Turning telecommunications call details to churn prediction: a data mining approach

By Chih-Ping Wei, I-Tang Chiu. Expert systems with applications, 2002

493 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/5be6e653955d5ff18b7cf2dbffa94d31f11d1f06

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Citations in the corpus

Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry (2008, unknown): “…health care, customer relationship management, failure detection and prediction, and organization learning (e.g., Chien, Chen, & Lin, 2002; Peng & Chien, 2003; Peng, Chien, & Tseng, 2004; Shiue & Su, 2003; Shaw, Subramaniam, Tan, & Welge, 2001; Wei & Chiu, 2002; Wu, Kao, Su, & Wu, 2005).”
Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry (2006, methodology): “For example, detailed call data is used to predict the probability of customer churn (Mozer, Wolniewicz, Grimes, Johnson, & Kaushansky, 2000; Ng and Liu, 2000; Wei and Chiu, 2002); a subscriber’s remaining tenure with the company is estimated using internal company databases (Drew, Mani, Betz, & Dat”
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, methodology): “, 2000), decision trees (Wei and Chiu, 2002; Lima et al., 2009), and neural networks (Au et al.”
Concept drift detection via competence models (2014, background): “Otherwise, as concept drifts, the induced pattern may not be relevant to the new data [5, 6], which may result in an increasing number of errors [7].”
Churn in Social Networks (2010, background): “[8] discusses several approaches for that problem, such as under-sampling [40] or using specific class ratios (ratio between churners and non-churners) [66].”
Customer churn prediction in telecom using machine learning in big data platform (2019, background): “However, comparing these strategies taking the value of return on investment (RoI) of each into account has shown that the third strategy is the most profitable strategy [2], proves that retaining an existing customer costs much lower than acquiring a new one [3], in addition to being considered muc”
Customer churn prediction in telecommunications (2012, methodology): “The use of call details in churn prediction is reported in the literature (Wei & Chiu, 2002; Zhang et al., 2006).”
A comparison of machine learning techniques for customer churn prediction (2015, methodology): “Such methods include Artiﬁcial Neural Networks [4, 5, 16, 17, 18], Decision Trees learning [4, 5, 7, 9, 16, 17], Regression Analysis [16], Logistic Regression [5, 9], Support Vector Machines [17], Na¨ıve Bayes [4, 19], Sequential Pattern Mining and Market Basket Analysis [20], Linear Discriminant…”
Churn prediction in new users of Yahoo! answers (2012, background): “The main approach for churn prediction is to construct a set of features for each user and train a classifier or regressor for the task [6, 12, 15, 7, 4, 14, 18, 11, 13, 2, 3, 10, 17, 8, 9].”
Differential Privacy in Telco Big Data Platform (2015, background): “The Data Publication Architecture publishes a synthetic dataset with DP guarantee.”