Extract the repeatable reason you win.
“What do our best deals have in common, and which open deals look like them?”
Reads your won deals, finds the signals, personas, and motions that repeat, and ranks active lookalikes to work.
What this skill does
Reads your closed-won deals and finds what they have in common: the segments you win, the personas who sign, the motions that work, and the competitive spots where you come out ahead. It turns that into one target profile you can hand a rep, then ranks your active pipeline by how closely each open deal matches your winners. The point is not a nice chart. It is a shorter list of accounts that look like the ones you already closed.
Inputs
- Works today with: a list of your closed-won deals, with segment or size, industry, win date, deal size, sales cycle length, primary contact title, source, and (if you have it) the competitor and the primary use case. Paste it or upload a CSV.
- More powerful connected to a CRM: it reads all of the above automatically, across every won deal in the window you pick.
- Sharper with a meeting or transcript tool: pulls the actual words buyers used, so the pattern is "why they said yes," not just the fields.
- Sharper with a product-analytics tool: adds which features the winners adopted first, so the profile includes real behavior.
How it works
- 1Pattern extraction (what repeats)
Group every won deal across your segment, persona, motion, competitive, and use-case fields. Report only the patterns that clear MIN_SAMPLE, so one lucky whale never becomes a "profile." For each pattern, show the share of wins, the average deal size, and the average cycle length, so a common-but-small pattern is not confused with a rare-but-huge one.
- 2Win-signal ranking
Rank the patterns by lift, not raw count. A signal earns its place when won deals show it far more often than the base rate of all your deals. "Half our wins are mid-market" means nothing if half your pipeline is mid-market. The signals that matter are the ones over-represented in wins.
- 3Target profile (the ICP you actually close)
Fold the top signals into one plain-language profile: the segment, the persona who signs, the motion that lands them, the use case that resonates, and the competitive setup where you win. This is not the ICP on the website. It is the ICP your win data votes for.
- 4Lookalike ranking (active pipeline)
Score every open deal by how many profile signals it hits. Return a ranked list, best matches first, with the signals each one shares with your winners and the ones it is missing. This is the payoff: a shorter list of open accounts that look like deals you already won.
Output format
CLOSED-WON PATTERNS · 34 wins, trailing 4 quarters
Top win signals (by lift over base rate)
Signal Share of wins Avg size Avg cycle
Mid-market, 200-800 staff 62% $41K 38 days
Champion = Head of Ops 55% $44K 35 days
Inbound demo request 48% $39K 29 days
Displaced a manual/DIY setup 44% $47K 33 days
Target profile (what you actually close):
Mid-market ops leader, inbound, replacing a manual process.
Fast cycle, above-average deal size.
Active lookalikes (open pipeline, best match first)
Account Signals matched Missing
Haledon 3 of 4 (seg, persona, mo) no competitor read
Brightsea 3 of 4 (seg, persona, uc) outbound, not inbound
Corverin 2 of 4 (seg, use case) enterprise size
Next move:
1. Work Haledon and Brightsea first. They match your winners on 3 of 4.
2. Add a competitor field to your next 10 wins. It is your thinnest signal.
Tips
- No pattern reported below MIN_SAMPLE. Small samples lie, and one big logo is not a trend.
- Signals ranked by lift over base rate, never by raw count alone.
- Every lookalike shows the specific signals it matches, never just a score.
- Competitive and use-case claims come from a field or a transcript, never invented.
Categorize your losses and name the fix.
Sorts lost deals into no-decision, price, competitor, timing, or fit, pulls competitive intel, and splits preventable from structural.
Turn churn themes into an early-warning system.
Reads churned and downgraded accounts, extracts the themes, and flags active accounts that resemble recent churners.