000			a
999			_c23298 _d23298
003			OSt
005			20250811132914.0
008			250811b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_927028 _a Jeya, Mala D.
245			_aApplying AI and ML techniques for customer churn prediction in the telecom industry _b: a data-driven decision-making approach
250			_aVol.16(4), Nov
260			_aHyderabad _bIUP Publications _c2024
300			_a7-19p.
520			_aThe paper delves into the application of artificial intelligence (AI) and machine learning (ML) techniques to predict customer attrition rates and promote data-driven decision making in the telecommunications industry. Using a comprehensive dataset encompassing customer demographics, usage behavior, subscription details, billing information, customer interactions, and historical churn records, the paper proposes a holistic approach to churn prediction. The implementation of cutting-edge AI and ML algorithms enables to meticulously analyze and model this dataset, and develop predictive models that can accurately identify probable churners. The findings illustrate the manner in which AI and ML have revolutionized telecommunications industry, not just in terms of predicting client churn but also in fostering a culture of data-driven decision making. Telecommunications companies can employ these technologies to proactively manage customer attrition, optimize promotional strategies, and elevate overall service quality, ultimately ensuring customer loyalty and achieving sustainable growth in a highly competitive market.
650		0	_94619 _aEXTC Engineering
700			_927029 _aMaragathameena, R.
773	0		_dHyderabad IUP Publications _x0975-5551 _tIUP Journal of telecommunications
856			_uhttps://iupindia.in/ViewArticleDetails.asp?ArticleID=7704 _yClick here
942			_2ddc _cAR