Freelance Life
Restructuring the Five Star Rating System
Project - Course work
Duration - 4 weeks
Role - Secondary Research, User Interviews, Affinity Mapping, Mid-fi prototypes, A/B Testing
Tools - Figma
Evaluating the applicability of the conventional five-star review system in the context of car-hailing services, this project aims to restructure the rating system employed by ride-sharing platform, Uber.
Problem Statement
The current state of the Uber app features a basic five-star rating system and the platform deactivates the driver’s profile temporarily if their ratings go below 4.6. Currently, the user’s mental model differs from Uber’s business model in terms of rating. The users believe the 4 star to be a good rating, however, Uber's statistics show otherwise.
Objectives
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How might we create a fair rating system which is not perceptive but indicative of the actual riding/driving experience?
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How might we create an experience which will lead to ratings which are descriptive?
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How might we design for inclusivity and fairness, on the part of the driver as well as the rider?
Solution
Research Methodology
Secondary Research
Interviews with riders and drivers
Affinity Mapping
Comparative Analysis
Secondary Research
Understanding the Context
It has been evident through evaluation of numerous rating systems, that five star ratings have become less useful, as most of the products or services are either rated 1 which is harmful for the user as well as the service provider, or 5 which is becoming increasingly positive (Briglia, 2021).
The prevalence of high ratings, in the range of 4-5 stars, have created a perception that anything less than that is unfavorable. This creates an unrealistic expectation from the products/services, which leads to lack of transparency and genuine feedback within the rating system.
User Interviews
We conducted interviews with three drivers and four riders, as both of them were our user group. But due to time constraints, we decided to focus our insights on riders going forward.
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Users give bad reviews for sanitation issues or driver behavior.
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Users have to cancel rides if they have luggage, however, the drivers are accommodative most of the time.
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Gender was a concern with regards to female users who want to travel long distances.
Competitive Analysis
Rating systems are crucial for trust and safety in ride-hailing platforms like Uber, Lyft, Ola, and Grab. Here's a comparative analysis of their key features:
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All platforms use a 1-5 star system with additional options like "thumbs up" or "thumbs down" in Lyft and Grab.
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Newer reviews hold more weight in Uber and Lyft.
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Lyft offers "streaks" for maintaining high ratings. Ola and Grab have limited driver incentives.
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All platforms offer limited avenues for disputing unfair ratings. Lyft offers a more detailed appeal process.
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Uber shows specific reasons for low ratings to drivers. Lyft and Grab are less transparent.
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Lyft uses "match bonuses" to connect riders with well-rated drivers.
Ideation
We brainstormed few ideas based on the interviews and focused on these three aspects in the app.
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Enhanced pairing system
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Streamlined Rating System
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Freelancer Feedback and Growth System
Mid-Fidelity Prototypes
We also worked on three intermediate solutions for the ratings systems and included that in our mid-fi prototypes.​
Thumbs up & Thumbs Down
Slider System (NPS)
Descriptive five stars
First User Testing
We conducted a A/B testing with a total of 3 participants (1 male, 2 female) based on our intermediate wireframes. All 3 were students of Purdue University (2 graduates, 1 undergrad). We used the think-out-loud technique to note their observations.
Based on the testing result, we found out that Solution A (Slider system) incorporated with the NPS score system is most effective for the participants to rate their drivers. It was preferred by 2 participants out of 3. They said the visualized scale is easy to understand. 1 participant preferred Solution B (Descriptive Stars). So we went forward with Solution A (Slider with NPS Score System) in the Hi-fi Prototype.
Slider System - Net Promoter Scale (NPS)
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After the testing we went ahead with introducing the slider-based Net Promoter Score (NPS) system to Uber's rating process, which is driven by a user-centric approach to gather more accurate and nuanced feedback.
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Solution
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The users will have an option to choose their preference.
There is an option to customize their luggage, number of pets and number of passengers.
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Users are provided the option to choose the cabs which are provided as per their preferences.
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Users then rate the ride with NPS which is a User friendly and intuitive design choice.
It simplifies the rating process by allowing riders to select their satisfaction level through a single, easy-to-use interface.
User Testing
With the conducted a remote usability test with 3 participants, individually, using the hi-fi prototypes.
The participant group included 1 male and 2 female from diverse backgrounds in order to gain varied perspectives for the app feedback.
Testing Results:
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Users found the colors of scale to be helpful in indicating their experience.
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Users voiced that the scale was intuitive to use as the emoticons also helped them understand what the rating would be like.
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The users did not have feedback regarding the snapping of the scale.
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Users said they would not use the description option only when they have a bad experience.