Identify in advance which customers are likely to leave, and why. Use all available information about customer, not just the obvious signs.
Identify customers likely to leave, take preventative action
Dissatisfied customers don’t always complain. Sometimes they just leave – discontinue service, close their account, withdraw their money, or just stop buying. Use data science to predict which customers are at risk, regardless whether they’ve spoken up, and take action to prevent this attrition. It’s the best possible marketing investment, as the cost of retaining an existing customer is far less than acquiring a new one. And the rewards of a rescued customer can be tremendous.
Get started on your churn project today!
Download RapidMiner Studio and use the “Churn Modeling” template to get started quickly. In this template, you can train, optimize, and evaluate a decision tree model.