Maccas Run with Uber
I conducted research in two different stages using both quantitative and qualitative methods. The first stage involved distributing surveys to analyse and assess respondents’ answers.
The second stage involved conducting in-depth interviews with some of those respondents as well as analysing recent reviews of a ridesharing app so that I could gain a higher level understanding of user problems and needs. My research objectives were:
1. To identify what rideshare apps were used the most and in what context they were used
2. To find out if there were any common pain points or issues that respondents/users had when using rideshare apps
3. To determine if there was any functionality that would improve a respondents experience when using a rideshare app
A survey was sent out to participants to see what rideshare apps they used, what they rated them and analyse what context they use them in. It also explored if they use any companion apps with them (such as ubereats).
Select participants from the survey (those that used rideshare apps the most) took part in structured but open-ended interviews.
Based on the survey responses on what rideshares were used the most, an app was selected (Uber) and recent reviews were analysed to evaluate any other pain points, frustrations or comments users had regarding the app.
Survey takeaway #1
Younger people regularly use rideshare apps more often than those that are older and are mostly using it for their night out.
That said they use rideshare apps regularly (4+ times a week) were between 18-30
of those respondents exclusively use Uber
of those respondents only use Uber when going out socially at night
Survey takeaway #2
Most people want something to eat after a night out but only if it’s within walking distance
of respondents were ‘likely’ to want something to eat after a night out
of respondents were only willing to get food that was within walking distance of where they were
of those respondents would use a food delivery app to get something to eat after their night out
Interviews and secondary research
I collated the insights from the interviews and the secondary research into affinity diagrams. I split them into two different diagrams to try and keep the insights consistent. This was because the secondary research was made up of insights from reviews while the insights from the interviews came from the same question that were asked for all participants.
Although most of the reviews that I analysed were critical of factors that I wouldn’t be able to design a solution for (such as pricing and drivers cancelling last minute) I found that accuracy and booking with a peace of mind were important for users. The interviews contained more important insights with participants voicing a need for simplicity and convenience when using rideshare apps.
The majority of users mention time-saving as the main reason for using Uber. While it seems obvious, any design will need to make sure that it’s not unnecessarily wasting time for the user.
Users want to be able to easily use the app without any hitches, particularly when drinking or tired at the end of a night.
Conveniently meets needs
Users cited how food and alcohol are inconvenient to get at the end of the night. They usually gave up because it was frustrating to plan a detour or they didn’t know of anywhere open nearby.
Framing the problem
When I started my research I assumed users would just need help with using a rideshare app in the most seamless way possible. However, I found that there were pain points before and after a trip that could be designed and solved for within the framework of the app.
My research revealed that one of these pain points was users wanting to stop for food on their way back from a night out – which can be difficult to do if they had ordered or were already in an Uber. This helped me develop an actionable problem statement:
It can be inconvenient and time consuming to stop by somewhere to eat after a night out. Not only are there fewer places open, but often you have to go out of your way to reach them which can be difficult when you’re in an Uber going back home.
Defining the users
The problem that I identified affected most of the participants that were interviewed. So I decided to define two personas based on the information and experiences that were shared in the interviews and surveys. These personas helped inform me during the sketching and ideation process and made sure I was fulfilling and meeting the needs of the users.
I initially sketched an idea that would integrate ordering and picking up food in a drive-through within the Uber app.
These first sketches showed how the interface would function and how a user could progress through each stage – starting from when they’ve ordered an Uber and going until they have ordered food and rerouted their trip.
This was used as a starting point for the UI and allowed me to think through how users would potentially progress through each state of the journey.
Low/Mid fidelity Wireframes
These were refined from the previous sketches and created within Figma so that a prototype could be tested.
Based on insights from the interviews I changed the app so it would only allow a user to select a Mcdonalds to order from and reroute to pick up through their drive-through.
Select parts of the copy were changed to reflect this and because this was going to be a feature within the Uber app I based the layout and design elements on the existing app. Simple interaction was also added so that key buttons could be tapped on to move forward or backward through the wireframes.