This story is cross-posted at www.dista.ai
Customer is a US based fast food restaurant chain distributed across more than 100 countries. They serve more than 65 million people each day. A part of their diversified operations of 200 restaurants run in west and south regions of India. This case study unfolds how we helped them leverage the power of location intelligence with Google Maps in these regions.
As a technology advisor, we delivered a Google Maps powered location-enabled system. The system helped them successfully deliver delightful delivery experience to their customers. The solution now helps them deliver a modern, engaging food delivery experience.
Core challenge: Reaching where the customers are…
Food delivery service is at the heart of customer’s operations. Their fast-food restaurant chains operating in South and West India were struggling with creating and maintaining their alpha-listing (an optimized list of serviceable areas in a particular geographical location).
Being a major fast food chain, customer had a vast scope of delivery operations. With that, they needed up-to-date serviceable location listings in their system. Their legacy system was obsolete and failed to identify any recently functional areas. The system was overwhelmed with following challenges:
False order refusals
The customer’s delivery operations team could not clearly demarcate the serviceable areas.
The system was limited. They could not precisely identify new serviceable areas. Hence, they were turning down the orders that they could actually deliver. This resulted in false order refusals or inability to keep delivery time promises.
The main reason was manually defined delivery areas, which were outdated, coupled with delivery staff’s fear of not meeting the SLA. Operations teams failed to find the optimum delivery area so as not to miss SLA and yet serve largest area.
Frustrated customers refused to accept order due to delayed delivery. The root cause for customers’ dissatisfaction was delivery staff’s lack of access to customer’s accurate location.
With these challenges, the fast-food brand was dealing with decreasing revenue and was losing out on their customer base.
Google Maps powered location solution helps customer deliver engaging customer experience with faster food deliveries
MediaAgility, a digital consulting company and Google Maps Premier Partner, recommended Google Maps based system to the customer. The solution runs on Google App Engine with robust mapping functionalities such as Delivery Perimeter Definition and Locate Me.
Dynamic delivery perimeters
With dynamic delivery perimeters, the operations staff could easily create the trade zone. The trade zone could be created manually on the map, while considering the distance/time and traffic or the system could auto-create the trade zone with same set of considerations. The trade zone was then used by the system to map orders to an appropriate store based on order pipeline and delivery personnel availability. They could even switch between different delivery perimeters based on seasons, weather conditions, time of day, etc.
The operations team could also define non-serviceable areas. Hence, it helped them to be more precise and well-timed with every order they delivered.
Finding customer’s exact location
‘Locate me’ feature helped them improve their delivery operations. The feature helped their customer pinpoint their exact location on the map. This ensured correct store mapping and speedy delivery within SLA and resulted in customer delight. With ‘Locate me’, their app users can now choose their exact location with a single click on Google Maps. They can also drag & drop the locator pin at an accurate location on the map. The solution helped delivery staff reached customers faster without any delivery delay.
Harnessing the power of location helped customer recover from the crunch of lack of location data or inaccurate data. As a result, customer optimized their sales, improved operational efficiency and customer service.
In their pre-Google Maps days, their traditional approach to analyze locations was painfully slow, exhausting and expensive. The employees used to scout the streets, painstakingly create hand-drawn maps of the delivery perimeters, manually pointed out landmarks such as fire stations, traffic signals, etc. Every new store took 1 month of setup time and the information was never updated.