AI-Predicted Delivery Times: No More Guessing

One of the most common frustrations with online shopping in West Africa is delivery uncertainty. 'It will arrive today' can mean anything from two hours to never. At Shadar, we decided to solve this with AI rather than hope.

When you accept a vendor's offer, our Delivery Prediction engine calculates a confidence-scored ETA. Not a vague range, not a best-case fantasy - a realistic prediction based on multiple data points.

The first input is vendor prep time. Every vendor has a historical profile of how long it takes them to package and hand off an order. A vendor who consistently ships within 20 minutes gets a different prep estimate than one who averages an hour. The system learns each vendor's actual behavior.

Next comes distance and routing. We calculate the real distance between vendor and buyer, accounting for delivery via Yango or Uber. This isn't straight-line distance - it factors in the actual road network and typical travel times for the route.

Time-of-day adjustment is crucial in cities like Accra and Lagos, where traffic patterns vary dramatically. A delivery estimate at 10 AM is very different from one at 5 PM during rush hour. Our system has learned these patterns from historical delivery data and adjusts predictions accordingly.

Finally, each prediction receives a confidence score: high, medium, or low. A well-known vendor with a short, traffic-free route at a quiet time of day gets a high-confidence prediction. A new vendor during rush hour across town gets a lower confidence score - and we communicate this transparently.

The result is that buyers can make informed decisions. When comparing two offers, delivery time and confidence become real factors - not afterthoughts. And as we accumulate more delivery data across our cities, the predictions keep getting sharper.