This n8n workflow is designed to predict customer churn risk up to 90 days in advance using AI analysis of multiple data signals. It combines data from product usage, support tickets, billing, and email engagement to assess customer health and identify those at high, medium, or critical risk of churn. Once the risk score is calculated, the workflow automatically routes customers into appropriate intervention paths: for critical risk, it sends Slack alerts and generates personalized emails, while high and medium risks trigger tailored messaging and CRM updates.

The workflow begins with a daily cron trigger that fetches data from various sources such as an analytics API (usage data), Zendesk (support tickets), Stripe (billing), and HubSpot (CRM info). All collected data is merged and analyzed using AI modules to generate a customer risk score. Based on this score, the workflow routes customers into different risk levels. Critical risks trigger immediate Slack alerts and personalized intervention messages, aimed at retaining at-risk customers and reducing churn. High and medium risks prompt targeted email campaigns and customer support updates.

This automation is ideal for customer success teams looking to be more proactive rather than reactive, enabling early identification of at-risk customers and delivering personalized, automated outreach to improve retention and reduce churn significantly.