Boosting the renter’s experience
Re-designing some funels on the app in order to improve the renter’s experience.
PRODUCT
Getaround App
DURATION
1 month
ROLE
Product designer/Product Manager
DELIVERABLES
User research,
Benchmark,
Data exploration,
Prototyping (both UX/UI),
User testing
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Getaround is a user-friendly peer-to-peer car rental app. It has two main type of users: car owners and renters.
Car owners can easily list their cars for renters to book and use. It's a simple way for people to share their vehicles and for others to find a convenient ride when needed.
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This project was conducted as part of the Noé program.
The project aimed at exploring why first time users and non first time users stop using the app. In other words, the exploration aimed at understanding why the churn rate was so high.
1. Getting started
Previous investigations highlighted a significant factor: the quality of the rented cars emerged as a key contributor to user churn.
Initial explorations revealed that users were prone to churning due to dissatisfaction with the quality of the rented cars. Specifically, renters often expressed disappointment with disparities between the car's condition listed on the app and its actual state.
To delve deeper into this issue, a second research plan was executed as part of the Noé program with a specific focus: How can we enhance the renter's experience concerning the physical quality of the booked cars to increase repeat usage?
For this project, we adopted the design thinking methodology as our guiding framework.
2. The research
The research was both qualitative and quantitative, involving stakeholder interviews, competitive analysis, and data investigation.
Before starting the research, we established clear goals to guide our exploration and understanding. The defined goals were as follows:
Identify the use cases:
Understand when and why users interact with the app.
Define our user personas:
Determine the demographics, behaviours and preferences of our users.
Assess the impact of car condition on churn:
Investigate whether the condition of the car significantly contributes to user churn.
Define user perception of "quality":
Explore and understand what users consider as "quality" in the context of the service.
This goal-oriented approach provided a structured framework for our research, ensuring that we proactively addressed key aspects relevant to user experience and potential reasons for churn.
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Quantitative analysis
We initiated the research by conducting a comprehensive analysis of the data in bulk. This analysis included examining ratings (both global and individual), user comments, and various demographics.
This approach not only enabled us to understand our users and their background but also shed light on the standards that signified “physical quality“ from their perspective.
3. Defining
After analyzing all the data gathered during the research we defined our objectives for the next steps.
After analyzing all the data collected during the research phase, we defined our objectives for the next step. The research allowed us to determine when users utilized the app and what factors were crucial to them when booking a car. With these insights in mind, we established our objectives for the upcoming phases—ideating and prototyping:
First objective: Enhance access to cleanliness information.
Second objective: Encourage users to maintain cars in the best possible condition.
Third objective: Improve the user's ability to report technical issues and prompt owners to address these concerns asap.
✍️ Results
The quantitative analysis provided us with an initial insight into our users' understanding of "physical quality" and its impact on the repeat rate. The key elements contributing to this understanding were:
Cleanliness of the vehicle.
Conformity of the vehicle compared to the ad.
Security of the vehicle, encompassing technical deficiencies that may arise during usage.
When combined, these three elements accounted for approximately a 3% impact on the overall repeat rate of our users. This finding underscores the importance of these aspects in influencing user satisfaction and subsequent usage patterns.
02
User interviews
We also conducted a total of 12 interviews with users of the app, further refining our understanding of user profiles and identifying the primary use cases in which the app proved valuable to them.
Additionally, these user interviews served to validate the findings from our data analysis, shedding light on the users' perception of "physical quality" when mentioned by them. The qualitative insights gained through these interviews complemented and enriched our quantitative data, providing a more holistic understanding of user perspectives.
✍️ Results
The interviews enabled us to pinpoint three primary use cases for which users engage with the app:
House moving.
Short trips (primarily on weekends).
Long trips (lasting over a week).
Moreover, we delved deeper into the concept of "physical quality" and discerned its significance to users:
Cleanliness comments emerged as a top priority, often being one of the initial aspects users scrutinize.
The security of the cars was universally regarded as a non-negotiable requirement, demanding impeccable standards.
Interestingly, conformity was not deemed a crucial factor by users, highlighting differing priorities in their assessment of "physical quality."
03
Competitive Analysis
Additionally, we conducted an extensive benchmarking exercise to explore how other products positioned themselves in the market and how they addressed similar issues. This comprehensive analysis of industry counterparts provided valuable insights into best practices, innovative solutions, and potential areas for differentiation within our product.
4. Prototypes
Once the objectives were set, I moved on the prototyping phase.
Using Figma, I began by prototyping wireframes, which were later followed by higher fidelity mock-ups.
5. User testing
Once the prototypes were completed, and the team was aligned, we conducted user tests.
Testing the prototype allowed us to identify areas for improvement. After collecting feedback, we iterated on the prototype and promptly cross-checked with some of our users, especially those who had highlighted areas for improvement during the tests.
6. Expected results
Before prototyping, we set some goal metrics in order to measure the success of the project.
The metrics we wanted to improve were:
Reducing the churn rate.
And subsequently, increasing the repeat rate from 67,5% to 75%.
🛑 ✋ That’s the end of the road!
To the next project?