Effective churn prediction relies on diverse, relevant data sources. These sources provide insights into user behavior, engagement, and satisfaction, helping identify potential churn risks.

Common data sources for churn prediction include:

• Usage logs: Frequency and patterns of product or service use
• Customer support interactions: Tickets, chat logs, and call records
• Transaction history: Purchase frequency, amounts, and types
• User feedback: Survey responses and product ratings
• Account information: Sign-up date, plan type, and demographic data

For instance, a mobile app might analyze daily active users, feature usage, and in-app purchase history to predict churn likelihood.

Combining multiple data sources often leads to more accurate churn predictions.^[1]