Key Features:

1. Customer Demographics:
   • customerID: Unique identifier for each customer.
   • gender: Gender of the customer.
   • SeniorCitizen: Indicates whether the customer is a senior citizen (1 = Yes, 0 = No).
   • Partner and Dependents: Whether the customer has a partner or dependents.
2. Account Information:
   • tenure: Number of months the customer has stayed with the company.
   • Contract: Type of contract (e.g., Month-to-month, One year, Two year).
   • PaperlessBilling: Whether the customer uses paperless billing.
   • PaymentMethod: Payment method used by the customer.
3. Services Signed Up:
   • PhoneService and MultipleLines: Whether the customer has phone service and multiple lines.
   • InternetService: Type of internet service (DSL, Fiber optic, None).
   • Services such as OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, and StreamingMovies.
4. Charges:
   • MonthlyCharges: The monthly amount charged to the customer.
   • TotalCharges: The total amount charged to the customer.
5. Customer Interaction:
   • numAdminTickets and numTechTickets: Number of administrative and technical support tickets raised by the customer.
6. Churn:
   • Churn: Indicates whether the customer churned (Yes = churned, No = retained).

From a Customer Churn Analysis Perspective:

This dataset can be used to identify patterns or factors influencing customer churn. For example:

• Customers with higher churn rates may have specific contract types, high monthly charges, or a lack of certain services like tech support or online security.
• Senior citizens or customers with shorter tenure could exhibit distinct churn behaviors.

Using this information, strategies can be developed to improve retention by targeting at-risk groups, improving service offerings, or addressing pain points.

From a Customer Risk Analysis Perspective:

This dataset also allows for risk assessment by examining:

• Financial Risk: High-churn customers might pose a financial risk, especially if they contribute significantly to revenue (high TotalCharges or MonthlyCharges).
• Operational Risk: Customers raising numerous numAdminTickets or numTechTickets might indicate service dissatisfaction or operational inefficiencies.
• Behavioral Risk: Customers on month-to-month contracts or without bundled services might be more likely to churn, representing a retention risk.

By integrating predictive models and visualizing the data, businesses can proactively mitigate risks by tailoring retention campaigns or optimizing service quality.