{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:37:45Z","timestamp":1774535865002,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,2,14]],"date-time":"2021-02-14T00:00:00Z","timestamp":1613260800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,14]],"date-time":"2021-02-14T00:00:00Z","timestamp":1613260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s00607-021-00908-y","type":"journal-article","created":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:13:40Z","timestamp":1613607220000},"page":"271-294","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":225,"title":["Customer churn prediction system: a machine learning approach"],"prefix":"10.1007","volume":"104","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0024-7738","authenticated-orcid":false,"given":"Praveen","family":"Lalwani","sequence":"first","affiliation":[]},{"given":"Manas Kumar","family":"Mishra","sequence":"additional","affiliation":[]},{"given":"Jasroop Singh","family":"Chadha","sequence":"additional","affiliation":[]},{"given":"Pratyush","family":"Sethi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,14]]},"reference":[{"issue":"8","key":"908_CR1","first-page":"35","volume":"19","author":"H Abbasimehr","year":"2011","unstructured":"Abbasimehr H, Setak M, Tarokh M (2011) A neuro-fuzzy classifier for customer churn prediction. International Journal of Computer Applications 19(8):35\u201341","journal-title":"International Journal of Computer Applications"},{"issue":"3","key":"908_CR2","first-page":"75","volume":"11","author":"O Adwan","year":"2014","unstructured":"Adwan O, Faris H, Jaradat K, Harfoushi O, Ghatasheh N (2014) Predicting customer churn in telecom industry using multilayer preceptron neural networks: Modeling and analysis. Life Science Journal 11(3):75\u201381","journal-title":"Life Science Journal"},{"issue":"1","key":"908_CR3","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s40537-019-0191-6","volume":"6","author":"AK Ahmad","year":"2019","unstructured":"Ahmad AK, Jafar A, Aljoumaa K (2019) Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data 6(1):28","journal-title":"Journal of Big Data"},{"key":"908_CR4","doi-asserted-by":"crossref","unstructured":"Archambault, D., Hurley, N., Tu, C.T.: Churnvis: visualizing mobile telecommunications churn on a social network with attributes. In: 2013 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), pp. 894\u2013901. IEEE (2013)","DOI":"10.1145\/2492517.2500274"},{"issue":"10","key":"908_CR5","first-page":"1149","volume":"119","author":"P Asthana","year":"2018","unstructured":"Asthana P (2018) A comparison of machine learning techniques for customer churn prediction. International Journal of Pure and Applied Mathematics 119(10):1149\u20131169","journal-title":"International Journal of Pure and Applied Mathematics"},{"issue":"4","key":"908_CR6","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/s40745-018-0155-2","volume":"5","author":"R Aziz","year":"2018","unstructured":"Aziz R, Verma C, Srivastava N (2018) Artificial neural network classification of high dimensional data with novel optimization approach of dimension reduction. Annals of Data Science 5(4):615\u2013635","journal-title":"Annals of Data Science"},{"key":"908_CR7","doi-asserted-by":"crossref","unstructured":"Br\u00e2ndu\u015foiu, I., Toderean, G., Beleiu, H.: Methods for churn prediction in the pre-paid mobile telecommunications industry. In: 2016 International conference on communications (COMM), pp. 97\u2013100. IEEE (2016)","DOI":"10.1109\/ICComm.2016.7528311"},{"issue":"3","key":"908_CR8","doi-asserted-by":"publisher","first-page":"4626","DOI":"10.1016\/j.eswa.2008.05.027","volume":"36","author":"J Burez","year":"2009","unstructured":"Burez J, Van den Poel D (2009) Handling class imbalance in customer churn prediction. Expert Systems with Applications 36(3):4626\u20134636","journal-title":"Expert Systems with Applications"},{"key":"908_CR9","doi-asserted-by":"crossref","unstructured":"Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: From big data to big impact. MIS quarterly pp. 1165\u20131188 (2012)","DOI":"10.2307\/41703503"},{"issue":"9","key":"908_CR10","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1016\/j.jbusres.2012.12.008","volume":"66","author":"K