Churn prediction is the process of identifying customers who are likely to stop using a product or service in the future. It is a predictive analytics technique commonly used in subscription-based businesses, SaaS companies, and consumer platforms.

Churn prediction models analyze historical behavior to estimate churn probability. Common signals include:

• Decreasing usage

• Fewer logins or sessions

• Reduced feature adoption

• Support complaints

• Payment issues

• Engagement drops

Machine learning models used for churn prediction include:

• Logistic regression

• Random forests

• Gradient boosting

• Neural networks

From a BI perspective, churn prediction outputs are often integrated into dashboards as risk scores or segments. This allows teams to:

• Proactively engage at-risk customers

• Prioritize retention efforts

• Personalize offers

• Improve onboarding

Accuracy depends heavily on data quality, feature engineering, and clear churn definitions. Poorly defined churn leads to misleading predictions.

Churn prediction turns analytics from reactive reporting into proactive intervention, allowing businesses to retain customers before revenue is lost.