AI Delivery Time Prediction: End the “Where Is My Parcel?” Call
A customer is waiting for their delivery. They don’t know when it will arrive. They call customer service — who calls the driver — the driver says “about an hour” — customer service calls the customer back. Hundreds of customers repeat this cycle every day.
AI ETA prediction eliminates that entire chain.
How AI Predicts Delivery Time
Inputs for Prediction
Real-time data:
- Vehicle GPS position (updated every 30 seconds)
- Traffic on the route (integration with HERE or Google Maps Traffic API)
- Weather on the route (snow, rain, fog)
Historical data:
- Unloading time at each customer (from previous deliveries)
- Driver’s performance on this specific route
- Seasonal patterns (Monday mornings, Friday afternoons)
ETA Calculation per Stop
Route R-2245 | Vehicle: BA-123AB | Driver: Peter Novak
Remaining route: 8 stops
Stop 4/8 — KOVOSPOL Ltd.:
Current distance: 23.4 km
Traffic: Moderate (N1 slowdown +8 min)
Historical unloading time at customer: 18 min (from 12 previous deliveries)
ETA: 14:32 (±12 min)
SMS sent to customer at 12:30:
"Your delivery will arrive today at 14:30. Track it here: [link]"Customer Live Tracking
The customer clicks the link and sees:
- Map with the vehicle’s current position
- Number of stops before their delivery
- Updated arrival time
ETA is recalculated automatically after every event: traffic jam, longer unloading at a previous stop, route change.
Results for the Transport Company
| Metric | Before AI | After AI |
|---|---|---|
| Inbound “where is my parcel” calls | 40–80/day | 8–15/day |
| On-time delivery rate (within window) | 72 % | 91 % |
| Customer satisfaction (CSAT) | 3.8/5 | 4.5/5 |
| Failed deliveries (customer not home) | 8 % | 3 % |
Customer knows when the driver is coming → they are home → fewer re-deliveries → lower costs.
