Telecom Customer Churn Prediction Data

Columns

• CustomerID: A unique identifier assigned to each customer.
• Age: The age of the customer.
• Gender: The customer's gender (e.g., Male, Female).
• Tenure: The duration, in months, that the customer has been with the company.
• ServiceType: The type of service the customer is subscribed to (e.g., Basic, Premium).
• MonthlyCharges: The recurring amount charged to the customer on a monthly basis.
• TotalCharges: The cumulative amount charged to the customer throughout their tenure.
• ContractType: The type of contract the customer holds with the company (e.g., Month-to-Month, One Year, Two Year).
• PaymentMethod: The method used by the customer for making payments (e.g., Credit Card, Bank Transfer).
• Churn: A binary indicator; '1' signifies that the customer has discontinued the service, while '0' indicates an active customer.
• account_length: The overall length of the customer's account.
• area_code: The geographical area code associated with the customer.
• international_plan: An indicator for whether the customer has an international calling plan.
• voice_mail_plan: An indicator for whether the customer has a voicemail plan.
• number_vmail_messages: The number of voicemail messages.
• total_day_minutes: Total minutes used for calls during the daytime.
• total_day_calls: The number of calls made during the daytime.
• total_day_charge: Total charges incurred for daytime usage.
• total_eve_minutes: Total minutes used for calls during the evening.
• total_eve_calls: The number of calls made during the evening.
• total_eve_charge: Total charges incurred for evening usage.
• total_night_minutes: Total minutes used for calls during the night-time.
• total_night_calls: The number of calls made during the night-time.
• total_night_charge: Total charges incurred for night-time usage.
• total_intl_minutes: Total minutes used for international calls.
• total_intl_calls: The number of international calls made.
• total_intl_charge: Total charges incurred for international calls.
• customer_service_calls: The number of calls made to customer service.

Distribution

Usage

• Predictive modelling: Developing machine learning models to forecast customer churn.
• Customer segmentation: Identifying groups of customers at high risk of churning for targeted interventions.
• Root cause analysis: Understanding the key factors and behaviours that lead to customer attrition.
• Strategic planning: Informing business decisions related to customer retention and service improvements.
• Academic research: Analysing telecommunications customer behaviour and evaluating predictive models.

Coverage

License

Who Can Use It

• Data Scientists and Machine Learning Engineers: For building and testing churn prediction models.
• Business Intelligence Analysts: For deriving insights into customer behaviour and market trends within the telecommunications sector.
• Telecommunication Companies: For developing proactive customer retention strategies and improving service offerings.
• Researchers and Academics: For studying consumer behaviour in the telecom industry and validating churn prediction methodologies.

Dataset Name Suggestions

• Telecom Customer Churn Prediction Data
• Cleaned Telecommunications Churn Analytics
• Customer Retention Dataset (Telecom)
• Telco Churn Behavioural Data
• Telecommunications Service Churn

Attributes