Customer churn is a critical problem faced by telecom companies, leading to lost revenue and increased marketing costs. In the highly competitive telecommunication sector, customer retention is essential for success. It costs five to seven times more toacquire a new customer than it does to retain an existing one. Considering this, churnprediction models are increasingly becoming an important tool for telecommunicationorganizations looking to minimize their customer attrition rate. Churn, or customer attrition, is a major problem for businesses in the telecommunications sector. Every year,millions of customers switch to new service providers, resulting in billions of dollarsin lost revenue. In the ever- evolving and highly competitive world of telecommunications, businesses are constantly looking for new ways to improve customer loyaltyand reduce customer churn. Machine learning techniques can be incredibly useful inthis endeavor. This study proposes a customer churn prediction model using machinelearning techniques to help telecom companies retain customers and reduce churn rates.The proposed model analyzes big data using machine learning algorithms, including KNearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR),Random Forest (RF), Adaboost, Light Gradient Boosting Machine (LGBM), GradientBoosting, and Extreme Gradient Boosting (XGBoost) to predict customer churn. The proposed model achieves high accuracy score of 95.74% with the XGBoost and LGBMclassifier. The results demonstrate that machine learning algorithms have the potentialto predict customer churn effectively and provide insights into the primary drivers ofcustomer churn.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-506134 |
| Date | January 2023 |
| Creators | Taskin, Nayema |
| Publisher | Uppsala universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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