Predictive Delivery Analytics
A logistics company handling 2,000+ daily shipments struggled with delivery delays and customer complaints. We built a predictive system that detects issues before they affect customers.
The Challenge
The company had no way to predict which shipments would be delayed. Customer complaints came after the fact, damaging relationships and increasing support costs. Dispatch teams were reactive instead of proactive, and manual tracking across multiple carriers was inefficient.
Our Solution
We built a predictive analytics platform that aggregates data from multiple carrier APIs, weather services, and historical delivery patterns. The system identifies at-risk shipments 4-8 hours before expected delays and automatically notifies affected customers with updated ETAs.
- Real-time data aggregation from 5 carrier APIs
- Machine learning model trained on 2 years of historical delivery data
- Weather and traffic integration for route-level delay prediction
- Automated customer notifications via email and SMS
- Dispatch dashboard with priority alerts and recommended actions
- Weekly performance reports with trend analysis
Results
Customer complaints decreased by 60% within the first quarter. Proactive notifications improved customer satisfaction scores by 25%. The dispatch team now resolves 80% of potential delays before they impact delivery windows.
Tech Stack
Project Details
Timeline: 6 weeks
Cost: $9,000
Industry: Logistics