User App Segmentation for better understanding of reviews and password resets Using Regression

Year : 2024 | Volume :15 | Issue : 03 | Page : –
By

Parth Agrawal,

Lishiv Sharma,

Alaap Varma,

  1. Student, School of Technology, Management & Engineering, (STME), NMIMS University, Navi Mumbai, Maharashtra, India
  2. Student, School of Technology, Management & Engineering, STME, NMIMS University, Navi Mumbai, Maharashtra, India
  3. Student, School of Technology, Management & Engineering, STME, NMIMS University, Navi Mumbai, Maharashtra, India

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 with comparison to other existing clients and competitors. The method is a way out by just reading out app’s own data to form and display required information needed. It also enables the institution to identify were they able to target the audience they wanted to catch, if not they can amend changes to the same. Ensuring a unbiased numeric values showcasing up reviews.

Keywords: User Reviews, App segmentation, Password Resets, Multinomial Logistic Regression, SPSS

[This article belongs to Journal of Computer Technology & Applications (jocta)]

How to cite this article:
Parth Agrawal, Lishiv Sharma, Alaap Varma. User App Segmentation for better understanding of reviews and password resets Using Regression. Journal of Computer Technology & Applications. 2024; 15(03):-.
How to cite this URL:
Parth Agrawal, Lishiv Sharma, Alaap Varma. User App Segmentation for better understanding of reviews and password resets Using Regression. Journal of Computer Technology & Applications. 2024; 15(03):-. Available from: https://journals.stmjournals.com/jocta/article=2024/view=171882



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Regular Issue Subscription Review Article
Volume 15
Issue 03
Received June 28, 2024
Accepted August 1, 2024
Published September 12, 2024
Views: 34

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