Telco customer churn "Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets]
About the Dataset
Customer churn, also called customer attrition,is the measure of how many customers stop using a product. This can be measured based on actual usage or failure to renew (when the product is sold using a subscription model). Often evaluated for a specific period of time, there can be a monthly, quarterly, or annual churn rate.
To detect early signs of potential churn, one must first develop a holistic view of the customers and their interactions across numerous channels.
To reduce customer churn, telecom companies need to predict which customers are at high risk of churn. By addressing churn, these businesses may not only preserve their market position, but also grow and thrive. More customers they have in their network, the lower the cost of initiation and the larger the profit. As a result, the company's key focus for success is reducing client attrition and implementing effective retention strategy.
Context
"Churn is a one of the biggest problem in the telecom industry. This project aims to Predict behavior to retain customers. The goal is to analyze all relevant customer data and develop focused customer retention programs."
Content Each row represents a customer, each column contains customer’s attributes described on the column Metadata. The data set includes information about:
Customers who left within the last month – the column is called Churn Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers – gender, age range, and if they have partners and dependents