HopIn
Neighbourhood Carpooling App
HopIn
Neighbourhood Carpooling App
Overview
HopIn is a carpooling app that connects neighbors and colleagues, offering an affordable, eco-friendly alternative to commuting alone. By fostering community connections, HopIn reduces travel costs, eases traffic, and promotes sustainability. In this bootcamp project, I led the design process, user research, and prototyping, focusing on building connections and creating a user-friendly platform that helps people thrive together through shared rides.
Role
UX Designer,content strategist.
Goal
The main objective of HopIn was to create a platform that simplifies carpooling for neighbors and colleagues, fostering community connections while reducing commuting costs and environmental impact. Aimed to reduce commuting costs and increase user interactions By Connecting people in the same area, HopIn helps reduce single-occupancy vehicle usage, promoting both community engagement and sustainability.
Tools
Figma, Miro,Maze
Interview
During this phase, I conducted 7 interviews over 4 days to understand the commuting habits and pain points of urban carpoolers. The interview script included 32 open-ended questions, focusing on daily routines, safety concerns, and attitudes toward sharing rides with neighbors.
Surveys
We distributed an online survey to community groups and commuter forums, receiving 25 responses within 3 days. The survey helped us identify key challenges and opportunities in carpooling, including trust, cost, and convenience.
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Personas
To better understand our users' goals, needs, and behaviors, we created four personas representing our key user segments. Based on user interviews and surveys, these personas were continuously refined throughout the project. They helped us reassess our design decisions and stay user-focused during the HopIn platform's development.
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What data did we use?
We gathered data from user interviews, surveys, and focus groups, analyzing user behaviors, pain points, and preferences. This helped us capture relevant demographic, psychographic, and behavioral information about our users.
How did personas impact the design?
Personas guided the prioritization of features like safety, ride-matching, and user interface simplicity. They helped us address specific user needs, ensuring a smooth experience for diverse users, from tech-savvy commuters to community-oriented individuals.
When did we use personas?
We referenced personas during ideation, wireframing, prototyping, and user testing stages, ensuring that HopIn’s design remained aligned with real user needs at every step.
Why did we create personas?
We wanted to ensure that HopIn’s design directly addressed user needs and problems. Personas allowed us to empathize with our users and shape features that aligned with their commuting goals, such as cost savings, community engagement, and eco-friendliness.
What did we specify for each persona?
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Basic Info: Age, job, location, education, and demographics.
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Technology Usage: Tech-savvy, frequent smartphone user, active on social media.
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Motivations & Goals: Reducing costs, community connection, eco-consciousness.
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Pain Points: Inconsistent service, complicated onboarding, limited features.
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User Journey
To ensure a seamless user experience, we crafted a user journey map for key interactions within the HopIn platform. The goal was to identify friction points and optimize the flow for better user engagement.
Path Selection: We chose to map the ride booking and ride-sharing process since these are core functions. This allowed us to focus on areas most crucial to user satisfaction and retention.
Testing and Validation: We used user interviews, real-time testing, and analytics data to validate the mapped journey. Feedback loops from early users helped confirm the map’s accuracy.
Findings from Journey Mapping: The map revealed pain points, such as confusion during ride selection and delays in booking confirmations. Two unnecessary steps were slowing down the process.
Main Pain Points:
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Unclear navigation between different ride options.
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Delays in matching drivers to riders.
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Lack of real-time notifications for ride statuses.
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Design Changes: We streamlined the ride booking flow by removing redundant steps and enhancing the matching process with real-time updates. These changes reduced friction and improved conversion rates, ensuring that users could book rides more quickly and confidently.
Low Fidelity Wireframes
Started with low-fidelity wireframes based on initial sketches and industry standards to outline the basic structure.
Tool Used
Utilized Figma to create and refine wireframes.
Testing & Feedback:
Conducted 4 user tests on low-fidelity wireframes. Gathered insights and identified necessary changes to enhance user flow and interface clarity.
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Iterations
Made a few alterations based on feedback, leading to improved wireframes before progressing to high-fidelity prototypes.
Final Stage
Added relevant stock images and marketing copies, then moved forward with high-fidelity prototypes for final user testing.
Brand Guidelines
Usability testing
I created a fully-functional, high-fidelity prototype of the new flows using Axure. At the same time, we started recruiting subjects for the test who fit our criteria. We did 4 usability tests in the first round and 3 after iterating on the issues that I’ve identified