Coussement","year":"2013","unstructured":"Coussement K, De Bock KW (2013) Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research 66(9):1629\u20131636","journal-title":"Journal of Business Research"},{"issue":"1","key":"908_CR11","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.eswa.2006.09.038","volume":"34","author":"K Coussement","year":"2008","unstructured":"Coussement K, Van den Poel D (2008) Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert systems with applications 34(1):313\u2013327","journal-title":"Expert systems with applications"},{"key":"908_CR12","doi-asserted-by":"crossref","unstructured":"Dahiya, K., Bhatia, S.: Customer churn analysis in telecom industry. In: 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), pp. 1\u20136 (2015)","DOI":"10.1109\/ICRITO.2015.7359318"},{"key":"908_CR13","doi-asserted-by":"crossref","unstructured":"Dong, T., Shang, W., Zhu, H.: Na\u00efve bayesian classifier based on the improved feature weighting algorithm. In: International Conference on Computer Science and Information Engineering, pp. 142\u2013147. Springer (2011)","DOI":"10.1007\/978-3-642-21402-8_23"},{"issue":"8","key":"908_CR14","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to roc analysis. Pattern recognition letters 27(8):861\u2013874","journal-title":"Pattern recognition letters"},{"issue":"4","key":"908_CR15","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.1016\/j.asoc.2009.04.004","volume":"9","author":"S Garc\u00eda","year":"2009","unstructured":"Garc\u00eda S, Fern\u00e1ndez A, Herrera F (2009) Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems. Applied Soft Computing 9(4):1304\u20131314","journal-title":"Applied Soft Computing"},{"issue":"1","key":"908_CR16","first-page":"35","volume":"39","author":"U\u015e G\u00fcrsoy","year":"2010","unstructured":"G\u00fcrsoy U\u015e (2010) Customer churn analysis in telecommunication sector. \u0130stanbul \u00dcniversitesi \u0130\u015fletme Fak\u00fcltesi Dergisi 39(1):35\u201349","journal-title":"\u0130stanbul \u00dcniversitesi \u0130\u015fletme Fak\u00fcltesi Dergisi"},{"issue":"2","key":"908_CR17","first-page":"104","volume":"1","author":"J Hadden","year":"2006","unstructured":"Hadden J, Tiwari A, Roy R, Ruta D (2006) Churn prediction: Does technology matter. International Journal of Intelligent Technology 1(2):104\u2013110","journal-title":"International Journal of Intelligent Technology"},{"issue":"10","key":"908_CR18","doi-asserted-by":"publisher","first-page":"2902","DOI":"10.1016\/j.cor.2005.11.007","volume":"34","author":"J Hadden","year":"2007","unstructured":"Hadden J, Tiwari A, Roy R, Ruta D (2007) Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research 34(10):2902\u20132917","journal-title":"Computers & Operations Research"},{"key":"908_CR19","unstructured":"Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier,"},{"key":"908_CR20","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhu, F., Yuan, M., Deng, K., Li, Y., Ni, B., Dai, W., Yang, Q., Zeng, J.: Telco churn prediction with big data. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data, pp. 607\u2013618 (2015)","DOI":"10.1145\/2723372.2742794"},{"key":"908_CR21","doi-asserted-by":"crossref","unstructured":"Idris, A., Khan, A., Lee, Y.S.: Genetic programming and adaboosting based churn prediction for telecom. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1328\u20131332. IEEE (2012)","DOI":"10.1109\/ICSMC.2012.6377917"},{"key":"908_CR22","unstructured":"Kirui, C., Hong, L., Cheruiyot, W., Kirui, H.: Predicting customer churn in mobile telephony industry using probabilistic classifiers in data mining. International Journal of Computer Science Issues (IJCSI) 10(2 Part 1), 165 (2013)"},{"issue":"6","key":"908_CR23","doi-asserted-by":"publisher","first-page":"7151","DOI":"10.1016\/j.eswa.2010.12.045","volume":"38","author":"P Kisioglu","year":"2011","unstructured":"Kisioglu P, Topcu YI (2011) Applying bayesian belief network approach to customer churn analysis: A case study on the telecom industry of turkey. Expert Systems with Applications 