5 Best Practices To Ensure Delightful and Profitable Hyperlocal Deliveries

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5 Best Practices To Ensure Delightful and Profitable Hyperlocal Deliveries

In times when delicious piping-hot meals, last-minute gifts, and medicines get delivered within minutes, it is hard to determine what is next in the ultimate convenience model. 

However, for quick commerce businesses, merely “knowing” the ultimate convenience is not the end. They need to create and deliver business offerings that align well with this modern notion of the ultimate convenience. Also, the brands that crack this puzzle are the ones that are going to grab a satiating share of this 72 bn dollar opportunity in the future. 

Let’s have a look at what exactly should be on their 2023 agendas to unlock this opportunity and how they can strategize for the same. 

Leverage In-Depth Real-Time Data for Decision Making

To thrive in the On-Demand Economy, businesses must completely eliminate guesswork. Data must back every decision taken, be it predicting demand, maintaining resources, gauging customer expectations, or managing risks.

Powered by real-time data-driven dashboards that aggregate analytics of riders, orders, and other SLAs, a smart logistics management platform helps logistics stakeholders ensure accurate decision-making. 

Stats suggest that a smart logistics management platform enables marking 95% of deliveries in real time. It also allows delivery managers to archive reports for improved logistics planning and KPI evaluation. 

Use Mixed Fleet To Boost Delivery Agility and Flexibility

Rigidly mapping drivers to stores and specific locations significantly reduces logistics flexibility. Hence, when executing deliveries from physical and dark stores, a floating fleet makes a huge difference. Such a fleet can quickly respond to unprecedented and fluctuating demands keeping SLAs and customer experience intact. 

Hyperlocal deliveries management

Stats reveal that brands using a floating fleet mark 90% of deliveries on time while saving big on costs. It eliminates the need to make significant investments in dedicated resources for stores where the load is considerably low.

Ensure Dynamic Enroute Order Clubbing For Better Utilization

When planning deliveries, hyperlocal delivery providers can club multiple planned and on-demand orders in the same delivery route to boost productivity and reduce trip volumes. Dynamic order clubbing can help businesses achieve 14% more deliveries per driver.

Smart routing tools automatically draw a polyline on a map with a defined deviation radius to optimize delivery productivity. Such tools also limit the maximum number of orders that can be clubbed to ensure equal load distribution among drivers and drive delivery efficiency.

Merge Planned Orders Using AI-Based Route Optimization 

When it comes to optimizing costs and capacity, merging planned orders is crucial. Hence, deploying intelligent route optimization tools is essential. Such tools use various constraints of your consignments and resources and align these with multiple optimization strategies ensuring the correct allocation of orders to multiple vehicles.

Intelligent route optimization technology leverages in-built AI routines to increase vehicle utilization by 31% and reduce the average distance traveled by bikers by 23%.

While allocating orders, such tools also analyze constraints to generate highly efficient delivery routes, understand resource requirements, and even chalk out an exact loading sequence.

Gamify and Automate Rider Incentivization Processes 

Massive order volumes, an increasing number of trips, and a growing number of riders make it difficult for hyperlocal delivery providers to chalk out incentive programs accurately. Gamification has been proven to increase on-time deliveries by 24%.

Gamification of rider KPIs offers complete visibility to both delivery managers and drivers over the number of orders successfully completed, customer ratings, delivery delays, and more. These metrics can be easily configured based on an incentive strategy. 

Then in-built leaderboards help drivers gauge their performance against others. Using machine learning algorithms, delivery stakeholders can quickly identify any attempts to manipulate rider KPIs and also suggest tips to improve rider performance.

Hence, in the future, the success in the quick commerce model stems from a multi-faceted business strategy that reduces costs, optimizes resource utilization, works on data-driven insights, and delivers scalability without compromising sustainability.

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