CUSTOMER SUPPORT

Weekly Macro Miner: Cluster Resolved Tickets into Draft Canned Replies

Every week, pulls the last 7 days of solved Zendesk tickets, clusters the ones answered with similar free-text replies.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule fires
  • ActionFetch tickets solved in last 7 daysZendeskZendesk
  • ActionCluster near-duplicate reply textOpenAI
  • LogicKeep clusters with 3+ tickets
  • ActionDraft macro title and body per clusterOpenAI
  • OutputCreate review cards in NotionNotionNotion

What it does

Finds the answers your agents keep retyping. Once a week it scans recently solved tickets, groups the ones whose resolution replies say essentially the same thing, and turns each cluster into a ready-to-edit macro draft — title, suggested body, and the example tickets it came from — parked in Notion for a lead to approve.

When to use it

When your team resolves the same questions by hand instead of with macros, and nobody has time to audit the backlog to find the patterns. Run it as a standing weekly ritual so the macro library grows from real answers, not guesswork.

How it works

  1. 1A weekly schedule fires the run.
  2. 2Fetch all tickets solved in the last 7 days from Zendesk, including the agent's public reply text.
  3. 3Send the replies to OpenAI to cluster near-duplicate answers and label each cluster by intent.
  4. 4Filter out clusters with fewer than 3 supporting tickets so only repeatable answers advance.
  5. 5For each surviving cluster, have OpenAI draft a clean macro title and body from the example replies.
  6. 6Create one Notion card per draft with the suggested macro and links to source tickets for review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZendeskTickets, queues, knowledge base.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect NotionPages, databases, comments.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    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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