Behavioral Customer Segmentation For Subscription

This research paper addresses the challenge faced by mobile app developers in converting free app users to premium users. Specifically, we aim to predict customer interest in the premium version of a mobile app by analyzing user behavior and app usage patterns. To accomplish this, we use a dataset of over 50,000 app users and employ machine learning algorithms to develop a model that predicts subscription conversion with high accuracy. Our study finds that features such as total time spent on the app, frequency of app usage, and user engagement with specific features of the app are strong predictors of subscription conversion. We also evaluate several machine learning classifiers and find that the XGBoost classifier outperforms other algorithms in terms of accuracy and precision. Our findings provide insights into customer behavior and highlight the factors that influence subscription conversion, allowing app developers to focus on improving user engagement and providing value-added features to increase the likelihood of subscription conversion.