Closed-won analysis is the practice of mining 2-3 years of your won deals to find the buying signals that preceded them. For each customer you look back 12-24 months at hiring, executive, and funding activity, find the patterns that repeat across wins, and turn them into a repeatable targeting model.
Ask most sales leaders why they win and you get a story: good champion, right timing, strong product fit. Ask them to prove it with data and the room goes quiet. Closed-won analysis replaces the story with a pattern, one you can point new pipeline at.
This is the first stage of the GTM intelligence flywheel. Before you can find companies that look like your best customers, you have to know what your best customers looked like before they bought.
What is closed-won analysis?
It is a backward look at your won deals to find the external signals that showed up before the deal existed. Not the CRM fields you already track (source, stage, close date), but what was happening inside the account: who they hired, who they promoted, what they filed, how their team grew. Every buyer leaves a trail before they buy. Closed-won analysis reads it.
How do you run a closed-won analysis?
- Pull every closed-won deal from the last 2-3 years. You want enough volume to see a pattern, not a coincidence.
- For each account, look back 12-24 months before the close date at external activity: hiring patterns, executive hires, tech stack shifts, org restructuring, and funding events.
- Find what repeats across wins. If 8 of your last 10 enterprise deals hired a new VP of Sales in the year before they bought, that is a pattern, not a fluke.
- Run the same pass on closed-lost deals. What was present in the wins and missing in the losses is often more telling than the wins alone.
- Segment everything by ICP, vertical, company size, and deal type. The pattern behind a logistics win will not carry over to a cybersecurity one.
What signals should you look for?
You are not looking for a single trigger. You are looking for combinations, because one signal is coincidence and stacked signals are a pattern. These are the categories worth pulling for every won deal:
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| Analysis | The question | What it tells you |
|---|---|---|
| Win signals | Which signals appeared before deals closed? | The core pattern to target on net-new accounts |
| Speed signals | Which appeared before fast closes vs slow ones? | Which accounts to prioritize this quarter |
| Size signals | Which correlated with larger deal size? | Where to point your senior reps |
| Champion detection | Did a specific hire precede fast closes? | That hire may be the real buyer to watch for |
| Negative signals | Layoffs, hiring freezes, leadership gaps | The do-not-call list that saves rep time |
Most companies have never seen their own win data analyzed against external signals.
Sentrion, Pillar 1: LEARN
Who owns it, and who runs it?
The mandate belongs to the VP of Sales or CRO. They own the question "why do we win," and the answer changes how the team targets, prioritizes, and staffs deals. RevOps executes: pulling the export, doing the lookback, and segmenting the results. The leader sets the question, the operator produces the pattern, and the whole team sells against it.
Can you run closed-won analysis manually?
Yes, for a first pass. Export your deals, pull public history on each account by hand, and eyeball the patterns. It works up to a few dozen deals. Past that, reconciling 12-24 months of hiring, executive, and filing history per account by hand stops scaling, and the lookback data gets thin the further back you go.
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| Manual | Automated | |
|---|---|---|
| History depth | Whatever you can find today | 6 years of structured signal history |
| Coverage | A few dozen deals before it breaks | The full list you hand it, not just the ones you had time for |
| Refresh | One-time snapshot | Re-run on demand when you want the pattern checked again |
| Sources | Manual searches, one account at a time | 230+ job boards, exec moves, SEC filings cross-referenced |
Sentrion supplies the history this analysis needs: 6 years of structured signal data across 230+ job boards, SEC filings from EDGAR full-text search, executive movement, and company web and news, with every claim tied back to the source it came from. Scoring the live market against a pattern is the FIND stage, which ships today as Motion in early access. Mining your CRM to build that pattern automatically is Pillar 1, and it is product direction rather than a shipped feature. Today the pattern is one you define, from your own read of your wins. This post is how you produce it.