GTM operations · pre-pipeline layer
What’s
working?
The simplest question your CEO asks, and the one your reporting answers worst. Not because your team is careless — because the work that creates pipeline gets recorded in a dozen places that were never designed to reconcile.
Two questions, one stack
Point of view
Marketing works. Its proof
is what’s broken.
Most demand isn’t manufactured. It gets shaped long before anyone fills in a form — a podcast eighteen months ago, a peer who vouched for you in a Slack group, a reputation built over years of showing up. That is marketing. It’s the most powerful marketing there is. It’s also invisible to a model built around last-touch source fields.
So the model undercounts the work that matters most and overcounts whatever happened to be measurable. Then someone carries that model into a budget meeting, and the wrong line gets cut.
I’m not here to prove marketing works. I’m here to stop the measurement from arguing that it doesn’t.
One side has a schema.
The other doesn’t.
The moment an opportunity is created, your data gets disciplined. Required fields. Stage gates. Close dates. One system, one record, one shared key everything else can join to.
Everything that produced that opportunity gets no such treatment — outbound activity, events, referrals, warm intros, advisor and partner motions, website conversion. Each recorded in whichever tool it happened to run in, under whatever that tool calls things, with no key connecting it to the opportunity it created.
Before an opportunity exists
Ad platforms, email tool, event registrations, call and meeting logs, spreadsheets, someone’s inbox. Governed by nothing. No shared key.
After an opportunity exists
The CRM. Required fields, stage gates, validation rules. The opportunity record ties it all together.
This is structural, not a reflection of your team. No CRM enforces a schema on work that happens before its records exist — so the second answer gets assembled by hand, differently each quarter, by whoever is building the deck. It survives one meeting and then has to be rebuilt.
Until recently, fixing that was a data-engineering project — a warehouse, a modeling layer, a team, two quarters. The cost of connecting scattered activity data to CRM outcomes has collapsed over the past year. It’s now six weeks of focused work.
How I think about proof
Three kinds of claim
Not everything can be measured the same way, and pretending otherwise is where credibility goes to die. Three layers, three different standards of proof — and being explicit about which is which is most of the job.
Most measurement fails by applying one standard of proof to all three — either the same false confidence everywhere, or a shrug at anything that resists a clean number. The discipline is knowing which claim you’re entitled to make, and refusing the ones you aren’t.
The engagement
How the work actually runs
Six weeks, six phases, a concrete artifact at the end of each. You can see progress without taking my word for it.
17 recurring questions · 4 answerable · 6 partial · 7 guesswork
> map --sources --canonical
61 raw values → 10 canonical · 6 conflicts flagged for sign-off
> bridge --identity --precision-first
match 84% · L1 contact 61% · L2 lead 12% · L3 domain 11%
> assemble --into=existing-stack --schedule=nightly
reporting layer live
> reconcile --axes=count,value --against=live-crm
3 defects found · written to ledger
> brain --write definitions,decisions,methods,gotchas
your team owns it
Question inventoryWeek 1
We start with the questions your leadership actually asks — the ones in board decks, QBRs and planning sessions. Each gets marked: you can answer this today, you can half-answer it, or you’re guessing. Most teams are surprised how long the guessing column is.
One name per sourceWeeks 1–2
Your systems probably carry forty different labels for where pipeline came from, and several of them mean the same thing. We collapse them into about ten your whole company agrees on — so when marketing and sales bring numbers to the same meeting, they’re counting the same way.
Connecting the dotsWeeks 2–3
Most of your activity data isn’t connected to your CRM. A webinar registrant and the account that later bought are two unrelated rows. We connect them — working from the most reliable signals down to the least, and stopping before it turns into guessing. You’d rather match 80% correctly than 95% with the credit landing in the wrong place.
One place to lookWeeks 3–4
Everything lands in one view that refreshes on its own, inside whatever tool your team already opens. I build into your stack rather than handing you another login to remember.
Checking the numbersWeeks 4–5
Every number gets checked against your live CRM two ways — how many, and how much. Checking one way is how wrong numbers survive: two errors can agree on dollars and still be wrong on count. Anything that doesn’t tie gets written down rather than smoothed over.
The revenue brainWeek 6
Everything we decided gets written down — the definitions, the rules, why each call was made, and how you’d know if a number went stale. Your team can run this without me. So can the AI tools they use, which is increasingly the point.
Evidence
What this looks like when it lands
Reconciliation produces a defect ledger — every number that didn’t tie, why, and what it cost. These are the failure types it catches. Each one produced confident, plausible output. None raised an error.
Full ledger with figures available under NDA. TODO: swap for real numbers once client sign-off is in
AI enablement
The part that outlives
the engagement
Every company I’ve worked in has the same problem underneath the reporting one: the decisions live in people’s heads. Why “sourced” means what it means. Which field is the real one. What was tried in 2024 and quietly abandoned. When someone leaves, it goes with them.
A revenue brain is that context written down — in a form your people and your AI tools can both work from. Not a wiki nobody opens. A living record of what your team decided and why, kept current as a by-product of doing the work rather than as a documentation project someone has to be nagged about.
Definitions
What each metric means, and which field it actually comes from.
Decisions
What was chosen, when, and why the alternative was rejected.
Methods
How each recurring analysis is really run, step by step.
Gotchas
What breaks silently, and how you’d know it had.
Why this matters more than it used to. Most GTM teams have adopted AI and gotten inconsistent answers out of it. That’s usually not the tool. It’s that every prompt starts from nothing, so every answer quietly reinvents the definitions — and two people asking the same question get two different numbers, both confidently wrong.
Your AI is only as good as the context you can hand it. Most companies have never written that context down anywhere.
Qualification
Who this is for
Good fit
- Your pipeline data is trustworthy once an opportunity exists — the problem is everything before that
- Marketing and sales are measured as one motion, or you’re trying to get there
- Enough pipeline history that patterns exist to find — roughly Series B and up
- Someone asks “what’s working” on a schedule, and the answer takes days to assemble
- You’d rather own the system afterward than rent it indefinitely
Not a fit
- You need campaigns run — I don’t execute demand gen, though I partner closely with people who do
- You need a marketing ops pair of hands for day-to-day workflow and list work
- You have no CRM discipline yet — fix that first, this layer sits on top of it
- The work is net-new data engineering to be staffed in-house — I specify and verify that build, I don’t staff it
- You want a dashboard rather than an agreement about what the numbers mean
Working together
Three ways in
Smallest way in
The Revenue Brain
Your definitions, decisions and methods captured so your team and your AI tools work from the same context. The right first step if your reporting is fine but the answers coming out of it aren’t consistent.
The core engagement
The Pre-Pipeline Build
The six phases above, end to end. Fixed scope, fixed fee, fixed date. You finish owning a reporting layer, a defect ledger, and a runbook — whether or not we work together again.
Continue
Fractional GTM Operations
I hold the pre-pipeline layer as your teams change and your motions do. Methodology stays versioned, numbers stay reconciled quarterly, and new motions get instrumented before the next board deck rather than after it.
Start with your own question set
The fastest way to know whether this is worth a conversation: write down the three questions your leadership asks most about pipeline, and try to trace each answer back to the query that produced it. Not the dashboard — the query. Then check whether anyone else in the company computes it a different way.
Most people who run that exercise don’t like what they find. If that’s you, that’s the conversation.