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Journal : Infolitika Journal of Data Science

A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan; Afjal, Mohd; Ray, Samrat
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.199

Abstract

Customer churn is critical for businesses across various industries, especially in the telecommunications sector, where high churn rates can significantly impact revenue and growth. Understanding the factors leading to customer churn is essential for developing effective retention strategies. Despite the predictive power of machine learning models, there is a growing demand for model interpretability to ensure trust and transparency in decision-making processes. This study addresses this gap by applying advanced machine learning models, specifically Naïve Bayes, Random Forest, AdaBoost, XGBoost, and LightGBM, to predict customer churn in a telecommunications dataset. We enhanced model interpretability using SHapley Additive exPlanations (SHAP), which provides insights into feature contributions to predictions. Here, we show that LightGBM achieved the highest performance among the models, with an accuracy of 80.70%, precision of 84.35%, recall of 90.54%, and an F1-score of 87.34%. SHAP analysis revealed that features such as tenure, contract type, and monthly charges are significant predictors of customer churn. These results indicate that combining predictive analytics with interpretability methods can provide telecom companies with actionable insights to tailor retention strategies effectively. The study highlights the importance of understanding customer behavior through transparent and accurate models, paving the way for improved customer satisfaction and loyalty. Future research should focus on validating these findings with real-world data, exploring more sophisticated models, and incorporating temporal dynamics to enhance churn prediction models' predictive power and applicability.
Co-Authors Afjal, Mohd Agustina, Maulidar Ali, Najabat Amalina, Faizah Apriliansyah, Feby Attari, Muhammad Umer Quddoos Ayu Puspitasari, Ayu Bani, Nor Yasmin binti Mhd Bruyn, Chané de Chairunnisa, Rizka Çoban, Mustafa Necati Dahlia, Putri Devi, N. Chitra Duwal, Niroj Eddy Gunawan, Eddy Eko Suhartono Emran, Talha Bin Fadila, Sintia Fajri, Irfan Fazli, Qalbin Salim Fijay, Ade Habya Fikri, Mumtaz Kemal Fitriyani Fitriyani Furqan, Nurul Ghazi Mauer Idroes Hadiyani, Rahmilia Hafizah, Iffah Hamaguchi, Yoshihiro Hapzi Ali Hardia, Natasha Athira Keisha Hidayatullah, Ferdy Hironimus Kihwili, Erick HUMAM, RAIS AULIA Idroes, Ghifari Maulana Idroes, Rinadi Iin Shabrina Hilal Irsan Hardi Irvanizam, Irvanizam Isaack Delya, Mussa Kadri, Mirzatul Khairan Khairan Khairul, Mhd Khairun Nisa Kusumo, Fitranto Lala, Andi Majid, M. Shabri Abd Majumder, Shapan Chandra Mardayanti, Ulfa Marsellindo, Rio Maulana, Aga Maulana, Ar Razy Ridha Maulidar, Putri Mirza, Muhammad Alfin Falha Muhammad Subianto Muksalmina Muksalmina Mursyida, Waliam Nghiem, Xuan-Hoa Nurleila, Nurleila Pernici, Andreea Phonna, Rahmatil Adha Prasetio, Rasi Qashmal, Muhammad Ray, Samrat Razief Perucha Fauzie Afidh Rimal Mahdani Rinaldi Idroes Ringga, Edi Saputra Salim Fazli, Qalbin Salimullah, Abul Hasnat Muhammed Saputra, Fachri Eka Saputra, Jumadil Sasmita, Novi Reandy Sikdar, Asaduzzaman Sofyan Syahnur Sofyan, Rahmi Souvia Rahimah Stancu, Stelian sufriani, sufriani Sugara, Dimas Rendy Sugeng Santoso Suhendrayatna Suhendrayatna Suriani Suriani Suwal, Sunil Syahyana, Ahmad T. Zulham Teuku Rizky Noviandy Thahira, Zia Utami, Resty Tamara Wiranatakusuma, Dimas Bagus Zahriah, Zahriah Zhilalmuhana, Teuku Zikra, Naswatun