We live in an age where we needn’t bother about cooking food in case we get back home from work late or in case we have sudden hunger pangs and need a quick snack. Ordering food from our favorite eating place has never been so easy.
Going by numbers it is expected that the food delivery market is expected to grow to $5 billion in 2019 and $15 billion by 2023. With companies like Swiggy and Zomato already having a presence in almost all of the Indian Tier-I and Tier-II cities it is likely that this market will grow rapidly with new players coming in with differentiated strategies.
According to a report by data intelligence platform KalaGato, as of first half of 2018 , Swiggy had a market share of 36.40 percent, with FoodPanda being a close second having a share of 32.02 percent. Zomato had a market share of 23.78 percent . Although Zomato had a lesser market share , it has made its global mark by being present in more than 300 cities. [Source]
Many factors affect or help food-tech companies grow their business. Real-time traffic, order preparation time, delivery executive efficiency to name a few. Time and distance play a very important role in various legs of completing a delivery.
Gone are the days when we used to use maps just to locate a place. Food-tech companies are using location data for improving the various facets of their organization. Let’s have a look into some of them!
Search and Discovery:
FoodTech companies use location data to help their customer to search or find new restaurants near them. They do this by finding which restaurants can take orders and deliver them within the maximum delivery time. This is known as serviceability.
The trick here is to find the right time to ensure delivery and providing the ample choice of restaurants for which the delivery time can be calculated as quickly as possible.
Customer experience remains the top priority of such food delivery services. If the delivery time is too high or if the user isn’t shown a good number of restaurants then he/she might not place an order in the first place!
Once you order on your phone, the nearest delivery executive is handed over your order. A lot of calculation goes behind this such as distance between the current location of delivery executive and the restaurant along with the time taken to reach the customer location.
At times there arises a situation in which a restaurant gets two orders from customers located nearby. In such a case, the concept of batching comes into play and a single delivery executive is assigned to deliver both the orders.
Two orders are said to be batchable if the delivery time of all orders in a batch match with the estimated delivery time promised to each of the customers.
Last-mile delivery is much more than just the journey from the restaurant to the delivery location. Food-tech companies use their own maps to accurately calculate the estimated delivery time which includes even the time taken by the Delivery Executive to travel the “last-to-last mile” (for example the time taken to travel from the society gate to the customer door).
They leverage both historical data along with real-time signals to build and improve their own maps to scale their needs.
Optimizing Delivery Cost and Time
Food delivery is a complex problem to solve as it involves finding a balance between maintaining good customer experience while keeping high efficiency in delivering orders. They do so by optimizing time, cost and routes even in unavoidable circumstances (like rain, traffic issues, and fall in available delivery executive).
They achieve this goal by minimizing the unutilized time by the delivery executive either by cutting down the waiting time at the restaurants (while the food is being prepared) or by minimizing the time spent by a delivery executive waiting for the next order to be assigned to him/her.
With the emergence of “cloud-kitchens” last-mile delivery is something that is making a huge storm in the market in terms of size and technological improvements.
Location intelligence along with artificial intelligence is helping them to predict customer buying patterns and stage inventories in the cloud kitchens nearest to where the demand for home-delivery is more.
To know more about how to use location data for site planning, check this out:
Burger King used real-time geofencing along with location intelligence to find customers within 600 feet of any of their outlets so as to offer digital coupon discounts on ordering from any of their outlets.
This way they optimize customer engagement which helps them increase awareness and win customers efficiently. [Source]
To read more about geofencing and geomarketing, you can head here:
As consumers are getting more relaxed about their data, food-tech companies are using this to their advantage and making use of real-time location to target the right customer at the right time.
Location-based advertising has many pros like better and real-time data and the traction that companies get from it is really high. For example, in areas where they mostly get lunch orders (like colleges or commercial spaces), they could make promotion regarding breakfast offers from various food joints.
Not just these but companies are using location intelligence to find zone-wise cancellation rates, find the ratio of demand and supply of food and delivery executives in an area and get a snapshot of delivery and restaurant footfall volumes and their variations.
Watch this video to get a visual understanding of how food-tech companies are using location to harness their business!
Eatigo, a popular restaurant reservation platform uses location intelligence to offer discounts to customers based on their location and time of day along with other factors like day of week and weather conditions.
The aim is to determine if a customer is willing to travel for availing a discount big in numbers. For example, a person might travel 10 km or so if the discount offered is higher. [Source]
Real-time Traffic Monitoring
Companies are monitoring real-time traffic to have a relative sense of consumer foot traffic patterns between their stores and their competitors.
Not only that these brands well known as “quick-serves” are using real-time traffic to have betters insights into the relative share of wallets, competitor store visits, and customer loyalty.