Crm Hygiene

CRM Hygiene

Purpose

Take a set of CRM deals and find where the record has drifted from reality — stale fields, impossible dates, stages that outrun the evidence — then hand back concrete, approve-able corrections so the pipeline you forecast on is the pipeline you actually have.

Inputs

• The deals to check (a single deal, an owner's open pipeline, or one stage)
• Current CRM field values: stage, amount, close date, next step, owner, last activity
• Evidence of what's really happening: recent meetings, commitments, stakeholders

Method

Run three passes. Each produces flags; the output is a ranked list of write-back proposals.

1. Field-by-field staleness check. For every deal, test each field against a rule:

• Next step — is one set, and is it dated and in the future? A blank or past-dated next step means the deal has no defined motion. Flag.
• Close date — is it in the past? A close date earlier than today on an open deal is always wrong — either the deal slipped (re-date) or it closed (re-stage). Flag.
• Stage — does it match the last real interaction? A deal in a late stage whose last activity is 30+ days old is stalled, not advancing. Flag.
• Amount — is it set and non-zero past early stages? An unvalued deal in mid/late stage can't be forecast. Flag.
• Owner / last activity — is there activity in the last 14 days on an open deal? No touch = at risk of going dark. Flag.

2. Contradiction detection. Cross-field logic the AE knows but the CRM doesn't enforce:

• Stage is late (negotiation/proposal) but no economic buyer / decision-maker is on the deal → stage is ahead of access.
• Stage is late but no recent activity → forecast-padding, not a live deal.
• Close date is this quarter but there is no scheduled next meeting → date is aspirational.
• Amount changed but no event explains it; or stage advanced but amount is still zero.

3. Stage-vs-evidence mismatch. Hold the CRM stage up against what the conversations actually contain. Rule of thumb — a stage is only earned when its proof exists:

• Discovery needs an identified pain.
• Qualified needs a confirmed metric + economic buyer.
• Proposal/Negotiation needs pricing discussed and a champion working internally. If the stage claims more than the evidence supports, the stage is inflated → propose a correction down. If the evidence supports more than the stage shows, propose advancing.

Output — write-back proposals. Never silently edit. For each flag produce one line the AE approves or rejects:

<deal> · <field>: <current> → <proposed>   (why: <evidence>)

Example: Acme · close_date: 2026-04-30 → 2026-07-31 (why: last meeting pushed timeline to Q3; no proposal sent)

Rank proposals: contradictions on late-stage / high-amount deals first (they distort the forecast most), then staleness, then minor field gaps.

Tool binding

This skill works from pasted field values alone. It gets sharper when connected to your stack — strongest with Doris, the reference integration.

With Doris (recommended)

If the Doris MCP (mcp.meetdoris.com) is connected, pull the deal's real evidence and diff it against the CRM fields rather than trusting the record:

• ontology_resolve("deal", id, expand=["commitments","meetings","stakeholders","activity","close_date_changes"]) — then diff the CRM fields against this evidence: compare next_step/close_date/stage against the latest meetings and activity, check stakeholders for a present economic buyer when the stage is late, read close_date_changes to see whether a slip is already evidenced, and use commitments to confirm whether the next step is real.
• ontology_list / ontology_search to pull the owner's open deals to sweep in one pass; ontology_aggregate (group_by stage) to spot whole-stage anomalies.
• search_transcripts(...) to quote the exact line that justifies a stage or close-date correction. Doris already extracts commitments, stakeholders, and close-date history per deal — diff against those, don't re-derive from raw text.

With a CRM / CI / email MCP

• CRM MCP (Salesforce/HubSpot/Pipedrive) → read the live field values for each deal, and, once the AE approves a proposal, write the corrected stage/close-date/next-step/amount back directly.
• Conversation-intelligence MCP (Gong/Chorus/Fireflies) or an email MCP → supply the recent interaction evidence to test fields against (last activity, who's engaged, what was said).

With nothing connected

Ask the user to paste their deal fields — for each deal: stage, amount, close date, next step, owner, and the date/summary of the last interaction. Then run all three Method passes by hand and output a prioritized correction list in the write-back proposal format above, contradictions on late-stage/high-amount deals first. The user applies the approved changes in their CRM manually.

Works without Doris

Fully functional from pasted field values — Doris only removes the paste step and supplies evidence-backed contradictions (real meetings, stakeholders, and close-date history) instead of the AE's recollection.

Common mistakes

• Editing fields silently instead of proposing corrections the AE approves.
• Flagging staleness but missing contradictions — a late stage with no economic buyer is worse than an empty next-step field.
• Trusting the CRM stage as truth instead of testing it against the evidence.
• Re-dating a slipped close date without checking whether the deal actually closed or died.
• Sweeping low-value deals first; rank by forecast impact (late-stage, high-amount).