Customer churn prediction in telecommunications

Huang, B. Q.; Kechadi, Tahar; Buckley, Brian

doi:10.1016/j.eswa.2011.08.024

Cited by 275 publications

(149 citation statements)

References 11 publications

Supporting

Mentioning

143

Contrasting

Unclassified

Order By: Relevance

“…For example, Tsai et al made use of the DT and ANN for customer churn predictions for videoon-demand service [33], while Huang et al used 7 machine learning methods to predict customer churn for mobile communications companies [34]. Research related to customer churn predictions showed that machine learning methods, such as ANN, DT and BN, have relatively good prediction results; therefore, we have also selected these methods for predicting students dropping out in this study.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Tan

Shao

2015

Int. J. Emerg. Technol. Learn.

124

View full text Add to dashboard Cite

Abstract-The dropout high rate is a serious problem in Elearning programs. Thus, it is a concern of education administrators and researchers. Predicting the dropout potential of students is a workable solution for preventing dropouts. Based on the analysis of related literature, this study selected students' personal characteristics and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN), Decision Tree (DT) and Bayesian Networks (BNs). A large sample of 62,375 students was utilized in the procedures of model training and testing. The results of each model were presented in a confusion matrix and were analyzed by calculating the rates of accuracy, precision, recall, and F-measure. The results suggested all of the three machine learning methods were effective for student dropout prediction, but DT presented a better performance. Finally, some suggestions were made for future research.

show abstract

Section: Machine Learning Techniquesmentioning

confidence: 99%

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Tan

Shao

2015

Int. J. Emerg. Technol. Learn.

124

View full text Add to dashboard Cite

show abstract

“…Many domains such as banks, mobile phone companies, internet service providers and supermarkets use churn analysis and churn rates as a key business metric as it has been shown that the cost of retaining an existing customer is less than the cost of acquiring new customers (Wei & Chiu 2002;Hung et al, 2006;Huang et al, 2012). These existing customers tend to purchase more than new customers and it is more efficient to deal with existing customers than dealing with new customers (Fornell & Wernerfelt 1987;1988;Reichheld & Sasser 1990;Bolton 1998).…”

Section: %$And*5281'mentioning

confidence: 99%

“…Organizations in many different domains such as wireless telecommunication and the telecommunication industry (Huang et al, 2012, Keramati, 2014, Mahajan et al, 2015, mobile phone (Kirui et al, 2013), internet service providers (Khan et al, 2010), energy providers and other industries such as insurance, retail banking (Mutanen et al, 2006), financial services and supermarkets are having increasing difficulty in attracting and retaining customers as reported by Shandiz (2015). This is in part owing to customers being able to access information regarding brands, products and price comparisons on many internet comparison websites (Mahajan et al, 2015).…”

mentioning

confidence: 99%

Case Studies in Applying Data Mining for Churn Analysis

Lomax

Vadera

2017

International Journal of Conceptual Structures and Smart Applications

View full text Add to dashboard Cite

The advent of price and product comparison sites now makes it even more important to retain customers and identify those that might be at risk of leaving. The use of data mining methods has been widely advocated for predicting customer churn. This paper presents two case studies that utilize decision tree learning methods to develop models for predicting churn for a software company. The first case study aims to predict churn for organizations which currently have an ongoing project, to determine if organizations are likely to continue with other projects. While the second case study presents a more traditional example, where the aim is to predict organizations likely to cease being a subscriber to a service. The case studies include presentation of the accuracy of the models using a standard methodology as well as comparing the results with what happened in practice. Both case studies show the significant savings that can be made, plus potential increase in revenue by using decision tree learning for churn analysis.

show abstract

“…Logistic Regression, as described by [15] is a method used to test a hypothesis pertaining to the relationships between categorical variables. As stated by [16] Logistic Regression is easy to use and provides quick and robust results.…”

Section: Data Mining Techniquesmentioning

confidence: 99%