A delivery fails, the driver calls, the customer gives the real landmark (“it’s the house with the green gate, not the corner one”), and the package still arrives, an hour late. For the business, that order closed out fine: delivered, customer happy, case closed. The problem shows up on that same customer’s next order, when it fails at the exact same spot, because nobody saved the correction a driver already made once.
The fix happens on the street, not in the system
The correction exists. Someone made it, on the spot, over a phone call with the carrier. What does not exist is the step that carries that correction back to the system that generated the original order (the storefront, the CRM, or the ERP where the customer record lives). The logistics platform knows, for a few hours, what the right address is; then that information gets lost between one delivery and the next, and the order system keeps the incomplete version.
It is the same mechanism behind why customer data ends up in silos: every system keeps its own version of the same person without sharing it. Here the silo is the address, and the cost does not show up until the second order.
Why the overall failed-delivery rate hides the problem
Most businesses track failed deliveries as a single monthly rate: how many out of total shipments. That number can look stable, even good, while a small group of repeat customers keeps failing at the same spot every time. The metric that actually exposes this is different: repeat-failure rate per customer, not the aggregate rate. How many of this month’s failures had already failed before, at the same address, for the same reason.
How to close the loop
- Give the driver one place to log the correction, inside the same app that already confirms delivery, not a separate call to support that nobody writes down.
- Wire that event to the customer’s profile, not just to that one order. The goal is for the next purchase to start from the corrected address, not the original one.
- Pre-fill the corrected address at that customer’s next checkout, instead of leaving them to retype (or not fix) the same incomplete data.
- Track repeat failures as its own metric, separate from the overall rate: it is the one that tells you whether the loop actually closed or whether every fix keeps getting lost again.
The same principle behind turning a shipping notification into a retention moment applies here in reverse: it is not just that the logistics event never reaches the business, it is that the data the business needs most (where its customer actually lives) sometimes only exists, for a little while, in the head of whoever made the last delivery. It is the same data quality problem we work on in our data and measurement practice: a business cannot automate retention on an address it never actually captured correctly.
If your logistics team is fixing the same address twice for the same customer, the problem is not the street: it is that the fix never made it back to where the order started.