About the company
BURRP is a restaurant recommendation engine that is functional across multiple cities in India. It provides its users with offers that are in association with various restaurants. The user can book and visit a restaurant after a comprehensive view of its cuisine, recommended dishes, etc.
The challenge was to design a new application that would allow the users to order via the BURRP application. This required comprehensive research of the users who were used to ordering food via their mobile. Every user perceives the application differently based on their requirements. The idea was to maneuver through the state of mind of each persona and create an optimum solution for ordering food.
Designing the user flow for a user who browses through restaurants available in their area, checks what they offer, chooses the desired dishes and places an order.
Research and discovery
We conducted user research with a small sample size of 51 users to see if they could confirm our hypothesis of the mistakes made by food delivery apps. We observed that food-delivery apps had to be focused on users who order everyday to twice a month. However, new users could not be neglected as they formed the base to improve conversion rates. Every response was unique providing divergent ideas that could be used across the platform.
Based on the responses, we narrowed down the audience to people who consistently use the delivery apps to get food quickly. The audience requires quick navigation spanning across every filter and doesn’t want to spend a lot of time on the application as they become indecisive over a period of time.
The designs take a straightforward approach and show off multiple feature recommendations based on the survey. The designs had to be clear and concise, making the user interaction seamless. The experience had to be explicitly thought through to enhance the usability of the application. Adding features that would be relevant to the responses received would improve click-through rates and customer retention.
Iteration was made over the food apps existing in the markets and the following logical features were included.
- Food delivery time — The time in which the food was delivered is crucial as it serves as a decision-maker at times.
- Quantity of food in cart — This would essentially let the person know the serving size of the food in the cart. Users tend to order more/less food. This feature would stop the hassle of over/under-eating.
- Data based recommendations — Users usually have specific food types that they prefer or even dietary regulations. Why not display the recommendations based on their past orders?
- Change for Cash on delivery orders — A simple feature that asks the delivery executive to get change for a specific denomination so that the order is delivered seamlessly with the required change.
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