Case Study: Ironhack Bootcamp project #1 Greenpoint App
For our first group project we chose to develop a solution for making urban mobility cleaner and more efficient. In this project we were supposed to emphasise UX with the main deliverables being User research conducted with different methods and concept sketches of the final project. Our timeframe for this project was 2 weeks. Since my team 3 members Carolin, Maria and me are living in capital cities we can witness first hand how the relationship between the cities residents and transportation is changing and keeps evolving the more a city develops. Our target audience for this solution were commuters in cities upwards populations of of 1 million people.
Our initial research led us to the underlying reasons preventing people from choosing greener alternatives in their daily commutes and influenced our final product as well as guiding our approach to designing a solution.
Most of our users depended on daily work commutes and lived in cities with populations larger than 3 million people. Our project was built on remote collaboration, complex tasks were split between us, smaller tasks were taken over by one person and then discussed and refined with the other people on the team. Our timeframe for developing our products was 2 weeks set by Ironhack. Due to working remotely we collaborated on a daily basis by coordinating within the team via Slack and working around each others schedules and timezones.
We used a Lean Survey canvas to guide the questions we wanted to ask in our Survey:
Our survey was created in Google Forms and answered by more than one hundred people. It allowed us to gain better insights into our Users.
The most popular transport options chosen were Car: 26% and Bike 26%, followed by Underground: 20% and Bus: 12%. To me it was surprising that so many people used their bicycle for their daily commute. The majority of people used city transport for commutes, followed by fun & activity. The majority of users said that they faced their main challenges while using city transport with encountering crowds and long waiting times. Users main criteria for choosing their mode of transport were:
1. Convenience
2. Speed
3. Accessibility
We proceeded by conducting User interviews. The interviews were conducted by each team member with another member being present to take notes. We collected our Users responses and created an Affinity Diagram from them, first only recording their answers on Sticky Notes and then grouping them into categories that we saw from patterns arising in our answers.
We used a voting system to decide on which replies we thought were the most insightful to what our User’s experience should look like.
We summarised our Users responses on an Empathy Map Canvas, to fully grasp what our Users current experience with city transport was.
Following this, we were able to create our Primary User Persona based on our previously conducted user research.
We outlined her User journey to identify opportunities where she encountered pain points, frustrations and problems.
We used Mind Mapping to map out the most important features for our “Greenpoint App”,
as well as phrasing different “How might we Statements” to make our ideas for the solution more solidified and implementable.
The final step of our project was to create concept sketches as well as presenting our results to our bootcamp.
My key learnings from this project were that most users were willing to use a greener alternative if they did not have to make a trade in any of the criteria they used to pick their transport (such as speed or convenience). Our solution addressed this with the help of an incentivization mechanism which allows the users to collect rewards for choosing greener options which they can later redeem for different bonuses such as a free ride, discounts at supermarkets, free coffee or to plant a tree alternatively.
Furthermore the users are able to utilise a map that calculates all the transport options for them and how many rewards they will get choosing the greener alternative. Our solution was not foreseeable at the point of picking a problem to work with and kept evolving based on our User research and how much we emphasised with the current User experience. We reached our final Product Design based on what our research demonstrated could improve the Users experience aligned with the project goal, which was to make urban mobility more clean and efficient.
