An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry

By Hyunseok Hwang, Taesoo Jung, E. Suh. Expert systems with applications, 2004

462 citations recorded by the source corpus.

Subfields: Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/b1bad6ba80b3f89bf22383be7ebb75a396d90436

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Citations in the corpus

Application of data mining techniques in customer relationship management: A literature review and classification (2009, background): “…map Bae, Park, and Ha (2003), Verdú, García, Senabre, Marín, and Franco (2006) Target customer analysis Regression Logistic regression Hwang, Jung, and Suh (2004) Classification Decision tree Chen et al. (2003), Wu, Kao, Su, and Wu (2005), Yu, Ou, Zhang, and Zhang (2005) Clustering…”
Customer segmentation and strategy development based on customer lifetime value: A case study (2006, methodology): “Hwang, Jung, and Suh (2004) considered three factors: current value, potential value, and customer loyalty to calculate LTV and present the method to segment the three factors for customer segmentation.”
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, background): “For instance, Mozer et al. (2000) found that neural networks performed significantly better than logistic regression for predicting customer attrition, whereas Hwang et al. (2004) reported that the latter outperforms the former.”
Computer assisted customer churn management: State-of-the-art and future trends (2007, methodology): “ Dividing the original dataset: Hwang et al. (2004) performed validation by creating a seventy/thirty divide of the data.”
Churn in Social Networks (2010, methodology): “In subsequent studies, factors such as these have been decomposed into feature profiles of the customer and customer experience in order to be able to predict churn [49, 33, 1].”
Building comprehensible customer churn prediction models with advanced rule induction techniques (2011, background): “For instance, both Mozer, Wol-niewicz, Grimes, Johnson, and Kaushansky (2000) Hwang, Jung, and Suh (2004) apply logistic regression and neural networks to predict churn.”