38(6):7151\u20137157","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"908_CR24","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/s12083-016-0531-7","volume":"10","author":"P Lalwani","year":"2017","unstructured":"Lalwani P, Banka H, Kumar C (2017) Crwo: Clustering and routing in wireless sensor networks using optics inspired optimization. Peer-to-Peer Networking and Applications 10(3):453\u2013471","journal-title":"Peer-to-Peer Networking and Applications"},{"key":"908_CR25","doi-asserted-by":"crossref","unstructured":"Lalwani, P., Banka, H., Kumar, C.: Gsa-chsr: gravitational search algorithm for cluster head selection and routing in wireless sensor networks. In: Applications of Soft Computing for the Web, pp. 225\u2013252. Springer (2017)","DOI":"10.1007\/978-981-10-7098-3_13"},{"issue":"5","key":"908_CR26","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1007\/s00500-016-2429-y","volume":"22","author":"P Lalwani","year":"2018","unstructured":"Lalwani P, Banka H, Kumar C (2018) Bera: a biogeography-based energy saving routing architecture for wireless sensor networks. Soft Computing 22(5):1651\u20131667","journal-title":"Soft Computing"},{"key":"908_CR27","doi-asserted-by":"crossref","unstructured":"Lejeune MA (2001) Measuring the impact of data mining on churn management. Internet Research","DOI":"10.1108\/10662240110410183"},{"issue":"2","key":"908_CR28","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/S0167-9236(01)00108-7","volume":"32","author":"AP Massey","year":"2001","unstructured":"Massey AP, Montoya-Weiss MM, Holcom K (2001) Re-engineering the customer relationship: leveraging knowledge assets at ibm. Decision Support Systems 32(2):155\u2013170","journal-title":"Decision Support Systems"},{"issue":"24","key":"908_CR29","doi-asserted-by":"publisher","first-page":"13409","DOI":"10.1007\/s00500-019-03879-7","volume":"23","author":"RA Musheer","year":"2019","unstructured":"Musheer RA, Verma C, Srivastava N (2019) Novel machine learning approach for classification of high-dimensional microarray data. Soft Computing 23(24):13409\u201313421","journal-title":"Soft Computing"},{"key":"908_CR30","first-page":"505","volume":"561","author":"SV Nath","year":"2003","unstructured":"Nath SV, Behara RS (2003) Customer churn analysis in the wireless industry: A data mining approach. Proceedings-annual meeting of the decision sciences institute 561:505\u2013510","journal-title":"Proceedings-annual meeting of the decision sciences institute"},{"issue":"4","key":"908_CR31","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1002\/(SICI)1522-7138(199723)11:4<109::AID-DIR12>3.0.CO;2-G","volume":"11","author":"LA Petrison","year":"1997","unstructured":"Petrison LA, Blattberg RC, Wang P (1997) Database marketing: Past, present, and future. Journal of Direct Marketing 11(4):109\u2013125","journal-title":"Journal of Direct Marketing"},{"key":"908_CR32","doi-asserted-by":"crossref","unstructured":"Qureshi, S.A., Rehman, A.S., Qamar, A.M., Kamal, A., Rehman, A.: Telecommunication subscribers\u2019 churn prediction model using machine learning. In: Eighth International Conference on Digital Information Management (ICDIM 2013), pp. 131\u2013136. IEEE (2013)","DOI":"10.1109\/ICDIM.2013.6693977"},{"issue":"2","key":"908_CR33","first-page":"80","volume":"3","author":"D Radosavljevik","year":"2010","unstructured":"Radosavljevik D, van der Putten P, Larsen KK (2010) The impact of experimental setup in prepaid churn prediction for mobile telecommunications: What to predict, for whom and does the customer experience matter? Trans. MLDM 3(2):80\u201399","journal-title":"Trans. MLDM"},{"issue":"1","key":"908_CR34","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s10586-017-0933-1","volume":"21","author":"R Rajamohamed","year":"2018","unstructured":"Rajamohamed R, Manokaran J (2018) Improved credit card churn prediction based on rough clustering and supervised learning techniques. Cluster Computing 21(1):65\u201377","journal-title":"Cluster Computing"},{"issue":"8","key":"908_CR35","first-page":"3961","volume":"17","author":"A Rodan","year":"2014","unstructured":"Rodan A, Faris H, Alsakran J, Al-Kadi O (2014) A support vector machine approach for churn prediction in telecom industry. International journal on information 