UBER UXR CASE STUDY
UBER UXR CASE STUDY
Navigating the Uber Experience
A UX Case Study for New and Frequent Users
Project Overview
The purpose of this case study was to conduct a usability evaluation of the Uber app, specifically focusing on the experiences of new and frequent users. Given Uber's dual functionality as both a ride-hailing and food delivery service, our goal was to explore how intuitive and seamless the app experience was for different user types. We focused on users’ onboarding, task flows, and overall ease of use, with insights collected to suggest improvements that enhance user satisfaction.
We decided to evaluate the Uber app because it’s a big part of how people get around and order food in their daily lives. Our goal was to dive into user experiences, pinpoint what works well, and identify areas that could use improvement. As product designers, we were really curious about how apps like Uber strike a balance between functionality and user engagement in a competitive landscape. This evaluation gives us the chance to apply our design skills and gather valuable insights for future user-centered designs.
Why Uber?
Research Goals
The core objective of this study was to understand the Uber app experience for new and frequent users across multiple interactions. We aimed to answer the following questions:
New Users
To understand the onboarding experience
Explore how new users perceive the sign-up and first ride processes
Are they easily able to navigate and use the app?
Identify any confusion, challenges, or technical issues that new users face
What might encourage them to continue using Uber
Discover why new users choose Uber for their first ride
Frequent Users
Explore how users feel about the app’s functionality over time
What features keep them engaged?
Understand how users adapt to and perceive changes
Are these changes seen as improvements or obstacles?
How users respond to personalized features, ride suggestions and promotions.
What motivates them to stick with Uber?
Research Methodology
User Interviews
Getting the Real Story from Real Users
Think Aloud Sessions
Observing Users in Real Time
Calculating System Usability Scale Scores
SUS Assesment
Target Participants
People who have just started using the Uber application, and are relatively new to the experience of booking rides/ ordering food from Uber. (Experience Level <= 2 months)
New Users
Why were they chosen?
New Users offer a fresh perspective on the app. They can specify any challenges they face in the beginning during the onboarding stages & during their initial experience with the app.
People who have moderate experience with the application i.e people who often book rides/ order food using Uber.
Frequent Users
Frequent Users who’ve used the app for quite some time, would know how the app works and can point out the good and bad aspects of it. They can also highlight what exactly makes them use the app more often.
Why were they chosen?
Phase 1:
User Interviews
Interviews
We asked users about the frequency with which they used the app, any specific rides or food orders they remembered, and what features they loved or wished were different. We collected data in two ways: we took notes and also recorded audio (with participants’ permission), which we later transcribed. This process allowed us to capture users’ exact words, making it easier to identify recurring themes and insights.
Analysis Process
The analysis followed a structured process using affinity diagrams to categorize and visualize user feedback. After transcribing interviews, key themes, pain points, and opportunities were identified. This analysis focused on pain points, what works well, likes and dislikes, and user satisfaction.
Affinity Mapping
This stage helped us identify the most common issues and pinpoint the specific features that users wanted to be enhanced. It also confirmed a clear distinction between the needs of new and frequent users, which guided our next steps.
User Personas
The analysis followed a structured process using affinity diagrams to categorize and visualize user feedback. After transcribing interviews, key themes, pain points, and opportunities were identified. This analysis focused on pain points, what works well, likes and dislikes, and user satisfaction.
New User
Frequent User
Key Findings
Our user interviews gave us a deeper look into what shapes the experiences of new and frequent Uber users, revealing the key factors that matter most to them.
Food Ordering
New users tried Uber Eats for promotions or recommendations. While they found the ordering process simple, slow app load times and unclear restaurant reviews were common pain points.
Frequent users opted for Uber Eats for its promotions and ease of reordering past meals. They praised the tracking system but mentioned frustrations with unresponsive drivers and customer support during order delays.
Ride Booking
New users appreciated the introductory onboarding tutorials and found the sign-up process easy but took time to get accustomed to the different ride options and app layout.
Frequent users value Uber's convenience, availability of vehicle options, and accurate ETA features. However, they expressed concerns about surge pricing and unreliable drivers. Users preferred Uber due to smoother app functionality and more options.
Think-Aloud Sessions
Phase 2:
Tasks Given
To deepen our understanding, we set up think-aloud sessions where participants could walk us through their thought process as they used the app. This allowed us to observe firsthand where they encountered friction and what went smoothly. We conducted in-person observations during which participants were asked to complete tasks while verbalizing their thoughts. These sessions were recorded using both screen and audio capture to analyze the users' interactions with the app and observe their navigation patterns. For data collection, we utilized audio recordings, video footage, and notes.
