4 Evolving Hyperlocal Delivery Challenges and How to Address Them

A rider enters a popular pizza restaurant to pick up the order to be delivered within the next 15 minutes. He finds out the restaurant still needs to start preparing. In another incident, a rider spoofs his GPS location to falsely mark a late-night grocery order as delivered. Or a freelance rider refuses to show up at the hub after a premium-value CoD order. Imagine its impact on the cost of operations and business reputation. The delivery provider will have to bear the wrath of unhappy customers due to delayed, fake deliveries, and inaccurate cash remittance, making them bleed more than just cash.

Let’s look at some new delivery challenges that have come to the fore in the evolving hyperlocal order fulfillment space. Also, identify the ways to nip them in the bud.

1. Inefficient Order Clubbing

This challenge is associated with the latter instance in the intro. While en route order clubbing enables a business to reduce logistics costs by combining multiple deliveries/pickups on the go, neglecting some crucial components can cost heavily.

Order clubbing at the hub level via APIs enables delivery managers to configure not assigning certain rider contract types for certain delivery types and distances. For instance, freelance riders are not to be assigned CoD orders whose amount exceed the riders’ daily wage limit or more than 3 km of delivery distance. Full-time riders can be prioritized in such cases for a secure and cost-efficient delivery execution.

Intelligent allocation engines allow greater control by considering configuration parameters based on wait times, the distance between two destinations, vehicle type, maximum assignment time, rider contract type, maximum orders to be clubbed, and many more. Let’s say although a system won’t allow clubbing an order that exceeds the driver’s maximum wait time, it may make an exception if the next rider is expected to take longer than usual. Bicycles can also be prioritized for shorter distances of up to 1 km, beyond which the system can configure other vehicle types like a bike or van. Well-defined clubbing configurations ensure optimal utilization of resources and reduce capital and operational expenditure.

2. Inaccurate pickup ETAs

So your delivery deadline exhausts in the next 4 minutes; the rider arrives at the dark store for the pickup, but the order awaits packaging. Lack of visibility into rider pickup ETAs is to be blamed here. 

Optimizing deliveries through pickup ETA tracking can help. Adding configurable pickup service time and delivery service time while calculating ETAs can render a transparent view if the orders are clubbed. Alerts on pickups and delivery ETAs can help store teams/merchants prioritize/de-prioritize order packing based on delivery SLAs. Notifying the merchant of rider arrival based on a defined geofence can also reduce the handover time.

3. Growing Cases of Fake Delivery Attempts

Customer satisfaction highly revolves around on-time deliveries and receiving the package in the proper condition. But what if an order is marked delivered without any delivery attempt? Technological progression has cradled opportunities for some miscreants to leverage GPS apps that spoof locations and show themselves within the customer geofence. So, while the eCommerce app shows the order was delivered before time, the end customer just still hasn’t received it. This could be done by riders to:

• Accept orders from busier locations
• Get paid for more, long-distance deliveries
• Get paid for orders they did not deliver
• Get incentivized for early deliveries

Fake deliveries cost retailers heavily. They have to bear the logistics costs of a fake delivery and pay back the dissatisfied customer through a refund or replacement. 

Fighting off such instances requires robust measures that: 

• Checks rider’s device for enabling developer options to use mock GPS apps
• Automatically captures rider’s GPS location at set intervals, say 10 seconds
• Alerts if the system-suggested route and actual delivery route do not match 
• Validates delivery statuses with customers

Smart delivery management solutions also notify riders to turn off mock locations before accepting more assignments or recording delivery progress. Such tools also enable merchants to configure auto-checkout for riders in case of non-compliance.

4. Poor Management of Non-Delivery Reasons (NDRs)

It happens when the merchant partners with multiple logistics providers to execute deliveries. All logistics partners use their own interface and terms to define the same non-delivery reason. These could be as simple as:  

• Not delivered at Illinois 
• Not delivered at IL 
• Order not delivered at Illinois

A different phrase for a common NDR makes it challenging to collate delivery data and identify major operational crevices since the system presumes the above as three distinct reasons. The count of such NDRs can go up depending on the partner network. 

Cutting-edge logistics management solutions empower retailers to seamlessly gauge non-delivery data, identify the primary causes for “undelivered” consignment and make informed decisions while taking corrective measures. We have seen instances wherein intelligent courier aggregator platforms have consolidated a list of 60+ NDRs into six categories. Innovative dashboards help the retailer to know NDRs that are:

• Most frequently cited in a particular city (or geography)
• Most recorded by a 3PL
• Most recorded at the origin hub

This not only simplifies and structures non-delivery data but also helps validate them with the customer.

Access an ultimate guide to Hyperlocal Delivery Management System here.

Balancing the triad of customer experience, delivery SLAs and operational efficiency is complex. But the right technological tools can arm businesses with the power to mitigate inefficiencies, make operations fool-proof, gain competitive advantage, and evolve with newer delivery models.