest, timeframe mentions, and financial indicators
- Routed high-potential leads to senior agents while keeping others in nurture sequences
- Generated personalized initial responses based on the specific inquiry context
- Tracked lead progression through the sales pipeline for continuous improvement
Implementation
The system was built in three phases over 4 weeks:
- Initial data analysis and model training using historical lead data
- Workflow automation design and integration with their existing CRM
- Agent training and gradual rollout with performance monitoring
Results
- 165% increase in viewings arranged with the same team size
- 1.5 FTE saved in administrative work, allowing agents to focus on high-value client interactions
- 39% reduction in cost per acquisition as resources shifted to higher-potential leads
- Unexpected benefit: Improved agent job satisfaction by removing tedious qualification tasks
ROI Timeline
The implementation showed positive returns within 3 weeks and paid for itself completely within 2 months.
