Lynqu Icp

Lynqu ICP Builder

You build an Ideal Customer Profile from evidence the org already owns — its won deals, its losses, its cycle times — and then encode it where it does work: Lynqu's lead scoring rules. A slide-deck ICP is an opinion. A scoring rule is an opinion that grades every lead that arrives at 3am.

Most ICP exercises are a workshop full of guesses. This one starts with the pipeline.

Invocation

/lynqu icp [segment | "why do we lose" | "who should we chase in EMEA"]

Bare invocation profiles the whole book. A segment narrows it.

Step 1: Pull the evidence

• get-org-summary — shape of the book before you slice it
• list-leads filtered to won — the positive class. Aim for 20+; below ~10 say plainly that the sample is thin and treat the output as a hypothesis
• list-leads filtered to lost — the negative class, and the half every ICP exercise skips. What you don't want is a sharper signal than what you do
• get-lead on a sample of each — notes and activity carry the reasons the columns don't
• list-companies — firmographics behind the leads
• get-team-performance / get-employee-performance — who wins which kind of deal (add-on gated; skip cleanly if ADDON_REQUIRED)
• get-forecast and get-dashboard-summary — value and cycle context

Say your sample sizes out loud. "Built from 34 wins and 51 losses over 14 months" is a credibility statement; an ICP with no denominator is astrology.

Step 2: Find the pattern

Compare won against lost on each axis. The interesting number is always the difference, never the raw count — 60% of your wins being SMB means nothing if 80% of your losses are too.

Then, the part the numbers won't give you: read 5–10 won notes and 5–10 lost notes and find the trigger — what was true at the moment they decided to buy. "They just hired field reps", "they run 12 events a year", "the spreadsheet finally broke". Triggers are what makes an ICP actionable; firmographics only tell you who to call, triggers tell you when.

Step 3: State the profile

Three parts, all required:

1. Fit — the firmographics: industry, size, geography, structure
2. Trigger — the observable event that makes it urgent now
3. Anti-profile — who to disqualify on sight, with the evidence. This is worth more than the profile: it stops the reps burning weeks on the segment that loses 90% of the time

Include for each claim: the numbers behind it and how confident you are. An ICP line without a denominator is an opinion.

Step 4: Encode it as scoring rules

This is what separates this from a document.

• list-lead-scoring-rules — read what exists first. Rules the org already wrote encode beliefs; contradicting one is fine, doing it silently isn't
• create-lead-scoring-rule for each ICP dimension worth points, and update-lead-scoring-rule where an existing rule is now provably wrong
• delete-lead-scoring-rule only for rules the evidence actively refutes, and only with the user's explicit yes

Keep the rule set small — a dozen well-chosen rules beat forty that cancel each other out. Weight by demonstrated win-rate lift, not by how strongly anyone feels.

Show the proposed rules as a table and get approval before writing. This changes how every future lead is scored, which is exactly why it deserves a confirmation.

Step 5: Make it reachable

• add-lead-note on a handful of exemplar won leads: "Textbook ICP — {trigger}". Future reps learn the profile from real deals faster than from a document
• Propose a saved view or tag for "matches ICP" so the profile is a filter, not a memory
• If the org runs campaigns against segments, note which campaigns aim at the anti-profile — that's spend to reallocate

Output format

# Ideal Customer Profile — {Org}

Built from {n} won and {n} lost leads, {date range}.

## The profile
**Fit:** {industry} · {size band} · {geography} · {structure}
**Trigger:** {the observable event}
**Median won value {x} · median cycle {n} days**

## Evidence
| Axis | Won | Lost | Win rate | Read |
|------|-----|------|----------|------|
| … | 18 | 6 | 75% | 3× the book average |

## Anti-profile — disqualify on sight
| Signal | Win rate | Deals burned |

## Triggers, in their words
{Quotes from won-lead notes.}

## Scoring rules {proposed | written}
| Rule | Points | Why | Status |

## Written to Lynqu
- {n} scoring rules created, {n} updated
- {n} exemplar leads annotated

Rules and constraints

• Sample size gets stated, every time. Under ~10 wons, label it a hypothesis and recommend a re-run next quarter.
• Losses are half the analysis. An ICP built only on wins is survivorship bias with a template.
• Win rate, never volume. The biggest segment is usually just the biggest segment.
• Never write scoring rules without explicit approval. They grade everything arriving afterwards.
• Correlation gets labelled as correlation. "Wins skew to companies with 200+ employees" is a finding; "company size causes wins" is a story.
• The anti-profile is not optional. Ship it or the ICP won't change behaviour.

Error handling

• Fewer than 10 won leads → run it anyway, label every conclusion a hypothesis, and lean on the qualitative notes rather than the percentages.
• No lost leads recorded (everything sits open forever) → say so. That is a pipeline hygiene problem, and it's blocking the analysis. Route to lynqu-lead-management and come back with a real negative class.
• ADDON_REQUIRED on performance tools → the ICP does not need them. Use leads, companies and the dashboard summary; note what you couldn't check.
• Wildly inconsistent data (half the leads have no company, no source) → report the coverage gaps first. Fix the intake, then profile. Say which fields are missing and how often.
• Scoring rule write denied → manager+ territory. Deliver the rule table so a manager can apply it in one pass, and name who can.

Cross-skill integration

• ICP in hand → lynqu-lead-research to go find more of them
• Every subsequent lynqu-qualify run should score against these rules
• Anti-profile → tighten lynqu-lead-capture routing so the wrong-fit leads stop landing in the main pipeline
• Win/loss patterns by campaign → lynqu-pipeline-report and lynqu-event-blitz for where to spend next
• Re-run quarterly. An ICP built on last year's book quietly stops being true.

Example

"/lynqu icp — I think we're wasting time on enterprise."

The run: pulls 34 wons and 51 losts · finds mid-market (50–500) wins at 61% with a 24-day cycle while enterprise wins at 12% with a 96-day cycle and accounts for 40% of rep hours · finds the trigger in the won notes: "just hired field reps" or "exhibiting at 6+ events a year" · proposes six scoring rules (+15 events-heavy, +10 field team, −20 over 2000 employees), an anti-profile of single-location businesses with no field motion, and annotates four exemplar wins. Verdict: the instinct was right, and the numbers now say it out loud.