MeterMaid is a mobile service design solution that opens up homeowner's driveways to drivers in need of parking in urban environments. Our final deliverable was an invision prototype.
Skills | Literature Review, Prototyping, Competitive Analysis, Screen Mockups, Reframing, Persona & Scenario Generation
Team | Jayanth Prathipati, Shannon Sullivan, Rho Eun Song, and Kathy Liu
MeterMaid is an application that enables homeowners to capitalize upon their open driveway space by connecting with drivers seeking convenient and affordable parking. We designed it specifically to facilitate trust between the two parties, particularly on the homeowner’s side by enabling them to monitor their property in the application, report inappropriate or malicious behavior, and deal with poor behavior through a robust ratings system.
We started our research by understanding the target personas for the application. We had one for the homeowner and one for the driver.
After examining the personas and problem statement, we conducted an Ecosystem Collection where we discussed what we know about the parking domain, what we don’t know, and what are the critical questions we want to explore.
From the ecosystem collection, we determined that we should focus on understanding the current experience for people looking for parking as well as understanding the factors that go into successful crowd-sharing platforms such as Uber, AirBnB, and eBay, particularly how they build trust between their users.
Based on this focus, we generated the following specific research objectives:
- To gain a more complete understanding of the context in which people look for parking and what they prioritize (proximity, cost, etc.).
- To understand the existing choices for parking and how they operate.
- To understand the trust mechanics of existing resource sharing services (e.g. AirBnB, Uber) and to understand what led current users to trust these services
In order to answer these questions, we researched existing services and how they leveraged design and technology to maintain trust (AirBnB, Uber, and eBay). We also used a short survey to figure out how users in Pittsburgh felt about parking. In addition, we interviewed 2 Uber drivers to understand how they mantained trust in the service. Finally, we interviewed two parking managers in person for an hour to understand the management aspect of parking.
Key Insights from our research
- Many of our users viewed safety as the most important factor when selecting parking
- Parking Lot Managers have to spend a lot of time balancing between managing revenue and dealing with exceptions such as towing unauthorized parkers
- Drivers trust Uber/Lyft to handle any edge cases, E.G getting an unruly driver.
- There are several factors that will build and retain user trust
- A positive first experience with the application
- Ratings System
- Good support system from the application
We then built scenarios to exemplify our key insights and figure out what edge cases we need to worry about when building this app, such as What happens when drivers arriving really late to the location? How does a driveway owner handle harassment? What security measures are in place to make sure that people do not use this service for robbing houses?
From these scenarios, we created over 20 storyboards in order to figure out what details we needed to look at. I was specifically interested in the idea of trust and focused on bringing that up in the scenarios that I created.
After creating these scenarios, we had a clear understanding of what edge cases we needed to accomodate for
and where breakdowns could potentially exist in a service like this. We narrowed down and focused on a set of 8-9 scenarios that seemed realistic.
We went through multiple rounds of prototyping, starting from paper and pencil prototyping to medium fidelity prototypes, to high fidelity prototypes using InVision and animations using Principle.
I was focused on how to build trust into the situation and started prototyping out screens to help both sets of users; the driver and the homeowner do that. I specifically tried to help the homeowners understand their value and how much money they were getting through the service for their time.
I helped create some of the low and medium fidelity prototypes and focused on directing our user flows to help with situations to help build and retain user trust, such as ratings and support.
Here are a few examples of lo-fi prototypes that I built:
I also focused on making simple animations with Principle to give a testers a better sense of functionality and flow in the app. In addition, we wanted to add a sense of delight into the app as well.
We built a prototype using invision to give a better sense of our story and presented it to the class. We found that we weren’t extracting the data from this service effectively and could have had better a better value proposition for businesses and individuals trying to use the service. Overall, people loved our service and found that they wanted us to keep exploring and building out this system.