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.
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.
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?
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.
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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