Applying Survival Analysis for Customer Retention: A U.S. Regional Mobile Service Operator

Lim, Tristan

doi:10.1109/zinc50678.2020.9161811

Cited by 1 publication

(1 citation statement)

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“…King and Rice (2019), analyzed the churn in Mobile Telecommunications domain where it aimed to predict the timing of customer churn. Lim (2020), also examined the customer churn through Survival Analysis where it found useful in supplier convergent triple-or-quad-play services. Furthermore, Masarifoglu and Buyuklu (2019), applied Survival Analysis to predict churn in Telecommunications sector which analyzed the lifespan of customers, hence, creating proactive measures.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer Churn Prediction using Predictive Analytics: Basis for the Formulation of Customer Retention Strategy in the Context of Web-based Collaboration Platform

Awit¹,

Marticio²

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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This study utilized Machine Learning (ML) models to analyze and predict customer churns in the context of webbased collaboration platform. Secondary data was used in the study, containing 109,740 observations and 12 predictors. This was divided into test dataset and out-of-time (OOT) dataset where the prior was used for model fitting and the latter is to test performance stability in unseen data. Furthermore, Synthetic Minority Over-sampling Technique (SMOTE) was performed to resolve the data imbalance, hence, preventing bias and distortion in the models' performance. These 3 ML models were assessed based on Accuracy, ROC-AUC, Precision, Recall and F1-Score. Given the business context's applicability, F1-score and Accuracy were used as bases for performance, leading to the selection of Decision Tree Classifier as the ML model in this study, with Accuracy of 92.1% and F1-Score of 63%. Furthermore, hyper-parameter tuning was performed on Decision Tree Classifier to prevent overfitting. To reinforce the model selected, Survival Analysis was implemented, specifically, Kaplan-Meier (KM) Estimator and Cox Proportional Hazard (CPH) were utilized to analyze the rate and timeframe of disengagement to the platform, revealing that beyond 72 months, it was projected to retain only 60% of its user base. Hence, these multidimensional results and insights derived from both Decision Tree Classifier and Survival Analysis were anchored in the formulation of customer retention strategy, proactively target customers who are predicted to churn.

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Section: Literature Reviewmentioning

confidence: 99%