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Telephony Customer Retention Data
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About
This collection of customer information details various aspects of accounts within telecom services, specifically capturing whether individual customers have stopped using the service, known as churning. The focus is on providing robust data for analytics aimed at understanding and mitigating customer attrition, which is critical for long-term business sustainability. Poor service quality, inadequate customer experience, and the ease with which customers can switch providers are known factors contributing to high churn rates, making this dataset essential for developing strategies to foster customer loyalty and retention.
Columns
The dataset includes 20 features detailing customer profiles and service usage:
- gender: Customer's stated gender (Male/Female).
- SeniorCitizen: Indicates if the customer is a senior citizen (1 = Yes, 0 = No).
- Partner: Whether the customer has a partner (Yes/No).
- Dependents: Whether the customer has dependents (Yes/No).
- tenure: The number of months the customer has remained with the company.
- PhoneService: Whether the customer subscribes to a phone service (Yes/No).
- MultipleLines: Indicates if the customer has multiple phone lines (Yes, No, No phone service).
- InternetService: The type of internet service subscribed to (DSL, Fiber optic, No).
- OnlineSecurity: Whether the customer has online security features (Yes, No, No internet service).
- OnlineBackup: Whether the customer has online backup service (Yes, No, No internet service).
- DeviceProtection: Whether the customer has device protection (Yes, No, No internet service).
- TechSupport: Whether the customer has access to tech support (Yes, No, No internet service).
- StreamingTV: Whether the customer subscribes to streaming TV (Yes, No, No internet service).
- StreamingMovies: Whether the customer subscribes to streaming movies (Yes, No, No internet service).
- Contract: The type of contract agreement (Month-to-month, One year, Two year).
- PaperlessBilling: Whether the customer uses paperless billing (Yes/No).
- PaymentMethod: The method used for payment (Electronic check, Mailed check, Bank transfer, Credit card).
- MonthlyCharges: The charges incurred by the customer each month.
- TotalCharges: The total amount charged to the customer over the tenure.
- Churn: The target variable, indicating whether the customer has churned (Yes/No).
Distribution
This data is available as a CSV file named
customer_churn_telecom_services.csv, with a size of approximately 901.44 kB. The structure consists of 20 distinct columns. The dataset typically contains 7,043 records, though the 'TotalCharges' column has 11 missing values, resulting in 7,032 valid entries for that specific field.Usage
This data is perfectly suited for Binary Classification tasks. Ideal applications include building predictive models to forecast which current customers are highly likely to churn. It can also be used for deep statistical analysis to pinpoint specific service deficiencies (such as lack of tech support or contract length limitations) that require immediate improvement to reduce customer loss. Strategies for improving customer service and investing in new technologies can be informed by insights derived from this analysis.
Coverage
The dataset focuses on specific attributes of telecom customers and their service subscriptions. It captures demographic indicators such as gender and seniority, alongside detailed behavioural and contractual data. The expected update frequency for this type of data is annual.
License
CC0: Public Domain
Who Can Use It
The dataset is appropriate for practitioners ranging from beginner to intermediate skill levels. Intended users include:
- Data Scientists: For training machine learning models focused on attrition prediction.
- Business Analysts: To quantify the impact of service offerings, contracts, and payment methods on customer retention.
- Mobile and Wireless Providers: To gain actionable insights into customer loyalty and service viability.
- E-Commerce Services: To benchmark customer lifecycle analysis strategies.
Dataset Name Suggestions
- Telecom Attrition Factors
- Customer Churn Prediction Dataset
- Telephony Customer Retention Data
- Wireless Service Loss Analysis
Attributes
Original Data Source: Telephony Customer Retention Data
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