Predicting User Engagement in Mobile Applications Using Machine Learning: Insights for Optimizing App Design and Retention

R.  Sivakani; G. Rajasekaran; S. Manimaran; M. Mohamed Sameer Ali; S. Suman Rajest; R. Regin

R. Sivakani Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
G. Rajasekaran Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
S. Manimaran Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
M. Mohamed Sameer Ali Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
S. Suman Rajest Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
R. Regin SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India

Keywords: Forecast User, Revolutionary Power, Optimization Tactics As The Digital World Develops, Machine Learning Algorithms, Mobile App Design, Potential of Machine Learning

Abstract

The use of machine learning methods to foretell how users will interact with mobile applications is the focus of this research. Predictive models are created to foretell engagement levels by assessing a variety of features, including user demographics, app usage habits, and user feedback. In order to help app developers optimise user experiences and retention methods, our research seeks to shed light on the elements that influence user engagement and to construct accurate prediction models. Machine learning algorithms may be able to predict how users will interact with a mobile app, according to the results. This might lead to better app designs and more downloads. User engagement dynamics are illuminated by this ground-breaking investigation on the complex relationship between user behaviour and app functionality. Results from extensive testing and analysis show that machine learning has great promise as a method for understanding user actions and preferences. These findings demonstrate the impact of predictive analytics on app development and herald a new age of personalised and interactive user experiences. The findings from this study could significantly impact how mobile app optimisation strategies are developed in the future.

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