Customer churn prediction system: a machine learning approach
The customer churn prediction (CCP) is one of the challenging problems in the telecom industry. With the advancement in the field of machine learning and artificial intelligence, the possibilities to predict customer churn has increased significantly. Our proposed methodology, consists of six phases...
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| Published in: | Computing Vol. 104; no. 2; pp. 271 - 294 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Vienna
Springer Vienna
01.02.2022
Springer Nature B.V |
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| ISSN: | 0010-485X, 1436-5057 |
| Online Access: | Get full text |
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| Abstract | The customer churn prediction (CCP) is one of the challenging problems in the telecom industry. With the advancement in the field of machine learning and artificial intelligence, the possibilities to predict customer churn has increased significantly. Our proposed methodology, consists of six phases. In the first two phases, data pre-processing and feature analysis is performed. In the third phase, feature selection is taken into consideration using gravitational search algorithm. Next, the data has been split into two parts train and test set in the ratio of 80% and 20% respectively. In the prediction process, most popular predictive models have been applied, namely, logistic regression, naive bayes, support vector machine, random forest, decision trees, etc. on train set as well as boosting and ensemble techniques are applied to see the effect on accuracy of models. In addition, K-fold cross validation has been used over train set for hyperparameter tuning and to prevent overfitting of models. Finally, the obtained results on test set have been evaluated using confusion matrix and AUC curve. It was found that Adaboost and XGboost Classifier gives the highest accuracy of 81.71% and 80.8% respectively. The highest AUC score of 84%, is achieved by both Adaboost and XGBoost Classifiers which outperforms over others. |
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| AbstractList | The customer churn prediction (CCP) is one of the challenging problems in the telecom industry. With the advancement in the field of machine learning and artificial intelligence, the possibilities to predict customer churn has increased significantly. Our proposed methodology, consists of six phases. In the first two phases, data pre-processing and feature analysis is performed. In the third phase, feature selection is taken into consideration using gravitational search algorithm. Next, the data has been split into two parts train and test set in the ratio of 80% and 20% respectively. In the prediction process, most popular predictive models have been applied, namely, logistic regression, naive bayes, support vector machine, random forest, decision trees, etc. on train set as well as boosting and ensemble techniques are applied to see the effect on accuracy of models. In addition, K-fold cross validation has been used over train set for hyperparameter tuning and to prevent overfitting of models. Finally, the obtained results on test set have been evaluated using confusion matrix and AUC curve. It was found that Adaboost and XGboost Classifier gives the highest accuracy of 81.71% and 80.8% respectively. The highest AUC score of 84%, is achieved by both Adaboost and XGBoost Classifiers which outperforms over others. |
| Author | Lalwani, Praveen Mishra, Manas Kumar Sethi, Pratyush Chadha, Jasroop Singh |
