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All Journal MIX : Jurnal Ilmiah Manajemen Syntax Literate: Jurnal Ilmiah Indonesia JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan) Jurnal Ilmiah Manajemen Kesatuan Jurnal Ilmiah Akuntansi Kesatuan Cakrawala Repositori Imwi Journal of Social Science Journal of Economics and Business UBS Devotion: Journal of Research and Community Service Co-Value : Jurnal Ekonomi, Koperasi & Kewirausahaan COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Journal of Social Science Inkubis: Jurnal Ekonomi dan Bisnis Jurnal Indonesia Sosial Teknologi Journal Research of Social Science, Economics, and Management Matriks Journal of Business, Social and Technology Equivalent: Jurnal Ilmiah Sosial Teknik Journal of Social Science Journal of Law and Social Politics Journal of Economic Development and Village Building Journal of Digital Marketing and Search Engine Optimization Journal of Digital Business and Data Science Journal of Applied Econometric Journal of Strategic Marketing and Applied Economics
Nendi, Ikhsan
Politeknik Siber Cerdika Internasional
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Journal : Journal of Digital Business and Data Science

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Customer Churn Prediction Uses Machine Learning to Improve Retention on Digital Platforms Budiyono, Anton; Nendi, Ikhsan
Journal of Digital Business and Data Science Vol. 2 No. 2 (2025): Journal of Digital Business And Data Science
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jdbs.v2i2.23

Abstract

Customer churn is a critical challenge for digital platforms operating in highly competitive markets such as e-commerce. This study aims to develop a machine learning–based predictive model to identify Shopee customers in Indonesia who are at high risk of churn, using behavioral and transactional data. A supervised learning approach was employed using multiple algorithms, including Logistic Regression, Decision Trees, Random Forests, and XGBoost. The dataset consisted of user activities, including transaction frequency, recency, voucher usage, application session count, and interaction with promotional features. Data imbalance was addressed using the SMOTE technique to improve classification stability. Results showed that XGBoost achieved the best performance across all evaluation metrics, with an AUC of 0.948, indicating strong discriminative ability. Feature importance analysis revealed that recency, transaction frequency, voucher usage rate, and app session frequency were the most influential predictors of churn. These variables indicate declining engagement and reduced responsiveness to promotional incentives, which are key behavioral signals of churn. The study contributes to both academic literature and practical applications by demonstrating how behavioral analytics and machine learning can support early churn detection and inform targeted retention strategies. Implementing such predictive systems can help e-commerce platforms optimize customer lifetime value and reduce revenue loss.
Co-Authors Abdullah Abdullah Abdurokhim Abdurokhim Aen Fariah Aen Fariah Agus Rohmat Hidayat Akam Azad Mohamedamin Anak Agung Alit Merthayasa Arif Maulana, Arif Arman Md Arman Md Askarno, Askarno Azzahra, Adelia Budiyono, Anton Dedy Setiawan Dodi Sunanto Eko Siswo Adi Sahputra Fahira, Siti Hafsah fariah, Aen Fauziatul Fajaroh Hardianto, Feri I Wayan Dasna Koesharijadi Koesharijadi Koesharijadi, Koesharijadi Lestari, Anisa Ayu Dwi Merthayasa, Anak Agung Alit Nur Alifah Nurjanah, Aisyah Parlan . Pujayanti, Difa Ameliora Rexy Nakula Urbaningrum RUSLY HIDAYAH Rusly Hidayah Santika, Rani Saona, Saona Savandha, Septien Dwi Siti Komara Zanyar Nathir Ghafar Zuhriyah, Nabilaa Faizatuz