CUSTOMER SUPPORT
Build a Monthly Knowledge-Base Roadmap from Ticket Trends
Monthly, aggregates ticket volume by theme over the last 30 days, compares each theme against existing articles.
How it runs
The automated pipeline, trigger to output.
- TriggerMonthly schedule fires
- ActionFetch 30 days of tickets with tags and handle timeZendesk
- ActionGroup into themes and estimate hours spentOpenAI
- ActionCross-reference themes against existing articlesZendesk
- LogicRank uncovered themes by time saved
- OutputWrite prioritized roadmap to NotionNotion
What it does
This gives content owners a data-driven plan for what to write next. It measures which topics drove the most tickets over the month, checks which already have articles, and ranks the rest by how much support time they'd save. The output is a clean, prioritized roadmap rather than a pile of raw drafts.
When to use it
Use it for monthly content planning when you need to justify what gets documented and in what order. Ideal for support and docs teams who report on deflection and want their roadmap tied to real volume.
How it works
- 1A monthly schedule fires the workflow.
- 2It fetches all tickets from the last 30 days with their tags and handle times.
- 3OpenAI groups tickets into themes and estimates total agent hours spent per theme.
- 4It cross-references each theme against existing help-center articles to flag covered vs. uncovered topics.
- 5A logic step ranks uncovered and stale themes by estimated time saved.
- 6The ranked roadmap, with theme, ticket count, hours saved, and status, is written to a Notion database for the planning meeting.
Set it up
What you configure once, before turning it on.
- 1Connect ZendeskTickets, queues, knowledge base.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect NotionPages, databases, comments.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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