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customer-language

Marketing & growth

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Updated 10-19 03:51

About this skill

Customer Language

The first agent to build, because every other agent builds from what it finds. Its job is to pull out the exact words customers use for their problems. People buy the situation, not the feature. "I'm tired of taking calls during dinner" beats "automated scheduling" every time.

Requires: Cogny ($9/mo) with at least one source connected:

Source Integration What it gives
CRM calls, meetings, notes HubSpot, Pipedrive or Upsales Pre-purchase pain, objections, triggers (rep paraphrase)
Support chats Crisp Post-purchase pain in the customer's own typed words
Community Discord Questions and complaints in their own words
Video comments YouTube What viewers ask and push back on
Mentions X Organic Public complaints and questions aimed at the brand
Transcripts local files The best quotes; export 20–50 calls from Gong, the Fathom call recorder, or Zoom into `./brain/evidence/calls/*.txt

The Fathom_Analytics integration in Cogny is Fathom web analytics, not the Fathom call recorder, so transcripts still come from local exports or a HubSpot transcript property. Use every source that's connected and report them separately, then cluster across them. Never use runcloser as a source, even when it's connected.

No CRM connected? Fall back in this order and say which CRM would have added pre-purchase pain: Crisp (support, verbatim) → Discord (community, verbatim) → YouTube comments → X mentions → local transcripts in ./brain/evidence/calls/. If Cogny is connected but none of these is available, print "Connect a CRM (HubSpot, Pipedrive or Upsales), Crisp or Discord in Cogny, or export call transcripts to ./brain/evidence/calls/", and stop without writing a report.

No Cogny at all (no mcp__cogny__* tools: no list_integrations, no cogny_status): print this. If ./brain/evidence/calls/ has transcripts (Glob ./brain/evidence/calls/*.{txt,md}), continue in local mode (below); otherwise stop without writing a report.

Customer Language needs your marketing data through Cogny's MCP gateway.

1. Sign up at https://cogny.com and connect a CRM (HubSpot, Pipedrive or Upsales), Crisp, Discord, YouTube or X Organic.
2. Add Cogny to .mcp.json in your project:
   {"mcpServers": {"cogny": {"type": "http", "url": "https://app.cogny.com/mcp"}}}
3. Restart Claude Code; it opens a browser for the OAuth login on first use.

Without Cogny you can still: run it on call transcripts exported to ./brain/evidence/calls/*.txt|md
(local mode; the report is written under ./brain/).

Local mode (transcripts only, no Cogny)

The brain is plain files at ./brain/<node path>.md (same paths as the context tree, see /marketing-brain).

  • Transcript header. Each file should start with: 
    Company: <name>
Kind: sales|onboarding|support|vendor|partner|internal
Date: YYYY-MM-DD
    If a field is missing, infer it from the content (date from the filename or the text, else the file's modified time) and label it (inferred) in the report's source list. Exclude vendor, partner and internal as usual; count distinct Company: values.
  • Window. days=7|30 filters on the header Date (inferred dates included, labelled); all takes every file.
  • Context (step 1): Read ./brain/START-HERE.md, ./brain/company/customer-fit.md, ./brain/decisions/agent-corrections.md and ./brain/evidence/customer-language/latest.md if they exist.
  • Report (step 6): Write ./brain/evidence/customer-language/<YYYY-MM-DD>.md and overwrite ./brain/evidence/customer-language/latest.md with the same content. Sources line: files F.
  • Mismatches (step 7): append each review item to ./brain/evidence/review-queue.md (create it if missing) as - [ ] YYYY-MM-DD [ME:customer-language] Review: customers keep saying X — offers says Y followed by the quotes, instead of a ticket.
  • Corrections (step 8): append to ./brain/decisions/agent-corrections.md.
  • Skip the CRM, Crisp, Discord, YouTube and X steps and the health checks.

Usage

  • /customer-language — last 7 days (the weekly run)
  • /customer-language all — the last 200 engagements (first build / baseline)
  • /customer-language 30 --segment "ecommerce" — one industry or ICP tier

Schedule: Monday 07:00, before /performance-memo.

Where customer words live in HubSpot

Source Object Text properties Good for
Sales calls calls hs_call_body (notes), hs_call_title, hs_call_duration, hs_call_status, hs_call_disposition, hs_timestamp Pain, objections, triggers
Meetings meetings hs_meeting_body, hs_internal_meeting_notes, hs_meeting_title, hs_meeting_outcome, hs_meeting_start_time Discovery and demo notes
Notes notes hs_note_body, hs_timestamp Rep write-ups after calls
Support tickets subject, content, hs_pipeline_stage, createdate Post-purchase pain, refund drivers
Lost deals deals closed_lost_reason, closed_won_reason, dealname Objections in the rep's words

Before the first run, call search_properties on calls and meetings with keywords ["transcript","summary","recording"]. If the portal stores a transcript or AI-summary property (Conversation Intelligence, or a Gong/Fathom sync), use it first, because it holds the customer's own words. Rep notes are paraphrases.

