Customer churn analysis : A case study on the telecommunication industry of Thailand

Customer churn creates a huge anxiety in highly competitive service sectors especially the telecommunications sector. The objective of this research was to develop a predictive churn model to predict the customers that will be to churn; this is the first step to construct a retention management plan. The dataset was extracted from the data warehouse of the mobile telecommunication company in Thailand. The system generated the customer list, to implement a retention campaign to manage the customers with tendency to leave the company. WEKA software was used to implement the followings techniques: C4.5 decision trees algorithm, the logistic regression algorithm and the neural network algorithm. The C4.5 algorithm of decision trees proved optimal among the models. The findings are unequivocally beneficial to industry and other partners.

[1]  Rajan Chattamvelli Data Mining Algorithms , 2011 .

[2]  Li Xiu,et al.  Application of data mining techniques in customer relationship management: A literature review and classification , 2009, Expert Syst. Appl..

[3]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[4]  Tzung-Pei Hong,et al.  An improved data mining approach using predictive itemsets , 2009, Expert Syst. Appl..

[5]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[6]  Kenneth C. Lichtendahl,et al.  Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner , 2016 .

[7]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[8]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[9]  Marcin Owczarczuk,et al.  Churn models for prepaid customers in the cellular telecommunication industry using large data marts , 2010, Expert Syst. Appl..

[10]  Vadlamani Ravi,et al.  Churn prediction using comprehensible support vector machine: An analytical CRM application , 2014, Appl. Soft Comput..

[11]  Deyi Li,et al.  Spatial Data Mining: Theory and Application , 2016 .

[12]  Setak Mostafa,et al.  A Neuro-Fuzzy Classifier for Customer Churn Prediction , 2011 .

[13]  Li Hong,et al.  Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining , 2013 .

[14]  David C. Yen,et al.  Applying data mining to telecom churn management , 2006, Expert Syst. Appl..

[15]  Zhengxin Chen,et al.  A Descriptive Framework for the Field of Data Mining and Knowledge Discovery , 2008, Int. J. Inf. Technol. Decis. Mak..

[16]  Liu Dandan,et al.  Construction of Forestry Resource Classification Rule Decision Tree Based on ID3 Algorithm , 2009, 2009 First International Workshop on Education Technology and Computer Science.

[17]  Kristof Coussement,et al.  Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-selection Techniques Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparin , 2022 .