Unveiling Telecom Customer Attrition using Machine Learning

Abstract

In the telecom sector, Capturing Customer acquisition channels is becoming more competitive since here, Retaining existing customers requires fewer resources. Churn management has become pivotal in the telecom industry. The framework comprises six elements: data pre-processing, EDA (Exploratory data analysis), churn attrition (prediction), factor analysis, segmentation of customers, and customer behavior analytics. By merging churn prediction and customer segmentation processes, this framework delivers a comprehensive study to aid Telecom operators in effectively managing customer churn. The experimentation involves three datasets and employs six machine learning classifiers. Many machine learning algorithms are initially utilized to predict customers' churn status. To address imbalanced datasets, the Synthetic Minority Oversampling Technique (SMOTE) is employed in the training set. Model assessment is conducted using 10-fold cross-validation, with accuracy and F1-score serving as the metrics for evaluation. After carrying out churn attrition forecasting, Bayesian Logistic Analysis or Regression is used here to perform factor analysis and determine some essential features for the segmentation of customers. Here in the system, customer segmentation is done by utilizing K-means clustering. Customers are partitioned into multiple segments, which allows the marketers and decision-making team to execute retention strategies with the most précised output.