Predicting Telecom Customer’s Switch Over Intentions Using Machine Learning Algorithms-Reference-Cited by-同舟云学术

Predicting Telecom Customer’s Switch Over Intentions Using Machine Learning Algorithms

Published:2021-10-26 Issue: Volume: Page:603-614
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Raj S. N. Vivek,Rayer S. Prithiviraj Pallava

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-5157-1_47

Reference20 articles.

1. Quantzig. (2020a). Fortune 500 telecom company reduces customer churn with the help of customer lifetime value modeling. Bus Wire Engligh, Feb, 2020. [Online]. Available: http://search.ebscohost.com/login.aspx?direct=true&db=bwh&AN=bizwire.bw5454736&site=ehost-live.

2. Quantzig. (2020b). Analytics experts at quantzig analyze the ımpact of ıncreasing churn rates in telecom | submit RFP for comprehensive ınsights. Bus Wire Engligh, Jul, 2020. [Online]. Available: http://search.ebscohost.com/login.aspx?direct=true&db=bwh&AN=bizwire.bw10187429&site=ehost-live.

3. Singh, S., & Sirohi, N. J. (2015). Mobile number portability: It’s ımpact on consumer and service providers. BVIMR Manage Edge, 8(1), 92–104, Jan, 2015.

4. Chadha, S. K., & Bhandari, N. (2014). Determinants of customer switching towards mobile number portability. Paradigm, 18(2), 199–219. https://doi.org/10.1177/0971890714558708

5. Mazumdar, R. (2019). Reliance jio becomes India’s top telecom services provider. Bloomberg.com, p. N.PAG-N.PAG, Jul, 2019.

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