The Role of Data Analytics in Enhancing Digital Platform User Engagement and Retention
Keywords:
data analytics, user engagement, recommendation systems, predictive analytics, A/B testingAbstract
This review explores the role of data analytics in enhancing user engagement and retention on digital platforms. With the rapid growth of digital platforms, user engagement has become a key factor in ensuring sustained success and long-term growth. Data-driven strategies, such as personalized content, recommendation systems, predictive analytics, and A/B testing, have proven to be effective tools in increasing user interaction and reducing churn. This paper examines the various data analytics techniques used to optimize user experiences, as well as the challenges faced by platforms, including ethical concerns regarding data privacy and the need for cross-cultural applicability. The review concludes with a call for interdisciplinary collaboration in the development of sustainable data governance practices to ensure the ethical use of user data while continuing to improve user engagement and retention.
References
1. S. Thapa, S. Panda, A. Ghimire, and D. J. Kim, “From engagement to retention: unveiling factors driving user engagement and continued usage of mobile trading apps,” Decis. Support Syst., vol. 183, p. 114265, 2024, doi: 10.1016/j.dss.2024.114265.
2. K. Jansukpum, S. Chernbumroong, K. Intawong, P. Sureephong, and K. Puritat, “Gamified virtual reality for library services: the effect of gamification on enhancing knowledge retention and user engagement,” New Rev. Acad. Librariansh., vol. 31, no. 1, pp. 27–47, 2025, doi: 10.1080/13614533.2024.2381509.
3. E. M. Boucher and J. S. Raiker, “Engagement and retention in digital mental health interventions: a narrative review,” BMC Digit. Health, vol. 2, no. 1, p. 52, 2024, doi: 10.1186/s44247-024-00105-9.
4. Z. Liu, S. Liu, Z. Zhang, Q. Cai, X. Zhao, K. Zhao, et al., “Sequential recommendation for optimizing both immediate feedback and long-term retention,” in Proc. 47th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2024, pp. 1872–1882, doi: 10.1145/3626772.3657829.
5. R. Monachelli, S. W. Davis, A. Barnard, M. Longmire, J. P. Docherty, and I. Oakley-Girvan, “Designing mHealth apps to incorporate evidence-based techniques for prolonging user engagement,” Interact. J. Med. Res., vol. 13, no. 1, p. e51974, 2024, doi: 10.2196/51974.
6. S. Limpanopparat, E. Gibson, and A. Harris, “User engagement, attitudes, and the effectiveness of chatbots as a mental health intervention: A systematic review,” Comput. Hum. Behav. Artif. Humans, p. 100081, 2024, doi: 10.1016/j.chbah.2024.100081.
7. A. Capatina, D. Juarez-Varon, A. Micu, and A. E. Micu, “Leveling up in corporate training: unveiling the power of gamifi-cation to enhance knowledge retention, knowledge sharing, and job performance,” J. Innov. Knowl., vol. 9, no. 3, p. 100530, 2024, doi: 10.1016/j.jik.2024.100530.
8. T. Q. A. Ho, L. K. D. Le, L. Engel, N. Le, G. Melvin, H. N. Le, and C. Mihalopoulos, “Barriers to and facilitators of user engagement with web-based mental health interventions in young people: a systematic review,” Eur. Child Adolesc. Psychiatry, vol. 34, no. 1, pp. 83–100, 2025, doi: 10.1007/s00787-024-02386-x.
9. N. Shamim, S. Gupta, and M. M. Shin, “Evaluating user engagement via metaverse environment through immersive expe-rience for travel and tourism websites,” Int. J. Contemp. Hosp. Manag., 2024, doi: 10.1108/IJCHM-10-2023-1590.
10. S. Majumder, “The influence of UX design on user retention and conversion rates in mobile apps,” arXiv e-prints, arXiv:2501.13407, 2025, doi: 10.48550/arXiv.2501.13407.
11. M. R. Kumar, S. Vishnu, G. Roshen, D. N. Kumar, P. Revathi, and D. R. L. Baster, “Product recommendation using collab-orative filtering and k-means clustering,” in Proc. 2024 IEEE Int. Conf. Comput., Power Commun. Technol. (IC2PCT), vol. 5, pp. 1722–1728, 2024, doi: 10.1109/IC2PCT60090.2024.10486625.
12. Y. Chen and J. Huang, “Effective content recommendation in new media: Leveraging algorithmic approaches,” IEEE Access, vol. 12, pp. 90561-90570, 2024, doi: 10.1109/ACCESS.2024.3421566.
13. C. Yu, H. Lin, W. Dong, S. Fang, Q. Yuan, and C. Yang, “TripChain2RecDeepSurv: A novel framework to predict transit users’ lifecycle behavior status transitions for user management,” Transp. Res. Part C: Emerg. Technol., vol. 167, p. 104818, 2024, doi: 10.1016/j.trc.2024.104818.
