Telco Churn Anticipation Engine – A Predictive Attrition Model using Deep Learning

Telco Churn Anticipation Engine – A Predictive Attrition Model using Deep Learning

G. Bhavana1, G. Sai Kumar2, B. Bhagyasree3  , Ch. Sukesh Kumar4, Sk. Altaf Rahman5

 5 Assistant Proffessor ,LIET

[1,2,3,4,5] Computer Science and System Engineering ,Lendi Institute of Engineering and Technology,Vizianagaram

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I ABSTRACT

The Telecommunication Sector has emerged as one of the world’s fastest-growing industries, prioritizing customer satisfaction for business success. With a global reach, consumers have abundant service options, with pricing, flexibility, and customizability influencing their choices. Telecom companies strive to meet these demands through policies and services to attract and retain customers. However, customer churn remains a significant challenge, prompting telecom companies to monitor customer behaviors to predict subscription terminations. Churn prediction, facilitated by various algorithms, is crucial for organizational success. Therefore, there is a growing need to forecast potential churners to implement retention measures and maximize company revenue.

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Key Words: Churn, Exploratory data analysis, RNN, CNN, DNN