Ratings to Insights: Navigating McDelivery Indonesia App Comment

Many widely-used applications, such as McDelivery Indonesia has rated 3,2 stars and +1 million download, accumulate over 22.7 thousand on their respective pages. Users openly share their experiences, whether positive or negative, through these comments. This presents a valuable resource for researchers, offering an alternative to traditional user research methods like interviews. By analyzing user comments, researchers can effectively evaluate and enhance app development.

First of all, use some tools for mining and analyst those data. In this case use Python with its packages, such as google-play-scraper, wordcloud, and Sastrawi, a word manipulation for Bahasa language.

However, considering the sheer volume of comments, it becomes overwhelming to discern meaningful insights. So, in this study case just get 3000 comments sorted by most relevant.

In the realm of product management, addressing user feedback is paramount to success. The Eisenhower Matrix, a prioritization framework, proves to be a helpful tool in navigating through this vast array of user feedback.

When confronted with a slew of 1-star ratings, it’s imperative to zero in on Quadrant 1 issues — those that are both urgent and important. These negative reviews can have a domino effect, potentially dissuading new users and harming the app’s reputation. By swiftly addressing critical issues highlighted in these reviews, developers can nip potential problems in the bud. To knowing the issues, analysts play a crucial role in this process. Their role becomes urgent due to the negative impact of 1-star ratings. If left unattended, these issues can snowball, prompting more users to express similar grievances. Analysts serve as the front line, swiftly identifying and addressing these urgent concerns.

Delving deeper, a wordcloud analysis adds a layer of insight. The bigger the word, the more frequently it appears in user comments. This size-based representation can guide developers to pinpoint the most significant pain points. Prioritizing the largest words in the wordcloud ensures that the most commonly mentioned issues are addressed promptly.

The most biggest word above like word mcd as brand name, order, alamat is address, daftar is register, susah masuk is difficult to login. Provide valuable information about user experiences with the McD app. Notably, issues with registration and difficulty logging in are highlighted. Additionally, when users place orders, attention should be given to the address section to ensure a seamless experience.

In conclusion, focusing on Quadrant 1 issues and leveraging the power of wordcloud analysis offers a proactive approach to user satisfaction. By swiftly addressing urgent and critical concerns highlighted in 1-star reviews, developers can not only salvage the app’s reputation but also demonstrate a commitment to continuous improvement.

Addressing these challenges head-on ensures that the app evolves in response to user feedback, fostering a positive user experience and setting the stage for improved ratings in the future.