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| Cites_doi | 10.1016/j.eswa.2010.12.045 10.1007/s12083-016-0531-7 10.1016/j.eswa.2008.06.121 10.1016/j.jbusres.2012.12.008 10.1016/j.eswa.2006.09.038 10.1007/s00500-016-2429-y 10.1016/S0167-9236(01)00108-7 10.1016/j.eswa.2008.05.027 10.1002/(SICI)1522-7138(199723)11:4<109::AID-DIR12>3.0.CO;2-G 10.1016/j.asoc.2009.04.004 10.1007/s00500-019-03879-7 10.21275/v5i4.NOV162954 10.1007/s10586-017-0933-1 10.1007/s40745-018-0155-2 10.1057/palgrave.jt.5740056 10.1186/s40537-019-0191-6 10.1016/j.cor.2005.11.007 10.1016/j.patrec.2005.10.010 10.1016/S0957-4174(02)00030-1 10.1109/ICSMC.2012.6377917 10.1145/2723372.2742794 10.2307/41703503 10.1108/10662240110410183 10.1109/ICRITO.2015.7359318 10.1007/11941439_114 10.1145/2492517.2500274 10.1007/978-3-642-21402-8_23 10.1007/11527503_36 10.1007/978-981-10-7098-3_13 10.1109/ICComm.2016.7528311 10.1109/ICDIM.2013.6693977 |
| ContentType | Journal Article |
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| References | Coussement, De Bock (CR10) 2013; 66 Radosavljevik, van der Putten, Larsen (CR33) 2010; 3 Kisioglu, Topcu (CR23) 2011; 38 CR19 CR39 CR38 CR13 Petrison, Blattberg, Wang (CR31) 1997; 11 Sharma, Kumar (CR37) 2016; 5 Abbasimehr, Setak, Tarokh (CR1) 2011; 19 CR12 Asthana (CR5) 2018; 119 CR32 Gürsoy (CR16) 2010; 39 Hadden, Tiwari, Roy, Ruta (CR18) 2007; 34 Ultsch (CR41) 2002; 10 Rajamohamed, Manokaran (CR34) 2018; 21 Musheer, Verma, Srivastava (CR29) 2019; 23 Aziz, Verma, Srivastava (CR6) 2018; 5 Nath, Behara (CR30) 2003; 561 Shaaban, Helmy, Khedr, Nasr (CR36) 2012; 2 Wei, Chiu (CR43) 2002; 23 Massey, Montoya-Weiss, Holcom (CR28) 2001; 32 Hadden, Tiwari, Roy, Ruta (CR17) 2006; 1 Coussement, Van den Poel (CR11) 2008; 34 CR4 Ahmad, Jafar, Aljoumaa (CR3) 2019; 6 CR7 CR9 CR27 García, Fernández, Herrera (CR15) 2009; 9 CR25 CR46 CR45 CR22 Lalwani, Banka, Kumar (CR26) 2018; 22 CR21 CR20 Burez, Van den Poel (CR8) 2009; 36 CR40 Fawcett (CR14) 2006; 27 Adwan, Faris, Jaradat, Harfoushi, Ghatasheh (CR2) 2014; 11 Xie, Li, Ngai, Ying (CR44) 2009; 36 Umayaparvathi, Iyakutti (CR42) 2016; 4 Lalwani, Banka, Kumar (CR24) 2017; 10 Rodan, Faris, Alsakran, Al-Kadi (CR35) 2014; 17 V Umayaparvathi (908_CR42) 2016; 4 908_CR9 Y Xie (908_CR44) 2009; 36 K Coussement (908_CR10) 2013; 66 R Aziz (908_CR6) 2018; 5 908_CR27 908_CR7 908_CR25 D Radosavljevik (908_CR33) 2010; 3 908_CR4 O Adwan (908_CR2) 2014; 11 UŞ Gürsoy (908_CR16) 2010; 39 RA Musheer (908_CR29) 2019; 23 A Rodan (908_CR35) 2014; 17 E Shaaban (908_CR36) 2012; 2 908_CR46 908_CR45 908_CR22 908_CR21 908_CR20 LA Petrison (908_CR31) 1997; 11 T Fawcett (908_CR14) 2006; 27 908_CR40 908_CR19 908_CR39 P Kisioglu (908_CR23) 2011; 38 908_CR38 H Sharma (908_CR37) 2016; 5 AK Ahmad (908_CR3) 2019; 6 AP Massey (908_CR28) 2001; 32 J Burez (908_CR8) 2009; 36 S García (908_CR15) 2009; 9 P Lalwani (908_CR26) 2018; 22 R Rajamohamed (908_CR34) 2018; 21 J Hadden (908_CR17) 2006; 1 J Hadden (908_CR18) 2007; 34 A Ultsch (908_CR41) 2002; 10 CP Wei (908_CR43) 2002; 23 K Coussement (908_CR11) 2008; 34 SV Nath (908_CR30) 2003; 561 908_CR13 908_CR12 P Asthana (908_CR5) 2018; 119 908_CR32 P Lalwani (908_CR24) 2017; 10 H Abbasimehr (908_CR1) 2011; 19 |
| References_xml | – ident: CR45 – ident: CR22 – volume: 3 start-page: 80 issue: 2 year: 2010 end-page: 99 ident: CR33 article-title: The impact of experimental setup in prepaid churn prediction for mobile telecommunications: What to predict, for whom and does the customer experience matter? publication-title: Trans. MLDM – volume: 38 start-page: 7151 issue: 6 year: 2011 end-page: 7157 ident: CR23 article-title: Applying bayesian belief network approach to customer churn analysis: A case study on the telecom industry of turkey publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.12.045 – ident: CR4 – ident: CR39 – volume: 10 start-page: 453 issue: 3 year: 2017 end-page: 471 