── PLAYBOOK · GTM STRATEGY

Nail your ICP, then act on it

Everyone has an ICP slide. Few can act on it. Turn a fuzzy definition into a scored, worked motion.

Install the whole playbook ↓

[ BUILT GTM ]

Nail your ICP, then act on it

Solvethe problem worth solving

Problem · 01GTM Strategy

Everyone has an ICP slide.

Few can act on it. Turn a fuzzy definition into a scored, worked motion.

Stackthe workflow, box by box

Claude01

Design a customer-interview study and turn transcripts into messaging.

Get the real buyer language and the trigger that creates urgency.

Connect to analyze

Salesforce02

Find what your wins, losses, and churn share.

Learn what your won deals actually share.

Powers up with

Claude03

Score whether an account is a real ICP fit.

Spot-check individual accounts and catch where your vendor's fit score is wrong, before you score the whole list.

Connect to analyze

Salesforce04

Turn a pile of accounts into a stack rank with a reason on every row.

Run the one authoritative fit score at bulk: stack-rank the whole market with a reason on every row.

Powers up with

Claude05

Find where the funnel leaks and what to fix.

Take icp-scoring's tiers and find the channel that converts each one best, no new score computed.

Connect to analyze

Splithow control passes: agents branch left, humans right

HUMAN + AI, IN THE LOOP

How control passes.

AgentsHuman

Agent01

Design a customer-interview study and turn transcripts into messaging.

Claude runs it.

Agent02

Find what your wins, losses, and churn share.

Salesforce runs it.

Agent03

Score whether an account is a real ICP fit.

Claude runs it.

Agent04

Turn a pile of accounts into a stack rank with a reason on every row.

Salesforce runs it.

Agent05

Find where the funnel leaks and what to fix.

Claude runs it.

The skills run the work. You stay in the loop on the calls that need judgment.

Outputwhat the workflow produces

OUTPUT · 01

The real ICP from won-customer patterns

OUTPUT · 02

The one fit score per account from icp-scoring

OUTPUT · 03

A stack rank

OUTPUT · 04

The channel to work each tier

Outcomethe result it drives

OUTCOME · 01

A person, a day, a problem

The ICP rebuilt from real won-customer patterns, not a headcount band.

OUTCOME · 02

One score, run at scale

The same icp-scoring rubric this playbook builds, run across the whole TAM.

OUTCOME · 03

The number-one predictor was not in the model

Backtested the score against real wins and losses.

Built GTM

── 01 / WHY RUN IT

RUN THIS AND YOU CAN

✓The real ICP from won-customer patterns

✓The one fit score per account from icp-scoring

✓A stack rank

✓The channel to work each tier

── 02 / THE RUN, STEP BY STEP

The workflow that solves it, one step at a time.

Each step is the plain question you are already asking. The skills answer them in order, one handing its receipt to the next.

1✓ TESTED

Design a customer-interview study and turn transcripts into messaging.

Who to talk to, a non-leading interview guide, and how to turn transcripts into positioning and messaging inputs.

YOU GETThe interview slate, the non-leading guide, and transcripts-to-positioning.

POWER IT UP

OPEN THE SKILL →

2✓ TESTED

Find what your wins, losses, and churn share.

Reads closed-won, closed-lost, and churned accounts, then hands back a ranked list of lookalike accounts to work.

YOU GETThe won / lost / churn pattern logic and the lookalike ranking.

POWER IT UP

OPEN THE SKILL →

3✓ TESTED

Score whether an account is a real ICP fit.

Rolls firmographics, product usage, hiring intent, and tech stack into one 0-100 verdict, and flags where a scoring tool is wrong.

YOU GETThe weighted composite, the per-source breakdown, and channel tagging.

POWER IT UP

OPEN THE SKILL →

4✓ TESTED

Turn a pile of accounts into a stack rank with a reason on every row.

Fair across channels, blank fields never scored as zero, with a why behind each account's score.

YOU GETThe three score modes, the gates and escalators, and per-account reasoning.