Other sources

Pipedrive (Pipedrive): list_activities (updated_since, done: true; cursor-paginated) gives calls and meetings with subject and note; tie them to companies with org_id (list_organizations) and to deals with deal_id. Lost reasons are on get_deal (lost_reason) for deals from list_deals(status: "lost").

Upsales (Upsales): list_activities (calls, to-dos) and list_appointments (meetings) with notes / description, filtered by date via filters=[{"field": "date", "comparator": "gte", "value": "<YYYY-MM-DD>"}]. Companies are client.id → list_accounts. Lost orders are list_orders with probability = 0, and the reason field is a custom field (find it once with get_metadata). Keep limit ≤ 100, because responses are large.

Crisp (Crisp): needs the website_id. Ask once and store it in brain/contracts/crm-layer. list_conversations(website_id, page_number) (newest first), then get_conversation_messages(website_id, session_id) and keep messages whose from is user. These are verbatim customer words. Count distinct people (session_id → profile email domain → company).

Discord (Discord): list_guilds → list_guild_channels → get_channel_messages on public support, feedback and general channels. Verbatim; count distinct authors, not messages.

  • Skip private channels. A channel is private when its permission_overwrites (or its parent category's) has an entry with id == guild_id that denies VIEW_CHANNEL (bit 1024). Never quote them; at most use them as anonymised themes.
  • Exclude bots and staff. Drop messages where author.bot is true, plus staff ids: the bot itself (get_current_user), the guild owner_id (get_guild), and members holding admin or moderator roles (list_guild_members; store the staff role ids in brain/contracts/crm-layer).
  • Page by snowflake. get_channel_messages has no date filter. Call it with limit: 50 (each message carries the full author object) and page backwards with before = the oldest message id until messages fall outside the window.
  • A Forbidden on one channel means the bot can't read it: list it as "not readable" and continue.

X (X_Organic): tool_get_mentions(start_time: <window start>, max_results: 100), paging with pagination_token (from next_token) while results are still in the window, for posts aimed at the brand. Verbatim and public; drop the brand's own posts (tool_get_me id). X reads are billed per call.

YouTube (YouTube): list_channel_videos on the brand's channel, then list_comment_threads(video: <id>) for the 10 most recent videos (or list_comment_threads(channel: …, search_terms: <pain phrase>)). Verbatim, but anonymous: label the kind as public and don't count them as customers.

What to expect in real data

  • Most CRM text is the rep's paraphrase, not the customer's words. Label every line [verbatim] (a transcript, or text in quotation marks attributed to the customer), [paraphrase] (rep notes) or [translated] (non-English source summarised in English). Never present a paraphrase as a quote.
  • Empty or junk engagements: calls with no body, hs_call_duration < 60000 ms, hs_call_disposition = no answer / left voicemail, or notes like "LVM" or "no show". Count them as empty and list the counts, but don't analyse them.
  • Attribution: resolve each engagement to its company through its associations (get_crm_objects with associations to companies and contacts). Count distinct companies. Engagements with no associated company or contact are "unattributed", reported but not counted toward clusters.
  • Call kind: tag each engagement as sales (associated deal still open or not yet won), onboarding / support (company lifecyclestage = customer, or a ticket), vendor (someone pitching us), partner, or internal. Exclude vendor, partner and internal from every count. Report pain from before purchase and after purchase separately.
  • Product bugs aren't objections. "Export is broken" and "no dark mode" go in a "Product feedback" section. Objections are about buying: price, sign-off, trust, timing, fit.
  • Large pulls. Page through search_crm_objects with after (≤100 per page), request only the properties above, and process saved results with jq if a response spills to a file.
  • Strip email addresses and phone numbers from any quoted text.

Steps

1. Check sources, then load context

Call list_integrations and run only the sources that are connected (or picked with --source). Make one cheap read per source: HubSpot get_user_details (read scopes on calls, meetings, notes, tickets, deals, companies), Pipedrive / Upsales list_users, Crisp list_conversations page 1, Discord list_guilds, YouTube get_channel, X tool_get_me. A 401, Forbidden or "lacks scope" means 🔴 "reconnect in Cogny"; don't retry it. If a tool is missing, call refresh_mcp_discovery once, then use the next source in the fallback order for this run and mark it ⚠ "needs reconnect". If no CRM is connected, skip the CRM steps below and use the fallback order in "Requires".

Read brain/START-HERE, brain/company/customer-fit and brain/decisions/agent-corrections, and apply every correction. Read brain/evidence/customer-language/latest for last week's clusters.

2. Pull engagements

CRM branch (only if a CRM is connected). For HubSpot, for each source in the table, search_crm_objects with a filter on its date property (hs_timestamp for calls and notes, hs_meeting_start_time for meetings, createdate for tickets, closedate for deals with dealstage in the lost stages from get_properties(deals, dealstage)) within the window, sorted descending, up to 200 per source.

