Churn Prevention
Know which customers are likely to churn and why. Use all available information about your customers—not just the obvious signals—to determine who’s likely to churn so you can take preventative action and avoid attrition.
We’ve generated quite a few loyal customers of our own.
Early Action to Reduce Long-Term Costs
Customer churn is a killer for any business. It keeps acquisition costs high, complicates long-term planning, and often means that the expense of signing a customer was higher than their investment in your product.
Even if you turn a profit before a customer leaves, you lose additional cross-sell, upsell, and referral revenue. And 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.
Keeping Customers Costs Less Than Finding New Ones
Warning signs can be incredibly difficult to detect when manually sorting through a sea of customer records. RapidMiner can change that—and help you take action.
- Find red flags: Analyze customer data like product usage, purchase history, and other relevant risk factors that aren’t as obvious as complaints.
- Take preventative action: Reach out to at-risk customers to hear about their experience, gather feedback, and turn problems into solutions.
- Avoid attrition and improve ROI: Maximize the lifetime value of each customer by keeping churn at bay and ensuring that your customer acquisition budget is well spent.
Read more about the causes of customer churn here.
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How to Start Preventing Churn
Use our churn modeling template to get started quickly in the RapidMiner platform. This template lets you optimize and evaluate a decision tree model.
Step 1
Load a customer dataset with all available information about customers, not just the obvious warning signs. Examples include: age, technology used, length of time a customer, average bill, number of support calls, and whether they’ve left in the past.
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.
Recommended Resources
See Churn Prevention in Action
Know which customers are likely to churn and why, and turn prediction into prevention. Request a free AI Assessment to determine the feasibility and business impact of the your high-priority use cases.
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