POWER IT UP

OPEN THE SKILL →

5✓ TESTED

Find where the funnel leaks and what to fix.

Finds the leak, the accounts to work next, and where the scoring model is quietly letting you down.

YOU GETThe funnel-leak diagnosis, the account ranking, and the scoring audit.

POWER IT UP

OPEN THE SKILL →

WHERE YOU LAND

The real ICP from won-customer patterns, the one fit score per account from icp-scoring, a stack rank, and the channel to work each tier.

EVERY STEP, TESTED AND PASSED

EVAL RESULTPASS4/4 dimensions

HOW WE EVAL →

✓Skills resolve

✓Structure + proof

✓Example chat

✓Quality judge

All 5 chained skills resolve to real, scored skills.
Has a problem, an outcome, 2+ steps, and proof from a real build.
Ships an example chat showing the play run in Claude.
Reads in plain operator voice, grounded in a real build.

Last eval run 2026-08-05

DEEP EVAL · PASS

THE STANDARD →

We did not just check the structure. A judge read this play against the full text of every skill it chains (composition), then ran it on a real scenario end to end (simulated). It passed both. Test scenario: Maya, VP of Sales, has a vague 'mid-market SaaS, 200-2000 employees' ICP slide and wants a real, scored, worked motion across 640 accounts.

── 03 / SEE IT RUN

Watch the skills answer in order.

Describe your situation in plain language. The right skill fires on its own and hands its receipt to the next.

EXAMPLE CHATMaya, VP of Sales, running the loop in one sitting

Our ICP slide says mid-market SaaS, 200 to 2000 employees. That is basically everyone. I want to know who actually buys and act on it.

Step 1· Customer Research

Let's start from real buyers, not the slide. I pulled your 12 most recent won accounts and set up interview questions to get the language they used and the trigger that made them buy now instead of later.

Receipt

Interview guide plus 12 target customers, and 3 recurring triggers already showing up in your notes.

Good. Now what do those won deals have in common that we keep missing?

Step 2· Pattern Analyst

I read your closed-won, closed-lost, and churned accounts together and looked for the shared signals. The wins cluster tighter than your slide does, and two loud traits are ones you are not filtering on today.

Receipt

Wins share 4 traits; the strongest is a recent RevOps hire, present in 9 of 12 wins.

So take a specific account. Are they actually a fit or do they just look like one?

Step 3· Icp Analyst

I scored that account on the traits that predict wins, not headcount band. Firmographics, hiring intent, and stack all rolled into one number with the reasoning shown, so you can see why it landed where it did.

Receipt

Account scored 78 of 100, dragged down by no RevOps hire and a competitor already in the stack.

Do that for the whole list so I know where to point the team.

Step 4· Icp Scoring

I stack-ranked all 640 accounts with a reason on every row and kept it fair across channels, so a blank field never scores as a zero. You get tiers, not a wall of numbers.

Receipt

640 accounts ranked; 84 land in Tier 1, and each row carries a one-line why.

Last thing. Where do these actually convert best so we do not waste the good accounts on the wrong channel?

Step 5· Strike Zone Analyst

I checked how your Tier 1 accounts convert by channel and found where the funnel leaks. Outbound is burning your best accounts at the demo stage, while warm intro closes them at more than double the rate.

Receipt

Tier 1 via warm intro closes at 31% vs 12% outbound, so route the top 84 to intro-first.

── 04 / SEE IT IN PRODUCTION

Before you run it, I ran it for real.

This workflow is genericized from a real build. Here is the number it moved, and what I actually did.

THE BUILD · ICP + PERSONAS

The ICP Playbook

~3x

paid conversion when the signup fits the ICP, 1 in 31 vs 1 in 95

2.6x

off-ICP accounts churn more, 47.8% vs 18.5%

6.9 days

median time to buy, no IT and no implementation in the loop

One job, many titles

the ICP is a job, not a title band, 248 titles proved it

WHAT I DID ›

Interview the people who bought. Talk to real customers, not a panel. Why they came, why they bought, why they stayed, and the exact words they use for the problem. That language is the raw material for everything downstream.

