Customer churn — the loss of customers who stop doing business with a company — is a critical challenge in many industries. Identifying customers at risk of churning allows companies to take proactive retention measures, improve customer satisfaction, and protect revenue streams. SAP Predictive Analytics provides an effective platform to build churn prediction models by leveraging historical customer data and advanced machine learning techniques.
This article guides you through the process of building churn prediction models using SAP Predictive Analytics, highlighting best practices and integration within the SAP ecosystem.
Churn prediction is a classification problem where the goal is to predict whether a customer will stay or leave. The model learns patterns from historical data involving customer behavior, demographics, product usage, and service interactions to identify at-risk customers.
SAP Predictive Analytics uses classification algorithms such as decision trees, logistic regression, and support vector machines to build robust churn models.
Effective churn prediction starts with collecting relevant data, which typically includes:
Data preparation is critical to ensure the model's success:
SAP Predictive Analytics integrates with SAP HANA and SAP BW, making it easy to extract and preprocess data from multiple sources.
For churn prediction, use the Classification Scenario in SAP Predictive Analytics:
SAP’s automated workflows help guide users through feature selection and model building with minimal manual intervention.
Using SAP Predictive Analytics:
Decision trees provide interpretable models that are easy to explain to business stakeholders, while logistic regression offers probabilistic outputs useful for scoring.
Validation ensures the model generalizes well on unseen data:
SAP Predictive Analytics provides detailed reports and visual tools to help refine models and avoid overfitting.
After validation, deploy the churn model to:
The model can be retrained periodically with fresh data to maintain accuracy.
Building churn prediction models using SAP Predictive Analytics enables organizations to proactively identify and retain at-risk customers. By leveraging SAP’s integrated tools and advanced algorithms, businesses can improve customer loyalty, reduce attrition, and enhance profitability.
With a structured approach to data preparation, modeling, validation, and deployment, SAP Predictive Analytics makes churn prediction accessible for both business analysts and data scientists in the SAP ecosystem.