Roadie
A Multi-App Solution For Gig Drivers
Role
User Experience Designer
Duration
Aug - Dec 2024
Responsibilities
Mobile Software Design,
User Research/Validation,
Visual Development and Tests
Team
2 UX Designers, 1 UX Researcher,
1 Product Manager, 4 Sponsor
Company Instructors
Background.
The why
In the Fall semester of 2024, I had the exciting opportunity to collaborate with Roadie as part of the User Research collaborative program at Georgia Tech MS-HCI. During the progress, I had a fantastic experience working on a real-world project under the instruction of 4 industry advisors within a group of 4 people.
Overview.
Why do we need this product?
Roadie has long been a great platform connecting local drivers with businesses and individuals for same-day deliveries across the U.S. By leveraging a network of everyday drivers, Roadie provides efficient and flexible shipping solutions for a wide range of customers, from small businesses to large enterprises.
Our project explores the possibility of extending Roadie’s services into the multi-app gig economy, specifically focusing on food delivery. This expansion could not only generate new revenue streams but also optimize driver utilization and position Roadie as a key player in the competitive food delivery landscape.
Interactive Dashboard
Allows user to easily access information for orders from multiple platforms.
A Giant Leap for Gig Drivers to Earn.
Our product for Roadie enables a smooth and effective multi-app gigging process for gig drivers. Drivers can unlock their hands and boost their earnings.
Primary Dashboard
Profile Dashboard
History Dashboard
Drive and Gain Impact.
We provide service beyond a delivery platform. With the reputable UPS and Roadie delivery service, we enable drivers to support the mutual development of the community.
Drive and Deliver
Share and Report
The core value of our product is human-centric research.
The key challenge we faced was engaging with the users. Unlike traditional academic or business contexts, recruiting gig drivers who rely on this work full-time is particularly difficult. Conducting interviews or usability tests with them proved even harder. Additionally, there are very few, if any, leading apps in the market to serve as benchmarks. Although the Roadie team had product designers and UX researchers who offered feedback, they themselves were uncertain about how to approach this type of research.
Our Process
1
Research
2
Synthesis
3
Ideation
4
Design
5
Reflection
Interviews
Precedent Analysis
Social Media Mining
Ethnography
User Personas
Storyboards
Empathy Maps
Affinity Diagramming
Brainstorming
Wireframing
Prototyping
Design System
Visual Interface
Heuristic Evaluation
Usability Testing
“How might we provide a multi-gigging service for drivers to maximize their working productivity without harming the right and benefits of the users?”
Problem Statement
Digital technologies, such as smartphones and the internet, have transformed industries like food delivery. What began with direct restaurant orders has evolved into online platforms, with apps like Postmates and ridesharing services like Uber shaping the gig economy. While these platforms offer flexibility, gig workers often struggle to efficiently manage multiple jobs across different apps, leading to inefficiencies and safety concerns. Our project seeks to develop a solution to help drivers better manage multiple gigs, improving efficiency and earnings.
2 Research Focuses
Given that the problem lies within an unfamiliar domain, our research goal became more complex: we needed to not only understand the pain points drivers face but also gain insight into what an ideal gig driving app should look like.
To gain enough base knowledge and identify how a driver's app would work, we applied precedent analysis and ethnographic research on prominent delivery driver's apps in the market.
To thoroughly analyze and understand drivers' pain points on their driving applications, we conducted social media mining and semi-structured interviews after a round of recruitment.
Focus 1: How might we create a smooth and nuanced tool for gig drivers to ensure their work efficiency?
We have claimed high level user flows from prominent products with a precedent analysis and tested their viability through ethnographic research.
Besides, I also conducted ethnographic research aiming to uncover real-world challenges by using UberEats, DoorDash, and Roadie. Through 40+ delivery attempts and insights, I gained a deeper understanding of driver challenges, including time management, route optimization, and income-maximizing strategies.
The thorough data cloud of 11 prominent apps were abstracted from online app stores or forums such as reddit and iOS AppStore. Extra case studies were also conducted for each app in order for us to gain insights on their subtle varied target groups.
The precedent analysis had established a great foundation for our design process. We also analyzed the pros and cons comparatively for these apps to mark as reference.
We discovered a huge demand of efficiency gap in between different strategies of gigging. The highest hourly income was incredibly high as $49. This indicates a huge potential for our product if it could help the drivers to construct and refine their own strategies.
Focus 2: How might we determine the pain points from in-depth connection with the target users?
We have interviewed users from online forums to further comprehend their experiences, meanwhile collecting their complaints via social media mining.
We had combined the findings from the interviews with the notes abstracted from the social media mining together into an affinity diagram, showcasing higher level pain points and serving as a theoretical foundation for the further progress of our project.
We separated the group into two to better manage the interview and social media mining progress. We recruited 6+ users from Reddit and conducted separate interviews with them regarding the current issues and experience during their gigging process.
We were curious on how they behave when making specific decisions and their distinct working strategies. Questions such as "What is the worst part…?", "Which factor you will value most…" were asked in order to gain direct insights from the target group.
After summarizing the notes taken from the previous research progress, we performed a synthesis session, turning the high-level research pain points into design requirements.
We created specific visualizations including user personas, empathy maps and storyboards to further elaborate our understanding on the key needs of the gig workers. These studies helped us to better manage the information that we had and leveraged the completeness of design requirements.
We determined 4 key design requirements in our research topic, serving as a parameter for our further design.
Efficiency and Ease of Use
Users are seeking improvement and unification on the base features of delivery apps. A higher regulated interface will boost their working efficiency.
Income Transparency and Optimization
Users want to have more accurate estimation and summary of earnings.
Driver Rights and Flexibility
Users want a fair way to maintain their rights and be able to customize their working conditions.
Safety and Lower Cognitive Load
Users need a efficient tool to maintain their attention on the road.
Summarization of ideation
Paper Prototyping and Wireframing.
By creating a series of brainstorming and crazy 8s, we visualized several critical pain points with a viability concern from the product aspect. We then analyzed these ideas comparatively on their technical and business feasibility.
We circled out functions based on a mutual access from a central dashboard. Side functions. such as gig filters, order history, and voice assistant, are then determined as extra features on the skeleton of a classic gig-driving app.
To ensure the possibility of shipment, we gathered feedback from the Roadie UX team and selected 4-6 core feature flows from the previous ideation.
We established a wireframe for the ideal workflow and started transferring paper sketches into digital prototypes. Feedback was obtained in each step when we interviewed the participants in different levels of familiarity of our progress.
DESIGN CODES! (pending to be made)
Show core features
Evaluation
Takeaways