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 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.
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.
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.
User Persona - Frequent User
Phase 2 : Think Aloud Sessions
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.
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.
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.
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.
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
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%.
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
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.
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.
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.
