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Churn Prediction and Prevention

Know which customers are likely to churn, and why. Use all available information about customers, not just the obvious signs.

Take early action and reduce long-term costs

Customer churn is a killer for any business. It keeps acquisition costs high, complicates long-term planning, and in many cases, means that the cost of signing a customer was higher than their investment in your product. Even if you do manage to break even or turn a profit before a customer leaves, there’s additional cross-sell, upsell, and referral revenue being left on the table.

For every customer who complains, provides critical feedback, or warns that they’re planning to leave before doing it, there are several who close their accounts or stop buying without notice. This doesn’t mean that there weren’t warning signs––just that they’re incredibly difficult to manually detect in a sea of customer records. Here’s how RapidMiner can change that.

Find red flags

Analyze customer data like product usage, purchase history, and other relevant risk factors that aren’t as obvious as a complaint.

Take preventative action

Reach out to at-risk customers to hear about their experience, gather feedback, and solve problems.

Avoid attrition and improve ROI

Maximize the lifetime value of each customer by keeping churn at bay, and ensure that your customer acquisition dollars are well spent.

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Understand how other execs are achieving success & planning for the future, so you can make the best decisions for your organization.

Get started on your churn prediction 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.

Step 1

Load a customer dataset with all available information about customers, not just the obvious signs. Examples include: age, technology used, date since he/she is a customer, average bill, number of support calls, did he/she abandon last year?

Step 2

Edit, transform, learn (ETL) and prepare data.

Mark the target label column (i.e. the churn indicator) and convert the numerical churn column to binary.

Step 3

Model validation is key! This cross-validation splits the dataset for training and, then, for independent testing.

This splitting is done several times to get a better performance estimate.

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Learn more about churn prevention with RapidMiner