---
name: revenue-operations
description: Runs the mechanics of the revenue engine — lead lifecycle definitions, routing, CRM hygiene, forecasting process, pipeline reporting, and the marketing-to-sales handoff. Use this to fix a broken handoff, define lifecycle stages, improve forecast accuracy, clean up CRM data, design territory or routing rules, or diagnose why pipeline numbers are not trusted.
---

# Revenue operations

## Definitions before dashboards

Most revenue reporting arguments are definitional. Write down and get agreement on, in one place:

- What each **lifecycle stage** means and the observable event that moves a record into it.
- What makes a lead **qualified** — and by whose judgment.
- When an opportunity is **created**, and what evidence is required.
- What each **pipeline stage** requires to be entered, stated as a buyer action rather than a seller
  feeling. "Prospect has confirmed budget" is observable; "showing strong interest" is not.
- What **closed-lost** means versus stalled, and when a stalled deal exits the pipeline
  automatically.

Without these, every number is negotiable and forecasting is a genre of fiction.

## The handoff

Where most revenue leaks. Specify: the exact criteria for passing a lead, the SLA for first contact,
what context transfers with it, and the route back when it is rejected — including the reason,
recorded.

A rejection loop with no recorded reason means marketing keeps sending the same unqualified leads,
and both sides believe the other is the problem.

## Lead scoring

Scoring exists to route attention, not to produce a number. If sellers do not change what they work
on because of the score, it is decoration.

Score on two independent dimensions and keep them separate:

- **Fit** — do they look like a customer? Company size, industry, geography, role and seniority,
  technology in use. Static, knowable before any engagement.
- **Intent** — are they acting like a buyer now? Pricing page visits, repeat sessions, demo request,
  content depth, response to outreach. Dynamic, and it decays.

Collapsing the two into one score is the standard mistake: a perfect-fit account with no activity
and a poor-fit account browsing aggressively land on the same number and get treated identically,
which is wrong in both directions.

Build the model from closed-won and closed-lost history, not intuition. Look at what actually
separated the two, and be prepared for the finding that a favored attribute has no predictive value.

Decay intent scores over time and recalibrate on a schedule. A scoring model built once and never
revisited drifts as the market and the product change, and nobody notices because it keeps producing
numbers.

## Forecasting

Forecast accuracy comes from process, not optimism.

- Stage-based probabilities derived from your own historical conversion, recalculated periodically —
  not from defaults.
- Commit, best case, and pipeline reported separately.
- Every forecasted deal has a date and a next step. A deal with neither is not in the forecast.
- Track forecast accuracy itself, by rep. It is the only way to know whose numbers to trust and it
  improves quickly once measured.

## CRM hygiene

Data quality decays continuously. Required fields at stage gates, validation at entry, scheduled
duplicate merges, and automatic aging of stale records. Rely on discipline alone and the data will
be unusable within two quarters.

Never require a field whose value is not used in a decision. Every unnecessary field trains sellers
to enter garbage in all of them.

## Sources

`references/sources.md` in this skill lists the outside authorities that settle the questions
here — what each one is authoritative for, and what you may do with it. Check them before
answering on anything they cover, and cite what you used. Most are free to read and not free
to reproduce; the use note on each is binding.

## Tooling

CRM: Salesforce, HubSpot, Pipedrive, Zoho CRM, and similar. The CRM is the record for
pipeline. Anything that disagrees with it is a report, not a number.

Enrichment and routing: Clay, ZoomInfo, Apollo, Chili Piper, and similar.

Forecasting and conversation data: Clari, Gong, and similar — useful once you have enough
deals for a pattern to mean anything.

## Never

- Change a definition without restating history on the new one. A metric that moved because you redefined it is not a result.
- Report a forecast number you cannot trace back to named deals.
- Let two systems each keep their own version of the same field. Pick the source of truth and make the other read from it.
- Score leads with a model you cannot explain to the reps who have to work them.

## Return contract

State the definitional gaps found, the process change proposed, what it costs sellers in time, and
the metric that will show it worked.