Abstract
The churn rate, being a major issue in the telecommunication industry today as it describes the degree at which customers desert a company and discard its services in one way or another. Over the past years, various changes evolved in the telecommunications industry like liberalization of the market, giving rise to serious competition and hence the evolution of new technologies and opportunities which directly and indirectly affect customer choice and preference for the services of a specific telecommunications company. In this study, a generalized K_LoRD hybrid model for predicting customer churn in the telecommunication industry was developed using K Nearest Neighbor, Logistic Regression, Random Forest and Decision Tree. A publicly available dataset from a telecom company which has a record of customer information and their churn was collected. The K_LoRD hybrid model is evaluated based on the following performance metrics: Accuracy and Receiver Operating Curve. The results showed that the hybrid model efficiently predicts customer churn with 91.85% prediction accuracy and 95.9% Area Under Curve. The experiments have shown that our hybrid prediction model is superior to ordinary K nearest Neighbor, Logistic Regression, Random Forest and Decision Trees.
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References
Goryakin, Y., Lobstein, T., James, W.P.T., Suhrcke, M.: The impact of economic, political and social globalization on overweight and obesity in the 56 low and middle income countries. Soc. Sci. Med. 133, 67–76 (2015)
Taipale, K., Fellini, C., Le Blanc, D.: Challenges and way forward in the urban sector. Sustainable Development in the 21st Century (SD21); Technical report; United Nations Department of Economic and Social Affairs (UNDESA), New York (2012)
Adebiyi, S.O., Oyatoye, E.O., Amole, B.B.: Relevant Drivers for Customers’ Churn and Retention Decision in the Nigerian Mobile Telecommunication Industry. J. Compet. 8(3), 52–67 (2016). https://doi.org/10.7441/joc.2016.03.04
Oyediran, O., Omoshule, A., Misra, S., Maskeliūnas, R., Damaševičius, R.: Attitude of mobile telecommunication subscribers towards sim card registration in Lagos State, Southwestern Nigeria. Int. J. Syst. Assur. Eng. Manag. 10(4), 783–791 (2019)
Amin, A., Shehzad, S., Khan, C., Ali, I., Anwar, S.: Churn prediction in telecommunication industry using rough set approach. In: New Trends in Computational Collective Intelligence, pp. 83–95. Springer, Cham (2015)
Umayaparvathi, V., Iyakutti, K.: Automated feature selection and churn prediction using deep learning models. Int. Res. J. Eng. Technol. (IRJET) 4(3), 1846–1854 (2017)
Xie, Y., Li, X., Ngai, E.W.T., Ying, W.: Customer churn prediction using improved balanced random forests. Expert Syst. Appl. 36(3), 5445–5449 (2009)
Khan, M.R., Manoj, J., Singh, A., Blumenstock, J.: Behavioral modeling for churn prediction: early indicators and accurate predictors of custom defection and loyalty. In 2015 IEEE International Congress on Big Data, pp. 677–680, June 2015
Blessing, G., Azeta, A., Misra, S., Chigozie, F., Ahuja, R.: A machine learning prediction of automatic text based assessment for open and distance learning: a review. In: International Conference on Innovations in Bio-Inspired Computing and Applications, pp. 369–380. Springer, Cham, December 2019
Ahmed, A., Linen, D.M.: A review and analysis of churn prediction methods for customer retention in telecom industries. In 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1–7, January 2017
Mishra, K., Rani, R.: Churn prediction in telecommunication using machine learning. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 2252–2257. IEEE, August 2017
Qureshi, S.A., Rehman, A.S., Qamar, A.M., Kamal, A., Rehman, A.: Telecommunication subscribers’ churn prediction model using machine learning. In: Eighth International Conference on Digital Information Management (ICDIM 2013), pp. 131–136, September 2013
Saini, M.N., Monika, Garg K.: Churn prediction in telecommunication industry using decision tree. Int. J. Eng. Res. Technol. (IJERT) 6(4), 439–443 (2017)
Li, H., Yang, D., Yang, L., Lin, X.: Supervised massive data analysis for telecommunication customer churn prediction. In: 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications, pp. 163–169 (2016)
Amin, A., Khan, C., Ali, I., Anwar, S.: Customer churn prediction in telecommunication industry: with and without counter-example. In: Mexican International Conference on Artificial Intelligence, pp. 206–218. Springer, Cham, November 2014
Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J., Anwar, S.: Customer churn prediction in telecommunication industry using data certainty. J. Bus. Res. 94, 290–301 (2019)
Hudaib, A., Dannoun, R., Harfoushi, O., Obiedat, R., Faris, H.: Hybrid data mining models for predicting customer churn. Int. J. Commun. Netw. Syst. Sci. 8(05), 91 (2015)
Nigam, B., Dugar, H., Niranjanamurthy, M.: Effectual predicting telecom customer churn using deep neural network. Int. J. Eng. Adv. Technol. (IJEAT) 8(5), 121–127 (2019)
Malleswari, M., Maniraj, R., Kumar, P., Murugan: Comparative analysis of machine learning techniques to identify churn for telecom data. Int. J. Eng. Technol. 7(3.34), 291–295 (2018)
De Caigny, A., Coussement, K., De Bock, K.W.: A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. Eur. J. Oper. Res. 269(2), 760–772 (2018)
Hanif, E.: Applications of data mining techniques for churn prediction and cross-selling in the telecommunications industry (Doctoral dissertation, Dublin Business School) (2019)
Kaggle: Telco customer churn dataset (2019). https://www.kaggle.com/lampubhutia/telecomcustomer-churn/downloads/telecomcustomer-churn.zip/1. Accessed 10 Mar 2019
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Odusami, M., Abayomi-Alli, O., Misra, S., Abayomi-Alli, A., Sharma, M.M. (2021). A Hybrid Machine Learning Model for Predicting Customer Churn in the Telecommunication Industry. In: Abraham, A., Sasaki, H., Rios, R., Gandhi, N., Singh, U., Ma, K. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2020. Advances in Intelligent Systems and Computing, vol 1372. Springer, Cham. https://doi.org/10.1007/978-3-030-73603-3_43
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