In the commercial world, customers are paramount, and understanding their
behavior patterns can lead to highly impactful business decisions. Customer churn,
defined as the rate at which consumers switch from one business to another, presents a
significant obstacle, especially for businesses that rely on subscription models, such as
telecommunications companies. Accurately forecasting customer turnover enables
firms to effectively implement targeted retention initiatives and minimize income loss.
The objective of this study is to employ logistic regression in order to forecast
customer attrition for a telecoms company. We utilized a dataset that comprised
comprehensive client information, encompassing demographics, service usage, and
payment records. Through a thorough exploratory data analysis (EDA), we identified
important patterns and trends that are directly linked to customer attrition. We
performed data preprocessing by addressing missing values, encoding category
variables, and normalizing numerical features. A robust statistical technique,
specifically designed for binary classification, was employed to construct a predictive
model. The discovered key predictive variables included tenure, monthly charges, and
contract type. Our data indicate that clients who have been with us for a shorter period
have higher monthly rates, and those on month-to-month contracts are more likely to
discontinue their services. The regression model attains the highest accuracy,
surpassing other machine learning techniques in terms of overall predictive capability.
This study highlights the efficacy of this instrument as a valuable tool for forecasting
customer turnover, enabling firms to enhance customer satisfaction and reduce churn
rates by making informed decisions based on data.
Keywords: Classification models, Customer churn, Customer retention, Data analysis, Machine learning, Predictive modeling, Telecom industry.