Mine G2 and the calls for their words. Pull the voice of the customer from G2 reviews and call transcripts: what they praise, what they complain about, the job they hired you for, in their language, not your marketing.

Backtest closed-won for the pattern that repeats. Read twelve months of won deals for the shape that actually recurs, not the logos you wish you had. Who looks like a win across firmographic, technographic, and how they use the product.

A firmographic filter is not an ICP; it is a list. Derive the real one from evidence: interview buyers, mine G2 and calls for their words, backtest closed-won for the pattern that repeats, reconcile it against the data, and write personas about the person and their problem, not the headcount band. Then your ICP survives contact with a real pipeline.

Dig into the full build →

THE BUILD · TAM + PRIORITIZATION

Scoring the TAM

~4x

more revenue-bearing opps than the prior run rate

~4.5x

more new pipeline created than the prior run rate

~90%

of that pipeline came from strong-fit accounts

100%

of outbound landed on-strike-zone

WHAT I DID ›

One rubric for the whole market. ICP fit earns the base, 40 to 70. Product engagement adds up to 30, the loudest and most honest signal. Buying signals add up to 20, bounded so they can only ever help, never carry a bad-fit account.

Three stages, one engine. Fit, product, and intent are scored separately and stacked, so the number always shows its work and no single signal can hide behind another.

Every account gets a tier and a move. The output is not a score in a vacuum. It is a tier, Strike or Hot and down, and a specific first action per account, by role, so a rep never has to ask what to do next.

You cannot prioritize a market by scoring one deal at a time. Put the entire TAM through one rubric, fit for the base, product for the lift, intent capped so it can only help, and make reps work the ranked list top-down. The teams that do book more of the right pipeline and stop burning a quarter of their capacity on accounts that were never going to buy.

Dig into the full build →

THE BUILD · SCORING EVIDENCE

The Win/Loss Backtest

2.67x

win-lift from the strongest behavioral signal

3.56x

that signal stacked with an ICP threshold

16 to 0

won-lost record of every account that reached Hot or Strike

1.13x

the fit score everyone trusted, barely discriminating

WHAT I DID ›

Measure lift, not opinion. For every signal, I compared how often it showed up in won deals versus lost ones. Lift, not vibes. A signal that appears equally in wins and losses is noise, no matter how good it feels.

Find the predictors that were not in the model. The strongest discriminators were behavioral, how the buyer actually worked, not the firmographics we had been scoring on. The single best one carried a 2.67x lift and was not weighted at all.

Stack the signals that compound. One behavioral signal plus an ICP threshold stacked to a 3.56x lift. The combination beat either alone, which is the whole argument for keeping signals separate so they can compound.

Before you trust a scoring model, backtest it against the deals you actually won and lost. Measure lift per signal. The ones that feel important and the ones that are important are rarely the same list.

Dig into the full build →

05 / INSTALL THE WHOLE PLAYBOOK

One plugin. 5 skills, bundled.

The whole playbook installs as a single plugin in Claude Code, no copy-pasting 5 times. Or grab any single step above. It runs on what you paste; connect your stack to go live.

OR KICK IT OFF IN CHAT

“Here is my situation: [describe your workflow or account in a sentence or two]. Walk me through Nail your ICP, then act on it step by step, and run each skill as we go.”

── GET THE NEXT PLAYBOOK

Nail your ICP, then act on it

Nail your ICP, then act on it

Everyone has an ICP slide.

The real ICP from won-customer patterns

The one fit score per account from icp-scoring

A stack rank

The channel to work each tier

A person, a day, a problem

One score, run at scale

The number-one predictor was not in the model

The workflow that solves it, one step at a time.

Watch the skills answer in order.

Before you run it, I ran it for real.

One plugin. 5 skills, bundled.

One runnable workflow a week. Free.