Open Access Open Access  Restricted Access Subscription Access

On The Analysis of Customer Engagements with A Telecommunication Company in Sokoto-North western Nigeria Using Machine Learning Techniques


Affiliations
1 Department of Computer Science,Usmanu Danfodiyo University, Sokoto, Nigeria
2 Undergraduate Student, Department of Computer Science,Usmanu Danfodiyo University, Sokoto, Nigeria
3 Department of Computer Science, Waziri Ummaru Federal Polytechnic. Birnin-Kebbi, Nigeria
 

This study was intended to analyse data mining techniques on the customer engagements with telecommunication companies in Nigeria. This study was guided by the following objectives; to provide an overview, on how prediction is being made in a telecommunication company using data mining. MTN Nigeria was chosen as a case study to identify fraud telecommunication companies in Nigeria; to identify the challenges of data mining faced by telecommunication companies in Nigeria. The study employed the descriptive and explanatory design; primary means were applied in order to collect data. Primary data sources were used and data was analyzed using orange data mining software. The study findings revealed that data mining significantly impacts on the performance of telecommunication industries. In this paper, we made an attempt in to the analysis of telecommunication company data to assess the impact of customer engagements.



Keywords

MTN, Machine Learning, K-Nearest Neighbor, K-Mean, Decision Tree.
User
Notifications
Font Size

  • Aregbeyen, A. (2011). The Determinants of Bank Selection Choices by Customers: Recent and Extensive Evidence from Nigeria. International Journal of Business and Social Science.Vol. 2, No. 22, pp.276-288.
  • BenlanHea, Y. & QianWan, X. (2014). Prediction of customer attrition of commercial banks based on SVM model. 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014, Procedia Computer Science Vol. 31, pp.423 – 430.
  • Ezawa, K. & Norton, S. (1995). Knowledge discovery in telecommunication services data using Bayesian Network models. Pp 100.
  • Han, J., Altman, R. B., Kumar, V., Mannila, H., & Pregibon, D (2002). Emerging scientific applications in data mining. Communications of the ACM; 45(8): 54-58.
  • Ngai, E.W.T. Li Xiu, D.C.K. % Chau, (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications. Vol. 36: pp. 2592–2602.
  • Oshini Goonetilleke, T.L & Caldera, H.A. (2013). Mining Life Insurance Data for Customer Attrition Analysis. Journal of Industrial and Intelligent Information.Vol. 1: 52-58.
  • Rehman,H. U. & Ahmed, S. (2008). An Empirical Analysis of the determinants of bank selection in Pakistan; A customer view. Pakistan Economic and Social Review. Vol. 46, no.2, pp.147-160.
  • Siddiqi, K. O. (2011). Interrelations between Service Quality Attributes, Customer Satisfaction and Customer Loyalty in the Retail Banking Sector in Bangladesh. International Journal of Business and Management. Vol. 6, No. 3, pp.12-36.
  • Soeini, R. A. & Rodpysh, K.V. (2012). Evaluations of Data Mining Methods in Order to Provide the Optimum Method for Customer Churn reduction: Case Study Insurance Industry”, International Conference on Information and Computer Applications. Vol. 24: 290297.
  • ZHOA Shan, M. LIU Ai-Jun, L. (2007), "A predictive Model of Churn in Telecommunications Base on Data Mining"., IEEE International Conference on Control and Automation", Guangzhou, China.
  • MTN official website retrieved from https://www.mtnonline.com/ on 11/01/2022
  • ORANGE official website retrieved from https://orangedatamining.com/ on 15/01/2022.

Abstract Views: 185

PDF Views: 1




  • On The Analysis of Customer Engagements with A Telecommunication Company in Sokoto-North western Nigeria Using Machine Learning Techniques

Abstract Views: 185  |  PDF Views: 1

Authors

Bello.A. Bodinga Bodinga
Department of Computer Science,Usmanu Danfodiyo University, Sokoto, Nigeria
A.O. Faramade
Undergraduate Student, Department of Computer Science,Usmanu Danfodiyo University, Sokoto, Nigeria
Bello.A. Buhari
Department of Computer Science,Usmanu Danfodiyo University, Sokoto, Nigeria
Muzzammil Mansur
Department of Computer Science, Waziri Ummaru Federal Polytechnic. Birnin-Kebbi, Nigeria

Abstract


This study was intended to analyse data mining techniques on the customer engagements with telecommunication companies in Nigeria. This study was guided by the following objectives; to provide an overview, on how prediction is being made in a telecommunication company using data mining. MTN Nigeria was chosen as a case study to identify fraud telecommunication companies in Nigeria; to identify the challenges of data mining faced by telecommunication companies in Nigeria. The study employed the descriptive and explanatory design; primary means were applied in order to collect data. Primary data sources were used and data was analyzed using orange data mining software. The study findings revealed that data mining significantly impacts on the performance of telecommunication industries. In this paper, we made an attempt in to the analysis of telecommunication company data to assess the impact of customer engagements.



Keywords


MTN, Machine Learning, K-Nearest Neighbor, K-Mean, Decision Tree.

References