DIRECTING CUSTOMERS TO SUBSCRIPTION THROUGH APP BEHAVIOR ANALYSIS

Computer Engineering

Mobile applications have become essential tools for businesses to interact with their clients in the changing environment of the modern market. Platforms like YouTube Red and Pandora Premium serve as effective examples of the shift from free to paid app memberships. The difficulty, though, is in identifying those who are less likely to subscribe, which calls for precision targeting and customized marketing initiatives. To improve ease and efficiency in financial tracking, this study explores a fintech company's entry into the mobile app market with a premium membership model. This research was motivated by the requirement for efficient targeting and conversions. This study's main objective is to strategically deliver promotions to users who are most likely to respond positively to maximize the return on investment. It starts by carefully examining data obtained from users' app usage during a free trial period of 24 hours. Predictive models are then built using cutting-edge machine learning techniques like Logistic Regression, Support Vector Machines (SVM), and XG Boost. The creation of an interactive web page is a crucial element of this process. This website's dynamic estimates of consumer registration in the premium membership are displayed, offering a clear and interesting user experience. The identification of potential subscribers and the use of customized marketing techniques are among the key findings. By making the most of available resources, streamlining marketing initiatives, and encouraging openness, the technique raises conversion rates. The predictive model encourages conversions and supports well informed decisions, aligned with the business's financial goals.

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