Predicting Customer Churn in Telecom: A Data-Driven Approach using Analytiqus
In the fiercely competitive telecommunications industry, retaining customers is as crucial as acquiring new ones. Customer churn, where customers discontinue their services, can significantly impact a company’s revenue and growth. Predicting customer churn allows telecom companies to implement proactive measures to retain at-risk customers, enhancing customer satisfaction and loyalty. This blog delves into the process of predicting customer churn using the Telco Churn dataset, showcasing how data science can be a game-changer for businesses.
Understanding the Telco Churn Dataset
The Telco Churn dataset, available on Kaggle, consists of various customer attributes, such as demographics, account information, and services used. The dataset includes the following columns:
- CustomerID: Unique identifier for each customer.
- Gender: Customer gender.
- SeniorCitizen: Indicates if the customer is a senior citizen.
- Partner: Indicates if the customer has a partner.
- Dependents: Indicates if the customer has dependents.
- Tenure: Number of months the customer has stayed with the company.
- PhoneService: Indicates if the customer has phone service.
- MultipleLines: Indicates if the customer has multiple lines.
- InternetService: Type of internet service (DSL, Fiber optic, No).
- OnlineSecurity: Indicates if the customer has online security service.
- OnlineBackup: Indicates if the customer has online backup service.
- DeviceProtection: Indicates if the customer has device protection service.
- TechSupport: Indicates if the customer has tech support service.
- StreamingTV: Indicates if the customer has streaming TV service.
- StreamingMovies: Indicates if the customer has streaming movies service.
- Contract: Type of contract (Month-to-month, One year, Two year).
- PaperlessBilling: Indicates if the customer uses paperless billing.
- PaymentMethod: Customer’s payment method (Electronic check, Mailed check, Bank transfer, Credit card).
- MonthlyCharges: The amount charged to the customer monthly.
- TotalCharges: The total amount charged to the customer.
- Churn: Indicates if the customer churned.
Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) is a critical step in understanding the underlying patterns and relationships within the dataset. It helps in identifying key features, detecting anomalies, and forming hypotheses for predictive modeling.
Initial Observations
The first step in EDA involves making initial observations about the dataset. This includes examining the structure, types of variables, and basic statistics such as mean, median, and standard deviation. Understanding the distribution of numerical features and the balance of categorical variables provides a foundation for further analysis.
Data Distribution
Analyzing the distribution of numerical variables like tenure, MonthlyCharges, and TotalCharges can reveal insights into customer behavior. For instance, a histogram of tenure might show how long customers typically stay with the company, while box plots can highlight any outliers in MonthlyCharges or TotalCharges.
Correlation Analysis
Correlation analysis helps identify relationships between different features. A correlation matrix can reveal which variables are strongly associated with each other. For example, a high correlation between MonthlyCharges and TotalCharges is expected, but discovering unexpected correlations can provide deeper insights.
Churn Analysis
Understanding the characteristics of customers who churn versus those who do not is crucial. This involves comparing distributions and summary statistics of various features between the two groups. Visualizations like bar plots and pie charts can illustrate the proportion of churners within different categories (e.g., contract type, internet service type).
Feature Importance in Predicting Customer Churn
Understanding which features are most influential in predicting customer churn is vital for developing effective retention strategies. Feature importance analysis helps identify the key drivers behind customer churn, allowing businesses to focus their efforts on the most impactful areas.
What is Feature Importance?
Feature importance refers to techniques that assign scores to input features based on their relevance in predicting the target variable. In the context of churn prediction, feature importance measures how much each customer attribute contributes to the likelihood of a customer churning. This insight helps prioritize areas for improvement and targeted interventions.
Methods to Determine Feature Importance
Several methods can be used to determine feature importance, each providing a different perspective on the influence of features:
Tree-based Methods:
- Random Forest: Random Forest uses an ensemble of decision trees to compute the importance of each feature. It does this by measuring the decrease in impurity (Gini impurity or entropy) caused by each feature across all trees. Features that result in larger decreases are deemed more important.
- Gradient Boosting: Similar to Random Forest, Gradient Boosting computes feature importance based on how much each feature contributes to reducing the loss function across all boosting iterations.
Coefficient Analysis:
- Logistic Regression: For linear models like logistic regression, the absolute values of the model coefficients indicate feature importance. Larger coefficients (positive or negative) suggest greater importance.
Permutation Importance:
- This method involves shuffling the values of each feature and observing the effect on the model’s performance. A significant drop in performance indicates that the shuffled feature is important.
Interpreting Feature Importance
Interpreting feature importance involves understanding how each feature influences the prediction of churn. For instance, if the Contract type is found to be highly important, it suggests that the nature of the customer’s contract significantly affects their likelihood of churning.
Key features often identified in telecom churn models include:
- Tenure: Longer tenure generally indicates lower churn risk, as customers who have been with the company longer are less likely to leave.
- MonthlyCharges: Higher monthly charges might correlate with higher churn, especially if customers feel they are not receiving value for their money.
- Contract Type: Customers with month-to-month contracts are typically more likely to churn compared to those with long-term contracts.
- InternetService: The type of internet service (e.g., fiber optic, DSL) can impact churn, with some services potentially being more reliable and satisfying than others.
- TechSupport: Availability and quality of technical support can significantly influence customer satisfaction and retention.
Identifying Key Drivers
Through visualizations like heatmaps and pair plots, we can identify key drivers of churn. These visual tools make it easier to spot trends and patterns that might not be apparent through numerical summaries alone. For instance, a heatmap might show a strong negative correlation between tenure and churn, indicating that long-term customers are less likely to leave.
Data Transformation
Handling Missing Values
The TotalCharges column may have some missing values. We can fill these missing values with the median of the column.
Encoding Categorical Variables
Categorical variables need to be converted into numerical format. We use one-hot encoding for columns with more than two categories and label encoding for binary categories. Columns for label encoding: ‘Gender’, ‘Partner’, ‘Dependents’, ‘PhoneService’, ‘MultipleLines’,
‘OnlineSecurity’, ‘OnlineBackup’, ‘DeviceProtection’, ‘TechSupport’,
‘StreamingTV’, ‘StreamingMovies’, ‘PaperlessBilling’, ‘Churn’
Columns for one-hot encoding: ‘InternetService’, ‘Contract’, ‘PaymentMethod’
Scaling Numerical Features
Scaling is essential for numerical features such as ‘tenure’, ‘MonthlyCharges’, ‘TotalCharges' to ensure all features contribute equally to the model.
Building the Predictive Model
With the data preprocessed, we can now build and train a predictive model. We’ll use a Random Forest classifier due to its robustness and ability to handle a mixture of numerical and categorical data. Based on the feature importance map select columns with high feature importance, where as reject those which has lesser importance score. How a predictive model can be built on Analytiqus is shown in the following video.