ident: CR24 article-title: Crwo: Clustering and routing in wireless sensor networks using optics inspired optimization publication-title: Peer-to-Peer Networking and Applications doi: 10.1007/s12083-016-0531-7 – ident: CR12 – volume: 36 start-page: 5445 issue: 3 year: 2009 end-page: 5449 ident: CR44 article-title: Customer churn prediction using improved balanced random forests publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.06.121 – volume: 66 start-page: 1629 issue: 9 year: 2013 end-page: 1636 ident: CR10 article-title: Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning publication-title: Journal of Business Research doi: 10.1016/j.jbusres.2012.12.008 – volume: 34 start-page: 313 issue: 1 year: 2008 end-page: 327 ident: CR11 article-title: Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques publication-title: Expert systems with applications doi: 10.1016/j.eswa.2006.09.038 – volume: 22 start-page: 1651 issue: 5 year: 2018 end-page: 1667 ident: CR26 article-title: Bera: a biogeography-based energy saving routing architecture for wireless sensor networks publication-title: Soft Computing doi: 10.1007/s00500-016-2429-y – volume: 32 start-page: 155 issue: 2 year: 2001 end-page: 170 ident: CR28 article-title: Re-engineering the customer relationship: leveraging knowledge assets at ibm publication-title: Decision Support Systems doi: 10.1016/S0167-9236(01)00108-7 – volume: 19 start-page: 35 issue: 8 year: 2011 end-page: 41 ident: CR1 article-title: A neuro-fuzzy classifier for customer churn prediction publication-title: International Journal of Computer Applications – volume: 36 start-page: 4626 issue: 3 year: 2009 end-page: 4636 ident: CR8 article-title: Handling class imbalance in customer churn prediction publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.05.027 – volume: 11 start-page: 109 issue: 4 year: 1997 end-page: 125 ident: CR31 article-title: Database marketing: Past, present, and future publication-title: Journal of Direct Marketing doi: 10.1002/(SICI)1522-7138(199723)11:4<109::AID-DIR12>3.0.CO;2-G – ident: CR40 – volume: 4 start-page: 1065 issue: 4 year: 2016 end-page: 1070 ident: CR42 article-title: A survey on customer churn prediction in telecom industry: Datasets, methods and metrics publication-title: International Research Journal of Engineering and Technology (IRJET) – volume: 9 start-page: 1304 issue: 4 year: 2009 end-page: 1314 ident: CR15 article-title: Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2009.04.004 – ident: CR25 – volume: 23 start-page: 13409 issue: 24 year: 2019 end-page: 13421 ident: CR29 article-title: Novel machine learning approach for classification of high-dimensional microarray data publication-title: Soft Computing doi: 10.1007/s00500-019-03879-7 – volume: 11 start-page: 75 issue: 3 year: 2014 end-page: 81 ident: CR2 article-title: Predicting customer churn in telecom industry using multilayer preceptron neural networks: Modeling and analysis publication-title: Life Science Journal – ident: CR27 – volume: 17 start-page: 3961 issue: 8 year: 2014 end-page: 3970 ident: CR35 article-title: A support vector machine approach for churn prediction in telecom industry publication-title: International journal on information – volume: 119 start-page: 1149 issue: 10 year: 2018 end-page: 1169 ident: CR5 article-title: A comparison of machine learning techniques for customer churn prediction publication-title: International Journal of Pure and Applied Mathematics – volume: 1 start-page: 104 issue: 2 year: 2006 end-page: 110 ident: CR17 article-title: Churn prediction: Does technology matter publication-title: International Journal of Intelligent Technology – ident: CR21 – volume: 5 start-page: 2094 issue: 4 year: 2016 end-page: 