Telecom churn prediction

Keywords: Telecom, churn

Background

Predicting telecom churn is crucial for telecom companies to retain customers and prevent revenue loss. This study suggests using Artificial Neural Networks (ANNs) as a predictive tool for telecom churn. ANNs are powerful machine learning algorithms that can analyze complex patterns and connections in large datasets. By training an ANN using historical customer data like demographics, usage patterns, and behavior, it becomes possible to identify which customers are more likely to churn in the near future. This prediction allows companies to take proactive steps in retaining customers, such as offering personalized incentives and promotions. Real-world telecom churn datasets were used to evaluate the proposed ANN model, which demonstrated accurate predictions of customer churn with high precision and recall rates. These findings suggest that ANNs provide a promising approach for telecom companies to proactively tackle churn, enhance customer loyalty, and optimize business operations. Overall, the application of ANN has provided some good results with an accuracy of 78%.

Aim & Objectives

Aim
To Predict the Churn in Telecom Industry using DL Techniques
Objective
Create an ANN model to predict telecom customer churn, enabling proactive retention strategies and service improvements.
Develop an ANN system that analyzes customer usage patterns and behavior to forecast churn probabilities.
Evaluate the ANN's churn prediction effectiveness, facilitating reduced customer attrition and enhanced telecom services.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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