CUSTOMER SUPPORT

Feature-request miner that links Intercom themes to HubSpot revenue

Daily, extract feature requests from Intercom conversations, dedupe them into canonical themes.

CategoryCustomer Support
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule fires
  • ActionFetch day's Intercom conversations with requester infoIntercomIntercom
  • ActionExtract and dedupe feature requests into themes (OpenAI)OpenAI
  • ActionMatch requesters to HubSpot companies and ARRHubSpotHubSpot
  • LogicAggregate revenue and count per theme
  • OutputPost revenue-weighted theme rankingSlack

What it does

Finds feature requests buried in support conversations and answers the question product really cares about: how much money is asking for this? It pulls Intercom conversations, extracts and de-duplicates requests into canonical themes, matches each requester to their HubSpot company, and sums the associated deal or ARR value per theme.

When to use it

Use it when feature prioritization keeps devolving into whoever-shouted-loudest. This gives a revenue-weighted backlog: each theme ranked by the total account value behind it.

How it works

  1. 1A daily schedule fires the run.
  2. 2Pull the day's Intercom conversations with the requester's email and company.
  3. 3An OpenAI step extracts explicit feature requests and maps each to a canonical theme, merging paraphrases.
  4. 4For each requester, look up the matching HubSpot company and its deal/ARR value.
  5. 5A logic step aggregates revenue and request count per theme.
  6. 6Output the revenue-weighted theme ranking to a shared sheet and the product channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect IntercomConversations, contacts, articles.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect HubSpotCRM, deals, marketing, support.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    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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