PostgreSQL Churn Risk Workflow: Gemini + Slack Alerts (n8n)

Spot PostgreSQL customer churn risk every day—then alert your team in Slack

This n8n workflow detects “relationship drift” from PostgreSQL metrics, uses Google Gemini to generate a structured diagnosis for medium/high-risk accounts, saves the assessment back to Postgres, and posts actionable Slack alerts with recommended next steps.

What this workflow does

• Runs daily (07:00) on a schedule (or can be run manually) to start the drift check.
• Pulls drift feature data from a Postgres view (v_customer_drift_features) and calculates a rule-based risk score, level, and supporting signals for each customer.
• Filters to the important cases: only customers above your configured threshold are sent back to Postgres to fetch recent support tickets and communications context.
• Generates an AI diagnosis with Google Gemini (via a LangChain agent), producing structured output such as drift diagnosis, evidence, and a recommended next action.
• Validates AI output by ensuring it cites known rule signal IDs, then merges AI-diagnosed and non-AI results.
• Creates a consolidated record (including Slack message text and CRM payloads), assigns urgency and a due date, and writes the assessment to the drift_assessments table.
• Posts Slack alerts specifically for actionable medium/high drift cases.

Use cases

• SaaS operations teams monitoring PostgreSQL churn risk and prioritizing outreach.
• Automation engineers building a daily customer health pipeline with Postgres + Gemini + Slack.
• Customer success managers responding faster using evidence-based drift diagnostics.

Technical details

• PostgreSQL: reads drift features from v_customer_drift_features; pulls support_tickets and communications; writes results to drift_assessments.
• Google Gemini: used via a LangChain chat model to generate structured drift diagnosis.
• Slack: sends actionable alerts with urgency and recommended next action.
• n8n nodes listed in the workflow: if, code, merge, slack, postgres, sticky note.

Setup notes

Configure Postgres credentials and ensure required tables/columns exist, then add a Google Gemini (Google PaLM) API credential for the LangChain agent used to generate the drift diagnosis.