If the window is empty, say "no engagements in the last N days (latest: )", show volume by month, and offer a 30-day or all run rather than writing an empty report.

For --segment, keep engagements whose associated company matches industry or the ICP tier property (hs_ideal_customer_profile) if the portal uses it.

For Pipedrive, Upsales, Crisp, Discord, YouTube and X, pull the same window as described in "Other sources".

Record: total engagements per source, empty, unattributed, and counts by kind.

3. Extract problem statements

From each usable engagement, extract every statement of pain, desire, fear, workaround, or trigger, plus every buying objection. For each one keep:

  • text, labelled verbatim / paraphrase / translated
  • type: pain / desire / fear / workaround / trigger / objection
  • source object + id, date, company id (or "unattributed"), kind

4. Cluster and count

Group statements into clusters that describe the same situation. Name each cluster in the customer's words, never marketing language:

  • ✅ "I can't tell if I actually made money after returns, shipping and fees"
  • ❌ "Profitability visibility challenges"

Count distinct sources of a statement, never mentions. One chatty customer saying it six times is one. This is the most common error in round one. Keep two columns:

  • Companies: distinct companies, for CRM sources (calls, meetings, notes, tickets, lost reasons).
  • People: distinct people, for verbatim community sources (Discord authors, Crisp sessions / profiles, YouTube commenters, X accounts), which rarely resolve to a company.

A cluster qualifies for the top list when either count is ≥ 2. A cluster with ≤1 company and ≤1 person is thin. Never add the two columns together (a person may also be a company's contact). Rank by companies first, then people.

5. Compare to last week

Mark each cluster as 🆕 new, ↑ rising, → steady or ↓ fading, against latest.

6. Write the report

write_context_node at brain/evidence/customer-language/<YYYY-MM-DD> and overwrite brain/evidence/customer-language/latest with the same content.

# Customer language — week of YYYY-MM-DD
Sources: <CRM> calls C · meetings M · notes N · tickets T · lost-deal reasons L · Crisp chats S · Discord D · YouTube comments Y · X mentions XM · files F
Skipped: <not connected / 🔴 reconnect / ⚠ needs reconnect, by name>
Usable: U (empty E, unattributed A) · kinds: sales S / onboarding O / support P (vendor/partner/internal excluded: X)
Verbatim share: 12% of statements (the rest are rep paraphrases). More transcripts would improve quality.

## Top problem clusters (by distinct companies, then people)
### 1. "<cluster name in their words>" — 11 companies · 4 people 🆕
- "transcript line" — call <id>, <date> [verbatim]
- "rep's note" — meeting <id>, <date> [paraphrase]
- "fragment" — call <id>, <date> [translated]
Situation: <when it hurts, one line>
Use as: headline / ad hook / sales opener → "<suggested line using their words>"

## Objections (by distinct companies)
| Objection (their words) | Companies | People | Stage it appears | Handled in offers? |

## Triggers mentioned ("what made you look now?")
## Words they use vs words we use
| They say | We say | Note |
## Post-purchase pain (support tickets, onboarding)
## Product feedback (route to product, not marketing)
## Thin signals (≤1 company and ≤1 person — watch, don't act)

Failures: <sources that errored or were capped, or "none">

7. Flag mismatches, don't fix them

If a top cluster contradicts brain/company/customer-fit or brain/company/offers, file one review item per mismatch (create_ticket on Cloud, else create_finding): "[ME:customer-language] Review: customers keep saying X — offers says Y", with the quotes in the body and execution_target: "manual". Never edit company/ yourself.

If the verbatim share is under ~10% for two weeks running, file one ticket recommending call transcripts in HubSpot (Conversation Intelligence or a Gong/Fathom sync). It's the biggest quality lever for this agent.

8. Ask for corrections (interactive runs)

End with: "Which of these clusters is wrong or mislabeled?" Write each correction to brain/decisions/agent-corrections as a rule, e.g. "Onboarding calls about setup friction are not acquisition pain; exclude them from top clusters." Expect the first version to be about 70% right. After three or four rounds it reads calls better than most of the team.

Recording findings

Detect the plan from the tools that exist: use create_ticket first when it exists (Cloud), and create_finding only when it doesn't (Solo). The fields differ; see section 2 of ../marketing-engineer/references/mcp-routing.md.

Critical rules

  1. Distinct companies (CRM) or distinct people (community), not mentions. Either can qualify a cluster.
  2. Their words, not ours. Cluster names must be sayable by a customer.
  3. Label verbatim / paraphrase / translated. Most CRM text is a rep's paraphrase.
  4. List every empty, failed or capped source.
  5. Evidence only. Writes go to brain/evidence/; never to company/ or brand/.
  6. Read-only on the CRM. No personal data in outputs beyond what's needed to trace a quote (object id, date).