Redesigning My Revolt app to make it more intuitive
Pet project, 2021
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My Role:
Expert review of the app,
User interviews and data analysis,
ideation, wireframes, visual design, motion design, prototyping
Introduction
Introduction
As rightly mentioned on their website, the revolt bikes were built for generation why (different from generation Y, born between 1980-1995). The target consumers of the vehicle were students / first time bike owners. It was also observed, in Pune that 98% of bookings were for male drivers. While designing the bike, care was taken to make the vehicle styling appealing to the younger generation.
Rishi, 19
Second year engineering student, bike enthusiast.
Attends college everyday, rides about 20 km per day. Born in the family of single earner, father, who is an officer in a nationalised bank.
Trying to save money, to buy his dream bike one day.
Sumit, 31
IT professional.
Supports environmental causes, like ocean4, and bamboo products. Relies on public transport as much as he can to curb pollution
Recently felt a need for a bike.
THE PROBLEMS
THE PROBLEMS
The app has a clean and minimal layout. however, there were few flaws that were uncovered by talking to the users as well as competitive research
Then came the stage of brainstorming. each and every problem was addressed and attempted to solve during the redesign.
THE REDESIGN
THE REDESIGN
After the brainstorming session and whiteboarding, wireframes were created with minimal, yet essential data, to concretize the data per screen, navigation and CTAs.
the following animations show the final result with interaction, visual design and embedded micro-interactions.
When the mode is selected, the range changes accordingly. small visual elements help identify the mode better
A cool-looking animation for the menu reveal can add the necessary edge to the experience
The app comes integrated with a mapping service. This allows the user to save favourite places like work, school, college, home, and a few custom names.
All the saved places will be listed in the destination for quick navigation
Once selected, the app will calculate if the journey can be made with the remaining charge and show appropriate warnings.
The most important problem to be solved, which was reported by the majority of consumers, was the lack of active notifications.
to tackle that, a Machine learning algorithm will be built into the app, which will capture the travel times, and places to be visited. e.g. Monday to Friday, 10:00 am is time to travel to work while 7:00 pm is time to travel back home.
based on the pattern, the app will trigger notifications, 2 hours prior to the time of travel, if the expected travel range is not achievable by the current charge.
e.g. if the battery - buffer is not sufficient to travel back home, then the app will trigger a notification, informing the user about the possible halts. this will allow the users to either charge the battery or plan their travels through swapping stations.
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