Exploring user retention in enhance VR: a comprehensive analysis using predictive models and clustering

Odizzio, Catalina; Pissinis, Agostina

Exploring user retention in enhance VR: a comprehensive analysis using predictive models and clustering

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Odizzio, C, Pissinis, A. Exploring user retention in enhance VR: a comprehensive analysis using predictive models and clustering. 2023. handle: http://hdl.handle.net/10230/58358

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Abstract

This study delves into understanding and predicting user engagement in Enhance VR, a virtual reality cognitive training application, through data-driven approaches. The dataset encompasses de-identified user data including demographic characteristics, mood and session related variables. Initial data exploration involves descriptive statistics, data visualization, and inferential statistics, assessing correlations between attributes and their effects on engagement and performance. Machine learning models including Random Forests and Gradient Boosting are developed to predict user engagement levels. K-Prototypes clustering is employed for segmentation, identifying distinct user groups based on behavioral and demographic attributes. This research informs the strategic design and content delivery of Enhance VR by identifying distinct user groups and predicting engagement patterns.