🎯
Business Objectives
- ›Standardize taxonomy
- ›Power analytics by vehicle type
⚙️
Operational KPIs
- ›Coverage %
- ›Mismatch rate
📈
Management KPIs
- ›Profitability by vehicle class
🤝
Customer KPIs
- ›Accurate descriptors on POD
🗄️
Available Data
- ›Make, model, year, body style
✨
AI Use Cases
- ›VIN decoding
- ›Auto-classify uncategorized vehicles
🔮
Predictive Analytics
- ›Demand by vehicle class
🚨
Anomaly Detection
- ›Spelling drift / duplicates
🎁
Recommendation Engines
- ›Canonical taxonomy
🤖
AI Copilot Features
- ›'Normalize all RAM 1500 variants'
💬
Natural-Language Reporting
- ›'Margin per body style'
📊
Executive Dashboard Metrics
- ›Mix and margin by class
⚡
Workflow Automation
- ›Auto-clean on ingest
🛰️
Autonomous Agents
- ›Data Stewardship Agent
🖥️
Recommended Dashboards
- ›Catalog Health
💡
Example Executive Insights
- ›7 duplicate make/model rows inflating reports by ~3%
📦
Required Datasets
- ›vehicle_makes_models
- ›vehicles
📅
Historical Data Needed
All-time
🧠
ML Models / AI Approaches
- ›String similarity + LLM normalization