🎯
Business Objectives
- ›Increase quote-to-order conversion
- ›Optimize pricing per lane / customer
- ›Speed quote turnaround
⚙️
Operational KPIs
- ›Avg time-to-quote
- ›Quote volume
📈
Management KPIs
- ›Win rate
- ›Avg margin on won quotes
🤝
Customer KPIs
- ›Quote turnaround time
🗄️
Available Data
- ›Quote line items, prices, win/loss, response time
✨
AI Use Cases
- ›Dynamic pricing
- ›Win probability scoring
- ›Auto-draft quotes
🔮
Predictive Analytics
- ›Probability of win at given price
- ›Optimal price point
🚨
Anomaly Detection
- ›Quotes priced far from market
🎁
Recommendation Engines
- ›Suggested price band
- ›Bundle suggestions
🤖
AI Copilot Features
- ›'Quote a 2024 RAM 1500 LAX→PHX next Tuesday'
💬
Natural-Language Reporting
- ›'Win rate by lane this quarter'
📊
Executive Dashboard Metrics
- ›Conversion %, avg margin, pipeline value
⚡
Workflow Automation
- ›Auto-draft quote from inquiry
🛰️
Autonomous Agents
- ›Quoting Agent issues + follows up on quotes
🖥️
Recommended Dashboards
- ›Pricing Intelligence
- ›Conversion Funnel
💡
Example Executive Insights
- ›Price elasticity model suggests +3.4% on Enterprise PHX lane without conversion loss
📦
Required Datasets
- ›quotes
- ›customers
- ›work_orders
📅
Historical Data Needed
18–24 months
🧠
ML Models / AI Approaches
- ›GBM win-rate
- ›Elasticity / pricing models
- ›LLM for drafting