Back to Principal Investigator at AI/Hub, Durham College
Smarter Car-Buyer Targeting
Delivered a machine-learning model that identifies online shoppers most likely to purchase a vehicle in the near term.
Machine LearningAutomotivePython
Project Details
Sales teams were investing valuable time in leads that rarely converted, so by analyzing historic CRM and web-analytics data we produced a propensity model that highlights genuine purchase intent. Qualified lead volume rose 22 percent and average deal closure occurred five days sooner, with sales managers providing feedback that informed ongoing tuning. The solution used pandas for feature engineering, XGBoost deployed behind a FastAPI microservice, and a Tableau dashboard for transparency.
Key Achievements
- Enabled the dealership group to reach quarterly sales targets nearly two weeks early.
- Solution was adopted by seven affiliate locations following the pilot.