CUSTOMER SUPPORT

CSAT Detractor Make-Good Drafter

When a survey response scores 1-3 (a detractor), this drafts a personalized apology and make-good email.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerwebhook
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerCSAT survey response receivedHTTP webhook
  • LogicKeep only scores 1-3 (detractors)
  • ActionFetch recent Intercom conversationsIntercomIntercom
  • ActionDraft personalized make-good emailOpenAI
  • ActionPlace tentative recovery call holdGoogle CalendarGoogle Calendar
  • OutputCreate review-ready Gmail draftGmailGmail

What it does

Turns a low CSAT score into a ready-to-send recovery package. It reads the detractor's verbatim comment and recent ticket history, drafts a specific make-good email (apology + concrete remedy), and pre-schedules a follow-up call so nothing slips.

When to use it

Use it when your support survey fires a low score and you want a same-day, human-reviewed response instead of a generic auto-reply. Best for teams that treat every detractor as a save-the-account moment.

How it works

  1. 1A new CSAT survey response arrives via webhook from your survey tool.
  2. 2A filter checks the score; anything 4-5 stops here, only 1-3 continues.
  3. 3The flow pulls the customer's recent Intercom conversations for context.
  4. 4An OpenAI step drafts a personalized apology referencing the actual complaint and proposes a make-good (credit, expedited fix, or escalation).
  5. 5A 15-minute recovery call is tentatively placed on Google Calendar 1-2 business days out.
  6. 6The draft email plus the calendar hold land in Gmail as a draft for the agent to review, edit, and send.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect IntercomConversations, contacts, articles.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect Google CalendarEvents, attendees, availability.
  5. 5
    Connect GmailRead, draft, send, label.
  6. 6
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  7. 7
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  8. 8
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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