In the highly competitive telecom industry, customer retention is crucial as acquiring a new customer costs significantly more than retaining an existing one. The telecom industry typically experiences an annual churn rate of 15-25%. To stay ahead in the market, it is vital for telecom companies to predict which customers are at high risk of churning and take proactive measures to retain them.
This project focuses on analyzing customer-level data from a leading telecom firm and building predictive models to identify customers who are at high risk of churn. The goal is to improve customer retention by understanding the main indicators of churn and targeting high-value customers, who are responsible for generating the majority of the company's revenue.
Telecom companies face the challenge of high customer churn rates, which directly impacts their profitability. The objective of this project is to predict customer churn using data from the first three months of customer activity and identify high-value customers who are at the greatest risk of leaving. By predicting churn accurately, the company can implement strategies to retain these customers and reduce revenue leakage.
In the telecom industry, churn can be defined in various ways, such as:
- Revenue-based churn: Customers who do not generate any revenue over a specific period.
- Usage-based churn: Customers who have not used any services (calls, SMS, data) over a period.
In this project, churn is defined using a usage-based approach, focusing on customers who have stopped using services in a given period. This project specifically targets the Indian and Southeast Asian markets, where prepaid models are prevalent.
In the Indian and Southeast Asian telecom markets, approximately 80% of revenue comes from the top 20% of customers. This project will focus on predicting churn among these high-value customers, as retaining them is crucial for reducing revenue loss.
The business objective is to predict customer churn in the last month of the dataset using data from the first three months. This involves understanding customer behavior over time and identifying early signs of churn. The project will use the first two months as the 'good' phase, the third month as the 'action' phase (where customers start showing signs of potential churn), and the fourth month as the 'churn' phase.
The dataset used in this project contains customer-level information for four consecutive months (June to September). The data includes various features such as usage metrics, recharge amounts, and customer demographics. The goal is to predict churn in the fourth month using data from the first three months.
To prepare the data for modeling, the following steps were taken:
- Feature Engineering: Derived new features based on business understanding to better capture customer behavior.
- Filtering High-Value Customers: Identified high-value customers based on their recharge amounts in the first two months.
- Churn Tagging: Tagged customers as churners or non-churners based on their usage in the fourth month and removed data corresponding to the churn phase.
The modeling approach involves the following steps:
- Data Preprocessing: Handle missing values, convert columns to appropriate formats, and preprocess the data.
- Exploratory Data Analysis (EDA): Conduct EDA to extract useful insights and inform feature engineering.
- Dimensionality Reduction: Use Principal Component Analysis (PCA) to reduce the number of variables.
- Model Training: Train various models, tune hyperparameters, and handle class imbalance using appropriate techniques.
- Model Evaluation: Evaluate models using appropriate metrics, focusing on identifying churners accurately.
- Feature Importance: Build a second model to identify important predictors of churn, using techniques like logistic regression or tree-based models.
Based on the results of the models, the project will provide actionable insights and recommendations to the telecom company on how to reduce customer churn. These strategies will be aimed at retaining high-value customers, thereby minimizing revenue loss and improving overall customer satisfaction.
This project showcases the application of data science techniques to solve a real-world business problem in the telecom industry, demonstrating the importance of predictive modeling in customer retention strategies.