Customer Churn Prediction Dataset

Key Dataset Features

1. Customer Identification:
   • Customer ID: A unique identifier for each customer.
   • Surname: The last name of the customer, which can be useful for demographic insights.
2. Customer Profile Information:
   • Credit Score: A numerical value representing the customer’s creditworthiness.
   • Geography: The customer’s region, which helps analyze geographic trends in churn.
   • Gender: Indicates whether the customer is male or female.
   • Age: The age of the customer, which may correlate with retention or attrition trends.
   • Tenure: The number of years the customer has been with the company, potentially affecting loyalty.
3. Financial and Product Data:
   • Account Balance: The customer’s current balance, useful for assessing financial engagement.
   • Number of Products: The total number of products or services the customer holds with the company, showing product engagement levels.
   • Credit Card Ownership: Whether the customer has a company-issued credit card.
   • Active Membership: Indicates if the customer is currently an active member of the service.

Behavioral and Engagement Metrics 

1. Interaction Frequency: Captures how often the customer interacts with the company via various channels such as apps, websites, or customer support.
2. Last Interaction Date: The last time the customer engaged with the company, useful for identifying how recent interactions affect churn rates.
3. Referral Status: Indicates if the customer was acquired through a referral, which can help measure the retention impact of customer referral programs.

Churn Indicator

• Exited: A binary variable that indicates whether the customer has churned (1) or not (0). This is the target variable used to build machine learning models aimed at predicting future churn.

Use Case and Benefits

This dataset helps businesses:

• Predict Customer Churn: By identifying trends in the data, businesses can proactively retain customers.
• Optimize Customer Experience: Understanding the reasons behind customer churn allows companies to offer tailored products and services.
• Enhance Marketing Strategies: Insights from this dataset can help businesses target specific customer segments and improve customer engagement.