International Journal on Science and Technology

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Browser-Based Customer Churn Prediction using WebAssembly (Pyodide)

Author(s) Mr. Zulkifl-Uddin Khairoowala, Mr. Raman Ravindra Bole, Mr. Madhuprasad Gaddam, Mr. Abhinash Naik, Mr. Somanath M.
Country India
Abstract Customer churn is a critical challenge for modern businesses, as retaining existing users is far more cost-effective than acquiring new ones. Traditional churn prediction methods rely on server-side machine learning, which introduces issues related to data privacy, network dependency, and deployment complexity. This study investigates an alternative approach by evaluating WebAssembly (Pyodide) for performing churn prediction entirely within the browser.

Five machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine, Neural Network, and XGBoost) were applied to three benchmark datasets from the telecommunications domain. Their performance was measured in terms of predictive accuracy, inference time, memory usage, and cross-browser compatibility. Results show that browser-based models preserve between 85 percent and 92 percent of the predictive accuracy of server-side systems, while reducing infrastructure overhead and ensuring complete data confidentiality. Logistic Regression and Random Forest offered the best balance of accuracy and efficiency, with lightweight models running reliably even on mobile devices.

This work demonstrates the feasibility of in-browser machine learning for churn prediction and provides practical guidelines for algorithm selection and deployment strategies. The findings are especially relevant for small and medium enterprises operating under strict data protection regulations and highlight opportunities for privacy-preserving customer analytics.
Keywords Customer Churn Prediction, WebAssembly, Pyodide, Machine Learning, Edge Computing, Privacy-Preserving Analytics
Field Computer > Data / Information
Published In Volume 16, Issue 4, October-December 2025
Published On 2025-10-08
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