Journal of Computer Technology & Applications

Journal of Computer Technology & Applications Review Article

User App Segmentation for Better Understanding of Reviews and Password Resets Using Regression

  1. Parth Agrawal School of Technology, Management & Engineering, NMIMS University, Navi Mumbai
  2. Lishiv Sharma School of Technology, Management & Engineering, NMIMS University, Navi Mumbai
  3. Alaap Varma School of Technology, Management & Engineering, NMIMS University, Navi Mumbai

Abstract

In today’s digital landscape, understanding users’ needs and behaviors is key to improving app experiences. This paper focuses on how we can better understand user reviews and password resets in mobile apps through a method called user segmentation. By dividing users into groups based on their feedback and password reset patterns, we aim to uncover insights that can enhance app design and security. Using a mix of user reviews and password reset data from a diverse set of users, we analyze patterns using regression. This statistical method helps us identify different types of users and their preferences. Our findings reveal valuable insights into user sentiments and the likelihood of password resets. Ultimately, this research highlights the importance of tailoring app experiences to different user groups. By gaining insights into users’ needs and behaviors, we can develop applications that are more user-friendly and secure. This can be utilized for attaining market insights for any penetration in comparison to other existing clients and competitors. The method is a way out by just reading out the app’s data to form and display the required information. It also enables the institution to identify where they can target the audience they want to catch, if not they can amend changes to the same. Ensuring unbiased numeric values showcasing up reviews.

Keywords

  • App segmentation
  • SPSS
  • User reviews
  • multinomial logistic regression
  • password resets

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