WashPay is a mobile payment application for laundry. It helps the users pay for their laundry with the help of a mobile app instead of using coins or cards.
WashPay was not engaging enough thus leading to increased negative feedback and poor ratings on the mobile payment application that negatively impacted the ROI.
Research and understand user’s pain points that prevent them from performing their tasks seamlessly and revamp the existing design with a focus on increasing the customer satisfaction rate by 50%.
I wanted to understand the user's perspective of the application including their pain points. Hence, I started analysing the reviews from Google and App Store to gather insights.
From the reviews I understood most users faced difficulties to complete a workflow that resulted in poor ratings to the application.
I collected poorly rated reviews from Play and App Store and identified all the possible issues reported.
To verify if the user reported issues were real, I conducted a survey using Google Forms with the actual users of the WasPay app.
Surveyed 15 participants and found the following :
Once the user survey was done, the existing application was tested with 5 participants. Usability testing was conducted to understand the impact of each issue identified during the survey and also to know whether these issues were actual issues to the users or not.
Participants were given 3 scenarios and 3 tasks under each scenario to understand users’ opinions and feedback on the website.
5 participants' behaviors were analysed while performing the task of starting a machine from start till end. Their behaviors and emotions were tracked to understand their pain points as they perform the actual task.
Issues that had severe impact in users:
Competitor Analysis was done to identify what distinguishable features are being provided. Identifying these distinguishable feature and implementing them into the WashPay app will help in increasing the retention rate.
With the insights from the survey, personas were created. Personas were classified into 2 categories based on the frequency of doing laundry. This is because users doing laundry weekly once had different pain points compared to those who do laundry bi-weekly.
Once the initial high fidelity designs were created, it was once again tested with 5 participants. Participants were given 3 scenarios and 3 tasks under each scenario which were the same as the previous usability test.
User behaviours and feedback were collected. With the help of feedback collected, necessary design iterations were made to make the product desirable and user-friendly for the users.
Users felt difficulty to identify the available machines while being at home which was a serious issue that made users get frustrated easily. This was a major cause of poor retention rate.
Quick QR code scan to start a machine, providing option for adding custom funds to wallet and letting users know the available machines while being at home helped users in completing the laundry process easily and eventually helped to improve the retention rate of the application.
