Improving Delivery Services With Custom Map Solutions

Published on February 3, 2021

Location intelligence and accuracy are critical for on-demand delivery, e-commerce, and mobility services. The accuracy of driver routes, geocodes, ETAs, and snap-to-road APIs is crucial as they directly impact customer satisfaction and operational efficiency. Enterprises need more accurate custom map solutions to enhance customer satisfaction, reduce driver stress, and increase revenue.

The rise of on-demand delivery and transportation has skyrocketed while consumer expectations have grown proportionally. Keeping ETA promises to consumers builds trust and loyalty while significantly improving brand perception and customer retention. Drivers, on the other hand, want stress-free environments and the best route possible.

Food delivery revenue in the U.S. in 2020 totaled $26.5 billion, an increase of 204% in the past five years, Business of Apps reported. U.S. food delivery revenue is projected to reach $42 billion by 2025.

Issues arise from underlying locations-based mapping technology that impact consumers daily, such as food deliveries that arrive late or cold, ride-hailing services provide the incorrect ETA arrival, and parcel packages are sometimes delivered to the wrong addresses.

AI Powered Custom Map Solutions

Artificial Intelligence (AI) solutions applied to locations-based technology leverages local traffic, addresses, various vehicle types, and unique driving behaviors to build custom map APIs. AI-powered geocoding and search APIs can be designed specifically to handle use scenarios across transportation and unstructured addresses. AI seamlessly leverages historic customer delivery data to ensure the same mistake never happens twice.

Proprietary engines backed by state-of-the-art AI deliver extremely accurate ETAs for all user scenarios. Delivery drivers are also held accountable for providing services within a time-based ETA. Customer satisfaction is determined by the delivery of service and tips are greatly improved when ETA promises are kept. 

Locations-Based Technologies For Enterprises

Enterprise adoption of locations-based technologies is already occurring with digital-native companies such as DoorDash, Lyft, and Uber. According to recent 451 Voice of the Enterprise research, locations-based technology is one of the top disruptive technologies that enterprise organizations plan to adopt over the next 24 months.

The hospitality and transportation industries have been adversely affected by the pandemic, .yet the pandemic has made customer experience more relevant. Throughout the lockdown consumers adopted services, such as e-commerce, grocery, and food delivery that rely on locations-based technologies. BIS World reports there are 247,466 Couriers and Local Delivery Services businesses in the U.S. as of 2021, an increase of 3.8% from 2020.

The demand for locations-based technologies is expected to expand as the adoption of key use-cases continues to grow post-pandemic. This includes business-to-consumer applications to enable discovery, dispatch, pricing, routing, billing, and zoning for ride sharing, as well as enabling order management, estimations, routing, and real-time tracking for food and grocery delivery services.

The enterprise addition of locations based AI technology with custom map platforms directly offers business solutions that lower operating costs, provide faster arrival times, lower cancellations, and more accurately priced orders. 


Ajay Bulusu, based in Singapore, Asia, is the Co-founder of NextBillion AI, an industry-leading startup in mapping platforms providing software-as-a-service (SaaS) for enterprises. Bulusu has spent the last decade working in mobility, maps, digital advertising, and eCommerce across various roles in the US, Singapore, India, Japan, and the UK. Bululu formerly worked at Google and led the Geo team at Grab. After building out the Grab Geo team from 0-400, he found the inspiration to start NextBillion AI. Bulusu handles investor relationships, APAC customer relationships, and manages the largest early-stage customers of Nextbillion AI.

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