17(8):3961\u20133970","journal-title":"International journal on information"},{"issue":"4","key":"908_CR36","first-page":"693","volume":"2","author":"E Shaaban","year":"2012","unstructured":"Shaaban E, Helmy Y, Khedr A, Nasr M (2012) A proposed churn prediction model. International Journal of Engineering Research and Applications 2(4):693\u2013697","journal-title":"International Journal of Engineering Research and Applications"},{"issue":"4","key":"908_CR37","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.21275\/v5i4.NOV162954","volume":"5","author":"H Sharma","year":"2016","unstructured":"Sharma H, Kumar S (2016) A survey on decision tree algorithms of classification in data mining. International Journal of Science and Research (IJSR) 5(4):2094\u20132097","journal-title":"International Journal of Science and Research (IJSR)"},{"key":"908_CR38","unstructured":"Simons, R.: Siebel systems: Organizing for the customer (2005)"},{"key":"908_CR39","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, f-score and roc: a family of discriminant measures for performance evaluation. In: Australasian joint conference on artificial intelligence, pp. 1015\u20131021. Springer (2006)","DOI":"10.1007\/11941439_114"},{"key":"908_CR40","unstructured":"Tamaddoni\u00a0Jahromi, A.: Predicting customer churn in telecommunications service providers (2009)"},{"issue":"4","key":"908_CR41","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1057\/palgrave.jt.5740056","volume":"10","author":"A Ultsch","year":"2002","unstructured":"Ultsch A (2002) Emergent self-organising feature maps used for prediction and prevention of churn in mobile phone markets. Journal of Targeting, Measurement and Analysis for Marketing 10(4):314\u2013324","journal-title":"Journal of Targeting, Measurement and Analysis for Marketing"},{"issue":"4","key":"908_CR42","first-page":"1065","volume":"4","author":"V Umayaparvathi","year":"2016","unstructured":"Umayaparvathi V, Iyakutti K (2016) A survey on customer churn prediction in telecom industry: Datasets, methods and metrics. International Research Journal of Engineering and Technology (IRJET) 4(4):1065\u20131070","journal-title":"International Research Journal of Engineering and Technology (IRJET)"},{"issue":"2","key":"908_CR43","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/S0957-4174(02)00030-1","volume":"23","author":"CP Wei","year":"2002","unstructured":"Wei CP, Chiu IT (2002) Turning telecommunications call details to churn prediction: a data mining approach. Expert systems with applications 23(2):103\u2013112","journal-title":"Expert systems with applications"},{"issue":"3","key":"908_CR44","doi-asserted-by":"publisher","first-page":"5445","DOI":"10.1016\/j.eswa.2008.06.121","volume":"36","author":"Y Xie","year":"2009","unstructured":"Xie Y, Li X, Ngai E, Ying W (2009) Customer churn prediction using improved balanced random forests. Expert Systems with Applications 36(3):5445\u20135449","journal-title":"Expert Systems with Applications"},{"key":"908_CR45","unstructured":"Yu, W., Jutla, D.N., Sivakumar, S.C.: A churn-strategy alignment model for managers in mobile telecom. In: 3rd Annual Communication Networks and Services Research Conference (CNSR\u201905), pp. 48\u201353. IEEE (2005)"},{"key":"908_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Li, B., Li, X., Liu, W., Ren, S.: Customer churn prediction using improved one-class support vector machine. In: International Conference on Advanced Data Mining and Applications, pp. 300\u2013306. Springer (2005)","DOI":"10.1007\/11527503_36"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-021-00908-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-021-00908-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-021-00908-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T15:03:38Z","timestamp":1643987018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-021-00908-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,14]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["908"],"URL":"https:\/\/doi.org\/10.1007\/s00607-021-00908-y","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,14]]},"assertion":[{"value":"19 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}