On April 13, 2026, T.S. Lim of Leap Research set out to show how QInsights can support thematic analysis of user reviews using Permata ME as a case study. Permata ME, the Android mobile banking app by Permata Bank, was rated only 2.5 stars out of 5 on the Google Play Store. The rating made the dissatisfaction visible, but it did not explain the specific pain points behind it. For Permata Bank, the useful question was not simply whether users were unhappy. It was which issues were driving that unhappiness strongly enough to damage trust in the mobile banking experience.
The Challenge
Leap Research extracted the 1,000 newest Google Play comments, spanning January 17 to April 10, 2026, and reviewed a sample of the raw comments before processing the full dataset. The low star rating signaled a problem, but star ratings alone do not tell product teams what to fix. The real task was to move from a number to a diagnosis.
Users are not rejecting Permata ME as a concept. They are rejecting its unreliability.
— T.S. Lim, Managing Partner, Leap Research
The Solution
After uploading the CSV file to QInsights, the team used Guided Conversational Analysis to create positive and negative themes based on the comments only, ignoring the star ratings. The prompt asked QInsights to output themes in a table with theme name, description, three sample verbatims, actual count, and sentiment, creating a structured bridge between raw user voice and product decision-making.
The key analytical insight is that over 70% of all comments are concentrated in just three themes: performance slowness, system errors, and login/authentication failures.
— T.S. Lim, Leap Research
The Impact
QInsights showed that more than 70% of all comments were concentrated in just three themes: app slowness and poor performance, system errors, and login, password, and OTP problems. Positive feedback existed, but it was fragmented and numerically small. That pattern suggested that user dissatisfaction was being driven by a few structural reliability issues rather than by broad rejection of the app concept or a lack of features.
The strategic takeaway was clear: the fastest way to recover trust is not new features or rebranding, but making the app stable. Once core reliability is fixed, the existing feature set already positions the app competitively.
For this kind of analysis, QInsights functions not just as an analytics tool, but as a qualitative reasoning layer between raw user voice and product decision-making.
— T.S. Lim, Leap Research
Key Outcomes
- Analyzed 1,000 newest Google Play comments from January 17 to April 10, 2026
- Found that over 70% of comments clustered around three reliability themes
- Turned a low app rating into a concrete product recovery strategy
Metrics
- 2.5-star Android rating
- 1,000 newest user comments analyzed
- Over 70% of comments concentrated in three themes