Task 1: Ordering a Pizza
We asked participants to order a pizza with extra pepperoni from an Italian restaurant, apply any available discounts, and prioritize the shortest ETA. This task helped us understand how users handle customization, locate coupons, and prioritize delivery times.
Task 2: Booking a Ride
We asked participants to book an UberXL to an unfamiliar location, with a pickup time set 30 minutes ahead. This task let us understand how users navigate scheduling options and their comfort level with various ride choices.
Analysis Process
After the think-aloud sessions, insights from the observations were gathered from all team members in the FigJam file. Common issues identified were eliminated, and the remaining observed issues were evaluated to analyze recurring challenges. Following a discussion on the criteria for defining success and failure, we calculated the probability of detection for the problems identified by our team, using a set of five raters.
Issues/Problems Observed
The next step was to analyze the interview data. From the data obtained, These were the common usability issues and problems that were faced by participants.
Common Codes/Themes
We identified common codes and themes to categorize related issues, focusing on aspects like the onboarding experience, ease of use, tracking and safety, technical performance, positive/negative factors, and motivations for continued use. This categorization allowed us to structure the data for more in-depth analysis.
Probability of Detecting Issues
Confidence Interval
During a think-aloud session 4 out of 4 (100 %) users successfully completed Task 1 and 4 out of 4 (100 %) users successfully completed Task 2.
We can be 95% confident that the actual population completion rate is between 54.34% and 83.33%, with small chance the completion rate is below 50%.
SUS Assesment
Phase 3:
After completing the tasks, participants filled out a System Usability Scale (SUS) survey to rate their experiences. The SUS included ten statements rated on a 5-point Likert scale, with scores ranging from 1 (strongly disagree) to 5 (strongly agree). Collected data was input into a Google Sheets document, where the SUS scores were calculated by adjusting raw ratings, summing them, and multiplying by 2.5 to produce a final usability score ranging from 0 to 100. Higher scores indicated better-perceived usability. The SUS results provided insights into user perceptions and identified key areas for improving the application experience.
Overall average SUS score: 76.625 ≈ 77
Calculating for 95% Confidence Interval
The t value for 95% confidence interval with 7 degree of freedom : 2.365
The margin of error is : 17.75
The confidence interval = mean ± t x margin of error
Lower bound = 77 2.365 x 17.75 = 35.02
Upper bound = 77+2.365 x 17.75 = 118.97
So the 95% confidence interval is 35.022 to 118.97
Interpretation
The product scored an average of 76.6 on the SUS, well above the industry benchmark of 68, which points to it being user-friendly and effective in meeting users’ needs.
The 95% confidence interval of 35.0 to 119.0 means we’re quite sure the true average usability score is somewhere within this range. This suggests that the system likely has minimal usability issues overall.
Findings
Our findings revealed several areas where Uber’s usability could be improved, particularly in terms of navigation between ride hailing and food delivery functions. New users expressed confusion about locating specific features and navigating between the two main services, often describing the interface as overwhelming due to its dense layout and multiple layers of menus. In our think-aloud sessions, we observed that participants tended to spend extra time searching for order status information or upcoming ride details, which created minor frustrations during their interactions. Frequent users demonstrated familiarity with the app’s features but mentioned that certain repetitive actions, such as confirming pickup locations or customizing orders, felt unnecessarily tedious, which impacted their overall experience. Analysis of SUS (System Usability Scale) scores further validated these observations, as both user groups rated the app’s usability below the industry standard for seamless multi service platforms.
Recommendations
To enhance Uber's usability, we recommend simplifying the interface by creating a clearer separation between ride-hailing and food delivery services. A central dashboard with easily accessible tabs and a persistent bottom navigation bar for core features like order tracking, ride status, and account settings can reduce confusion and improve efficiency. Customizable shortcuts for frequent tasks, such as reordering meals or setting pickup points, would further enhance user convenience.
For new users, brief tooltips or guided walkthroughs during onboarding can boost confidence and ease navigation. Finally, conducting usability testing and A/B testing on streamlined layouts will help refine these improvements based on real-world feedback, ensuring a seamless and intuitive user experience.
Collaborators
Ariel
Shubhangi
Shradda