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

Year : 2024 | Volume : 15 | Issue : 03 | Page : 1 9
    By

    Parth Agrawal,

  • Lishiv Sharma,

  • Alaap Varma,

  1. Student, School of Technology, Management & Engineering, NMIMS University, Navi Mumbai, Maharashtra, India
  2. Student, School of Technology, Management & Engineering, NMIMS University, Navi Mumbai, Maharashtra, India
  3. Student, School of Technology, Management & Engineering, 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 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: User reviews, App segmentation, password resets, multinomial logistic regression, SPSS

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

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):1-9.
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):1-9. Available from: https://journals.stmjournals.com/jocta/article=2024/view=171882


References

  1. Lee Y, Cho S. User segmentation via interpretable user representation and relative similarity-based segmentation method. Multimedia Systems. 2021;27(10):61–72.
  2. Roux M. A comparative study of divisive and agglomerative hierarchical clustering algorithms. J Classif. 2018;35:345–366. DOI: 10.1007/s00357-018-9259-9.
  3. Chan CCH. Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer. Expert Syst Appl. 2008;34:2754–2762. DOI: 10.1016/j.eswa.2007.05.043.
  4. Christy AJ, Umamakeswari A, Priyatharsini L, Neyaa A. RFM ranking – An effective approach to customer segmentation. J King Saud Univ Comput Inf Sci. 2021;33:1251–1257. DOI: 10.1016/j.jksuci.2018.09.004.
  5. Chen X, Fang Y, Yang M, Nie F, Zhao Z, Huang JZ. Purtreeclust: A clustering algorithm for customer segmentation from massive customer transaction data. IEEE Trans Knowl Data Eng. 2018;30:559–572. DOI: 10.1109/TKDE.2017.2763620.
  6. Böttcher M, Spott M, Nauck D, Kruse R. Mining changing customer segments in dynamic markets. Expert Syst Appl. 2009;36:155–164. DOI: 10.1016/j.eswa.2007.09.006.
  7. Dixit S, Bhamare A, Darpel A. Customer segmentation using machine learning. 2021.
  8. Bian J, Dong A, He X, Reddy S, Chang Y. User action interpretation for online content optimization. IEEE Trans Knowl Data Eng. 2013;25:2161–2174. DOI: 10.1109/TKDE.2012.130.
  9. Banduni AM, Ilavendhan A. Customer segmentation using machine learning. Int J Innov Res Technol. 2020;7(2):116–22.
  10. Ben Ayed A, Ben Halima M, Alimi AM. Survey on clustering methods: Towards fuzzy clustering for big data. In: Proceedings of the 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR). 2014. p. 331–336. DOI: 10.1109/SOCPAR.2014.7008028.
  11. Burri M, Schär R. The reform of the EU data protection framework: Outlining key changes and assessing their fitness for a data-driven economy. J Inf Policy. 2016;6:479–511. DOI: 10.5325/jinfopoli.6.2016.0479.
  12. Koziel AM, Shen CW. Psychographic and demographic segmentation and customer profiling in mobile fintech services. Kybernetes. 2023. DOI: 10.1108/K-07-2023-1251.
  13. Birtolo C, Diessa V, De Chiara D, Ritrovato P. Customer churn detection system: Identifying customers who wish to leave a merchant. In: Proceedings of the International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems. 2013. p. 411–420.
  14. Tiwari R, Saxena MK, Mehendiratta P, Vatsa K, Srivastava S, Gera R. Market segmentation using supervised and unsupervised learning techniques for e-commerce applications. J Intell Fuzzy Syst. 2018;35:5353–5363. DOI: 10.3233/JIFS-169818.
  15. Chalupa S, Petricek M. Understanding customer’s online booking intentions using hotel big data analysis. J Vacat Mark. 2024;30:110–122. DOI: 10.1177/13567667221122107.
  16. Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Comput Geosci. 1984;10:191–203. DOI: 10.1016/0098-3004(84)90020-7.
  17. Barman D, Chowdhury N. A novel approach for the customer segmentation using clustering through self-organizing map. Int J Bus Anal. 2019;6:23–45. DOI: 10.4018/IJBAN.2019040102.
  18. Golmes MA. A review on customer segmentation methods for personalized customer targeting in e-commerce use cases. 2023.
  19. Chan CCH, Cheng CB, Hsien WC. Pricing and promotion strategies of an online shop based on customer segmentation and multiple objective decision making. Expert Syst Appl. 2011;38:14585–14591. DOI: 10.1016/j.eswa.2011.05.024.
  20. Chang HC, Tsai HP. Group RFM analysis as a novel framework to discover better customer consumption behavior. Expert Syst Appl. 2011;38:14499–14513. DOI: 10.1016/j.eswa.2011.05.034.
  21. Chan CCH, Hwang YR, Wu HC. Marketing segmentation using the particle swarm optimization algorithm: A case study. J Ambient Intell Human Comput. 2016;7:855–863. DOI: 10.1007/s12652-016-0389-9.
  22. Stocchi L, Pourazad N, Michaelidou N, Tanusondjaja A, Harrigan P. Marketing research on mobile apps: Past, present and future. J Acad Mark Sci. 2022;50:195–225. DOI: 10.1007/s11747-021-00815-w. PubMed: 34776554.
  23. Brito PQ, Soares C, Almeida S, Monte A, Byvoet M. Customer segmentation in a large database of an online customized fashion business. Robot Comput Integr Manuf. 2015;36:93–100. DOI: 10.1016/j.rcim.2014.12.014.
  24. Kasem MS, Hamada M, Taj-Eddin I. Customer profiling, segmentation, and sales prediction using AI in direct marketing. Neural Comput Appl. 2024;36:4995–5005. DOI: 10.1007/s00521-023-09339-6.
  25. Chen X, Sun W, Wang B, Li Z, Wang X, Ye Y. Spectral clustering of customer transaction data with a two-level subspace weighting method. IEEE Trans Cybern. 2019;49:3230–3241. DOI: 10.1109/TCYB.2018.2836804. PubMed: 29994344.
  26. Pitka T, Bucko J. Segmenting customers with data analytics tools: Understanding and engaging target audiences. Acta Inform Pragensia. 2023;12:357–378. DOI: 10.18267/j.aip.220.
  27. Bellini P, Palesi LAI, Nesi P, Pantaleo G. Multi clustering recommendation system for fashion retail. Multimed Tools Appl. 2023;82:9989–10016. DOI: 10.1007/s11042-021-11837-5. PubMed: 35043044.
Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 28/06/2024
Accepted 01/08/2024
Published 12/09/2024
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