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Monitor customer risk and AI feedback using PostgreSQL, Gmail and Discord

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Created by: Avkash Kakdiya || itechnotion

Avkash Kakdiya

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Last update 6 hours ago

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How it works

This workflow monitors customer health by combining payment behavior, complaint signals, and AI-driven feedback analysis. It runs on daily and weekly schedules to evaluate risk levels, escalate high-risk customers, and generate structured product insights. High-risk cases are notified instantly, while detailed feedback and audit logs are stored for long-term analysis.

Step-by-step

  • Step 1: Triggers & mode selection

    • Daily Risk Check Trigger – Starts the workflow on a daily schedule.
    • Weekly schedule1 – Triggers the workflow for weekly summary runs.
    • Edit Fields3 – Sets flags for daily execution.
    • Edit Fields2 – Sets flags for weekly execution.
    • Switch1 – Routes execution based on daily or weekly mode.

  • Step 2: Risk evaluation & escalation

    • Fetch Customer Risk Data – Pulls customer, payment, product, and complaint data from PostgreSQL.
    • Is High Risk Customer? – Evaluates payment status and complaint count.
    • Prepare Escalation Summary For Low Risk User – Assigns low-risk status and no-action details.
    • Prepare Escalation Summary For High Risk User – Assigns high-risk status and escalation actions.
    • Merge Risk Result – Combines low-risk and high-risk customer records.
    • Send a message4 – Sends the customer risk summary via Gmail.
    • Send a message5 – Sends the same risk summary to Discord.
    • Code in JavaScript3 – Appends notification status and timestamps.
    • Append or update row in sheet3 – Logs risk evaluations and notification status in Google Sheets.

  • Step 3: AI feedback & reporting

    • Get row(s) in sheet1 – Fetches customer records for feedback analysis.
    • Loop Over Items1 – Processes customers one by one.
    • Prompt For Model1 – Builds a structured prompt for product feedback analysis.
    • HTTP Request1 – Sends data to the AI model for insight generation.
    • Code in JavaScript – Merges AI feedback with original customer data.
    • Append or update row in sheet – Stores AI-generated feedback in Google Sheets.
    • Wait1 – Controls execution pacing between records.
    • Merge1 – Prepares consolidated feedback data.
    • Send a message1 – Emails the final AI-powered feedback report.

Why use this?

  • Detect customer churn risk early using payment and complaint signals
  • Automatically escalate high-risk customers without manual monitoring
  • Convert raw customer issues into executive-ready product insights
  • Keep a complete audit trail of risk, feedback, and notifications
  • Align support, product, and leadership teams with shared visibility