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.

Using data science to accurately predict churn

With RapidMiner, you can identify in advance which customers are likely to leave, and why. Use all available information about customer, not just the obvious signs. When you identify these customers you will be able to:

Prevent attrition

Proactively take action with at-risk customers. Reach out, solve problems, make up for any missteps and dissatisfaction.

Improve marketing ROI

It’s more effective to retain a customer than acquire a new one. And customers with resolved issues are often more loyal and spend more over their lifetime.

Identify problem areas

Knowing what makes customers dissatisfied enough to leave can shed light on lurking problem needing improvement. Gain insight, take action, improve products and service.

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.

Step 1:

Load a customer dataset with all available information about customer, not just the obvious signs. Some examples include:
Age, Technology used (4G, fiber, etc.), Date since he/she is a customer, Average bill last year, 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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