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A Machine Learning Model for Customer Segmentation in a Telecom Company using K –Nearest Neighbor (KNN)
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 31 |
| Year of Publication: 2022 |
| Authors: Israa Abdulrauof Othman |
10.5120/ijca2022922385
|
Israa Abdulrauof Othman . A Machine Learning Model for Customer Segmentation in a Telecom Company using K –Nearest Neighbor (KNN). International Journal of Computer Applications. 184, 31 ( Oct 2022), 36-42. DOI=10.5120/ijca2022922385
Abstract
Now a days many countries hosts a fluid and competitive telecommunication market and for a company to create .sustain customer value and increase economic efficiency, it needs to better understand its customers. The purpose of customer segmentation or clustering is to deliver actionable results for marketing, business planning and product development .This paper focus on customer segmentation using clustering algorithms on real data of a telecommunication company, the dataset is from IBM recourses. After choosing appropriate attributes for clustering, KNN clustering algorithm was used in order to create different customer segments. Moreover, the insights obtained from each segment were analyzed before suggesting marketing strategies for up- selling and better targeted campaigns.
References
- Jain S., Sharma N.K., Gupta S., Doohan N. (2018) Business .Strategy Prediction System for Market Basket Analysis. In: Kapur P., Kumar U., Verma A. (eds) Quality, IT and Business Operations. Springer Proceedings in Business and Economics. Springer, Singapore. 2017. DOI https://doi.org/10.1007/978-981-10-5577-5_8
- Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K., “Customer churn prediction in the telecommunication sector using a rough set approach,”Neurocomputing, 237, pp. 242-254, 2017.
- TASIL, https://tasil.com/insights/segmentation-advantages-in-telecom/23/8/2022
- Kim, K., Jun, C. H., & Lee, J. (2014). Improved churn prediction in telecommunication industry by analyzing a large network. Expert Systems with Applications, 41(15), 6575-6584.
- H. Wang.: Nearest Neighbours without k: A Classification Formalism based on Probability, technical report, Faculty of Informatics, University of Ulster, N.Ireland, UK (2002)
- Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas,K.C.(2015).A comparisonof machine learning techniques for customer churn prediction. Simulation Modelling Practiceand Theory, 55, 1-9.
- Ahn, J. H., Han, S. P., & Lee, Y. S. (2006). Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommunications policy, 30(10-11), 552-568.
- Akhil Sharma,Published inPublished in Data Science https://medium.com/data-science-on-customer-churn data/k-nearest-neighbors-knn-on-customer-churn-data-40e9b2bb9266
- Customer churn prediction for telecommunication industry: A Malaysian Case Study [version 1; peer review: 2 approved] Nurulhuda Mustafa1, Lew Sook Ling 2, Siti Fatimah Abdul Razak 2 F1000Research 2021, 10:1274 Last updated: 13 JUN 2022
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