Missing numbers from residential complexes add challenges to delivery drivers and completing their last-mile.
Delivery drivers in peak season have one of the most challenging jobs to complete. As more companies are switching to a fully eCommerce interaction with their customer base, expanding B2C and D2C interaction, delivery drivers are experiencing the result of that expansion head-on and having to make do with incomplete data.
Here’s what we mean by that.
Delivery drivers are struggling with last-mile delivery challenges.
Understandably, people want to decorate their homes during the holiday season in preparation for festivities. However, in doing so, we sometimes need to remember the little things, like not covering house numbers.
These decorations can prove tricky for seasonal workers not familiar with routes to deliver packages to the correct destination successfully; it also makes it challenging for seasoned delivery drivers familiar with their routes.
And these last-mile delivery challenges are not mutually exclusive to the holiday season. These last-mile delivery challenges are a year-long problem that delivery drivers face daily.
Missing or covered house numbers and addresses spelled out can cause confusion and make it difficult for delivery drivers to determine the precise location of delivery.
These hindrances can also affect when packages are delivered as delivery drivers can change up routes to accommodate residential areas that are not well-lit at night (to account for poor visibility).
So, it’s in everyone’s best interest to try to accommodate delivery drivers in the following ways:
The last one doesn’t help delivery drivers find the correct address. Still, it’s only with the delivery drivers' help and commitment that we even receive our little packages of delight that we ordered less than 48 hours ago and already forgot. So, let's help our delivery drivers quickly locate our homes this holiday season by keeping them in mind all year long.
Learn about Beans.AI's mission to improve location intelligence in an evolving world that requires precisely mapped data.