ML model scoring on CRM Data for Lead scoring optimization.
For a large Real Estate advisory company.
CLIENT & PROBLEM STATEMENT
- Large Real Estate advisory company seeks lead scoring and optimization for sales efficiency on a live data processing basis.
- Need for real-time, automated lead scoring using CRM data via APIs.
APPROACH
- Established API connections with client’s sales CRM (MongoDB) to access lead data in real-time.
- Developed an automated Machine Learning (ML) pipeline for continuous model training on historical lead data.
- Implemented advanced ML algorithms to score incoming leads based on engagement, preferences, and financial indicators.
- Created a scalable, event-driven architecture to process leads through the ML model instantaneously.
- Established bidirectional APIs to send le
- The incremental learning approach allowed the model to continuously improve based on incoming data, ensuring accuracy and relevance in real-time scoring.
SOLUTION & OUTPUT
- Automated ML process adapts lead scoring models for optimal accuracy with new data.
- High-value leads prioritised in real-time via API-driven CRM updates, boosting sales efficiency.
- The model is now processing 3 million historical data points and scoring 100,000 incremental data points per month.
- The system efficiently managed large volumes of data, enabling dynamic and responsive marketing strategies.