AI vehicle recommendation lifted lead conversion and personalization across digital sales channels.
- Vehicle lead conversion
- Increased
- Personalization
- Improved
- Dealership lead quality
- Improved
What we shipped
A recommendation engine that analyzes browsing behavior, prior ownership, price sensitivity, and regional demand to surface relevant vehicles.
Automotive manufacturers and dealerships struggle to deliver personalized experiences across digital channels. Customers researching vehicles online typically receive generic recommendations that do not reflect their preferences, driving behavior, or budget.
Blueprint → AI Pilot → Production launch → Scale and operate.
We followed the Datablooz Delivery Model. See our process.
- Blueprint
Inventoried customer data sources and defined segmentation and recommendation objectives.
- AI Pilot
Built collaborative filtering and behavioral models on historical data, benchmarked against rule-based baselines.
- Production launch
Integrated recommendations into online configurators and dealership lead systems with experimentation infrastructure.
- Scale and operate
Expanded to retention programs, tuned models by region, and monitored lift against a generic baseline.
Business, technical, and governance outcomes.
- Higher vehicle lead conversion rates.
- Improved personalization across digital sales.
- Higher engagement on manufacturer sites.
- Better dealership lead quality.
- Python
- Scikit-learn
- TensorFlow
- Spark
- PostgreSQL
- Airflow
Segmentation rules reviewed by marketing, experiment governance on recommendation changes, and audit trails on feature usage.
Working on something similar?
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Reference calls available under NDA after the second working session.