2097 ident: CR37 article-title: A survey on decision tree algorithms of classification in data mining publication-title: International Journal of Science and Research (IJSR) doi: 10.21275/v5i4.NOV162954 – ident: CR46 – ident: CR19 – volume: 21 start-page: 65 issue: 1 year: 2018 end-page: 77 ident: CR34 article-title: Improved credit card churn prediction based on rough clustering and supervised learning techniques publication-title: Cluster Computing doi: 10.1007/s10586-017-0933-1 – volume: 5 start-page: 615 issue: 4 year: 2018 end-page: 635 ident: CR6 article-title: Artificial neural network classification of high dimensional data with novel optimization approach of dimension reduction publication-title: Annals of Data Science doi: 10.1007/s40745-018-0155-2 – ident: CR38 – volume: 10 start-page: 314 issue: 4 year: 2002 end-page: 324 ident: CR41 article-title: Emergent self-organising feature maps used for prediction and prevention of churn in mobile phone markets publication-title: Journal of Targeting, Measurement and Analysis for Marketing doi: 10.1057/palgrave.jt.5740056 – ident: CR13 – ident: CR9 – ident: CR32 – volume: 6 start-page: 28 issue: 1 year: 2019 ident: CR3 article-title: Customer churn prediction in telecom using machine learning in big data platform publication-title: Journal of Big Data doi: 10.1186/s40537-019-0191-6 – volume: 34 start-page: 2902 issue: 10 year: 2007 end-page: 2917 ident: CR18 article-title: Computer assisted customer churn management: State-of-the-art and future trends publication-title: Computers & Operations Research doi: 10.1016/j.cor.2005.11.007 – volume: 27 start-page: 861 issue: 8 year: 2006 end-page: 874 ident: CR14 article-title: An introduction to roc analysis publication-title: Pattern recognition letters doi: 10.1016/j.patrec.2005.10.010 – ident: CR7 – volume: 23 start-page: 103 issue: 2 year: 2002 end-page: 112 ident: CR43 article-title: Turning telecommunications call details to churn prediction: a data mining approach publication-title: Expert systems with applications doi: 10.1016/S0957-4174(02)00030-1 – volume: 39 start-page: 35 issue: 1 year: 2010 end-page: 49 ident: CR16 article-title: Customer churn analysis in telecommunication sector publication-title: İstanbul Üniversitesi İşletme Fakültesi Dergisi – volume: 2 start-page: 693 issue: 4 year: 2012 end-page: 697 ident: CR36 article-title: A proposed churn prediction model publication-title: International Journal of Engineering Research and Applications – ident: CR20 – volume: 561 start-page: 505 year: 2003 end-page: 510 ident: CR30 article-title: Customer churn analysis in the wireless industry: A data mining approach publication-title: Proceedings-annual meeting of the decision sciences institute – volume: 1 start-page: 104 issue: 2 year: 2006 ident: 908_CR17 publication-title: International Journal of Intelligent Technology – volume: 2 start-page: 693 issue: 4 year: 2012 ident: 908_CR36 publication-title: International Journal of Engineering Research and Applications – ident: 908_CR38 – ident: 908_CR40 – volume: 11 start-page: 75 issue: 3 year: 2014 ident: 908_CR2 publication-title: Life Science Journal – ident: 908_CR21 doi: 10.1109/ICSMC.2012.6377917 – ident: 908_CR19 – ident: 908_CR20 doi: 10.1145/2723372.2742794 – volume: 10 start-page: 314 issue: 4 year: 2002 ident: 908_CR41 publication-title: Journal of Targeting, Measurement and Analysis for Marketing doi: 10.1057/palgrave.jt.5740056 – volume: 27 start-page: 861 issue: 8 year: 2006 ident: 908_CR14 publication-title: Pattern recognition letters doi: 10.1016/j.patrec.2005.10.010 – volume: 23 start-page: 13409 issue: 24 year: 2019 ident: 908_CR29 publication-title: Soft Computing doi: 10.1007/s00500-019-03879-7 – volume: 