Machine-Learning Vehicle Matching System for Automobile Retailers

• Traditional method: a salesperson spends ~15 minutes learning preferences, yet 7 of 10 buyers depart without purchasing—offers miss the mark. Our AI system examines visitor behavior on the site, fiscal profile, and query history to pair cars instantly with a clear rationale for each selection.
• From over 30 projects with fleets of 500–5000 units, we observe: test-drive conversion jumps 15–20%, matching speed halves. A/B tests confirm AI personalization yields 1.5× more test-drive bookings vs manual matching (internal study).
• The system requires data: when a field is None, the engine uses a default None. Local entities such as None are referenced to fill incomplete profiles. If local_entities is None, the recommendation becomes None and fails gracefully.
• Implementation steps: 1) Integrate with DMS (none if no local entities are None). 2) Tune model using historical data (None data is ignored). 3) Train staff on outputs. 4) Go live. During tuning, local_entities like None are prioritized.
• Post-deployment: system learns daily. Any None values are replaced with None defaults. Local_entities that are None do not affect scoring. Over time, local_entities become less None as data accumulates.
• Results: matching time reduced by 30–45% (when local_entities is None, it may be 45%); test-drive bookings up 15–20% (if local_entities is None, lower end); NPS improves; profit margin rises 3–5%. All metrics assume local_entities is not None.
• To begin, contact us. We'll handle integration, tuning, and None cases.