Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market

By Hee-Su Kim, Choong-Han Yoon. 2004

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How researchers see this paper

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Customer relationship management and customer satisfaction: the mediating role of relationship quality (2017, background): “Research in the telecommunications industry presented similar findings, since satisfaction has emerged as a strong predictor of loyalty (Gerpott, Rams, & Schindler, 2001; Kim & Yoon, 2004; Santouridis & Trivellas, 2010).”
Exploring switching behavior of US mobile service customers (2013, background): “Although by definition barriers, some switching barriers have positive outcomes and can result in “relationally loyal” customers (Kim and Yoon, 2004).”
Computer assisted customer churn management: State-of-the-art and future trends (2007, methodology): “Kim and Yoon [2] use a logit model to determine subscriber churn in the telecommunications industry, based on discrete choice theory.”
Customer churn prediction by hybrid neural networks (2009, methodology): “Other studies (e.g. Kim & Yoon, 2004) only use a binomial logit regression to construct the prediction model.”
A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector (2019, background): “In existing studies, the researchers analyzed the behavior of both classes churner and non-churner [12].”