39 start-page: 35 issue: 1 year: 2010 ident: 908_CR16 publication-title: İstanbul Üniversitesi İşletme Fakültesi Dergisi – ident: 908_CR9 doi: 10.2307/41703503 – volume: 22 start-page: 1651 issue: 5 year: 2018 ident: 908_CR26 publication-title: Soft Computing doi: 10.1007/s00500-016-2429-y – volume: 38 start-page: 7151 issue: 6 year: 2011 ident: 908_CR23 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.12.045 – volume: 32 start-page: 155 issue: 2 year: 2001 ident: 908_CR28 publication-title: Decision Support Systems doi: 10.1016/S0167-9236(01)00108-7 – volume: 19 start-page: 35 issue: 8 year: 2011 ident: 908_CR1 publication-title: International Journal of Computer Applications – ident: 908_CR45 – ident: 908_CR27 doi: 10.1108/10662240110410183 – volume: 6 start-page: 28 issue: 1 year: 2019 ident: 908_CR3 publication-title: Journal of Big Data doi: 10.1186/s40537-019-0191-6 – volume: 5 start-page: 615 issue: 4 year: 2018 ident: 908_CR6 publication-title: Annals of Data Science doi: 10.1007/s40745-018-0155-2 – volume: 10 start-page: 453 issue: 3 year: 2017 ident: 908_CR24 publication-title: Peer-to-Peer Networking and Applications doi: 10.1007/s12083-016-0531-7 – volume: 561 start-page: 505 year: 2003 ident: 908_CR30 publication-title: Proceedings-annual meeting of the decision sciences institute – volume: 34 start-page: 2902 issue: 10 year: 2007 ident: 908_CR18 publication-title: Computers & Operations Research doi: 10.1016/j.cor.2005.11.007 – volume: 4 start-page: 1065 issue: 4 year: 2016 ident: 908_CR42 publication-title: International Research Journal of Engineering and Technology (IRJET) – volume: 23 start-page: 103 issue: 2 year: 2002 ident: 908_CR43 publication-title: Expert systems with applications doi: 10.1016/S0957-4174(02)00030-1 – ident: 908_CR12 doi: 10.1109/ICRITO.2015.7359318 – volume: 11 start-page: 109 issue: 4 year: 1997 ident: 908_CR31 publication-title: Journal of Direct Marketing doi: 10.1002/(SICI)1522-7138(199723)11:4<109::AID-DIR12>3.0.CO;2-G – volume: 36 start-page: 4626 issue: 3 year: 2009 ident: 908_CR8 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.05.027 – ident: 908_CR39 doi: 10.1007/11941439_114 – ident: 908_CR4 doi: 10.1145/2492517.2500274 – ident: 908_CR13 doi: 10.1007/978-3-642-21402-8_23 – ident: 908_CR22 – ident: 908_CR46 doi: 10.1007/11527503_36 – ident: 908_CR25 doi: 10.1007/978-981-10-7098-3_13 – volume: 66 start-page: 1629 issue: 9 year: 2013 ident: 908_CR10 publication-title: Journal of Business Research doi: 10.1016/j.jbusres.2012.12.008 – volume: 34 start-page: 313 issue: 1 year: 2008 ident: 908_CR11 publication-title: Expert systems with applications doi: 10.1016/j.eswa.2006.09.038 – volume: 21 start-page: 65 issue: 1 year: 2018 ident: 908_CR34 publication-title: Cluster Computing doi: 10.1007/s10586-017-0933-1 – volume: 119 start-page: 1149 issue: 10 year: 2018 ident: 908_CR5 publication-title: International Journal of Pure and Applied Mathematics – volume: 36 start-page: 5445 issue: 3 year: 2009 ident: 908_CR44 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.06.121 – ident: 908_CR7 doi: 10.1109/ICComm.2016.7528311 – volume: 17 start-page: 3961 issue: 8 year: 2014 ident: 908_CR35 publication-title: International journal on information – volume: 9 start-page: 1304 issue: 4 year: 2009 ident: 908_CR15 publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2009.04.004 – ident: 908_CR32 doi: 10.1109/ICDIM.2013.6693977 – volume: 3 start-page: 80 issue: 2 year: 2010 ident: 908_CR33 publication-title: Trans. MLDM – volume: 5 start-page: 2094 issue: 4 year: 2016 ident: 908_CR37 publication-title: International Journal of Science and Research (IJSR) doi: 10.21275/v5i4.NOV162954 |
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