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Turn product usage into booked pipeline

Your best-fit buyers are already using the product for free, and reps keep treating them like cold leads. Work the product channel like the warm one it is.

[ BUILT GTM ]

Turn product usage into booked pipeline

Solvethe problem worth solving
Problem · 01Sales

Your best-fit buyers are already using the product for free, and reps keep treating them like cold leads.

Work the product channel like the warm one it is.

Stackthe workflow, box by box
Find the ready ones
Amplitude01
Read how an account actually uses you.
Read who is ready to buy by how they use you.
Powers up with
AI
Amplitude02
Turn a buying signal into a timely touch.
Catch the usage signal and confirm it is inside the freshness window; hand the angle and timing to cold-email-writer instead of drafting the touch here.
Powers up with
AI
Work the lead
Outreach03
Turn one signal into a cold email that earns a reply.
Write the first-touch email itself, opening with what they are already doing in the product, using the angle and timing trigger-outreach just flagged.
Powers up with
AI
Outreach04
Design an outbound cadence where every touch has a point.
Sequence the touches with a point, tuned to a warm product lead.
Powers up with
AI
Splithow control passes: agents branch left, humans right
HUMAN + AI, IN THE LOOP
How control passes.
AgentsHuman
Agent01
Read how an account actually uses you.
Amplitude runs it.
Agent02
Turn a buying signal into a timely touch.
Amplitude runs it.
Agent03
Turn one signal into a cold email that earns a reply.
Outreach runs it.
Agent04
Design an outbound cadence where every touch has a point.
Outreach runs it.
The skills run the work. You stay in the loop on the calls that need judgment.
Outputwhat the workflow produces
OUTPUT · 01

A read on who is ready by how they use you

OUTPUT · 02

A timely reason to reach out

OUTPUT · 03

A first touch that opens with their usage instead of a template

Outcomethe result it drives
OUTCOME · 01

0 to our top channel

The product channel that was hiding in plain sight, scored and worked like the warm lead it was.

OUTCOME · 02

Score the whole market

One rubric across every account, so a rep works a ranked list instead of guessing.

OUTCOME · 03

The behavior that predicts a buyer

Backtested against real wins and losses: usage beat firmographics.

Built GTM
── 01 / WHY RUN IT
RUN THIS AND YOU CAN
A read on who is ready by how they use you
A timely reason to reach out
A first touch that opens with their usage instead of a template
── 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.

1Find the ready ones✓ TESTED
Read how an account actually uses you.

Turns raw usage into per-capability adoption tiers, a 12-week trend on each, power-user IDs, and a ghost-active flag.

YOU GETThe adoption-tier rubric, the trend classifier, and the ghost-active check.
OPEN THE SKILL →
2Find the ready ones✓ TESTED
Turn a buying signal into a timely touch.

Read the trigger (funding, hire, launch, job posting, tech change), pick the one angle it earns, and write the touch while it is still fresh.

YOU GETThe trigger read, the freshness gate, and a one-angle draft.
OPEN THE SKILL →
3Work the lead✓ TESTED
Turn one signal into a cold email that earns a reply.

From a prospect and a specific angle, writes a short email or 3-touch sequence built on one idea, no filler.

YOU GETThe one-idea rule, an earned opener, a soft CTA, and a 3-touch sequence.
OPEN THE SKILL →
4Work the lead✓ TESTED
Design an outbound cadence where every touch has a point.

The steps, timing, channel, and intent of each touch, tuned to the segment's value.

YOU GETThe touch plan, the timing, the channel mix, and a clean break-up.
OPEN THE SKILL →
WHERE YOU LAND

A read on who is ready by how they use you, a timely reason to reach out, and a first touch that opens with their usage instead of a template.

EVERY STEP, TESTED AND PASSED
EVAL RESULTPASS4/4 dimensions
HOW WE EVAL →
Skills resolve
Structure + proof
Example chat
Quality judge
  • All 4 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: An AE on a product-led motion has thousands of free users and no way to tell which ones are worth a call; templated emails to the whole list are getting ignored.

── 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 CHATDana, AE on a product-led motion, running the loop in one sitting
D
We have thousands of free users and I have no idea which ones to call. Sales treats them like cold leads and the emails flop. Where do I even start?
R
Read· Product Usage Analyst
Let the product tell you who is ready before you spend an hour. I pulled usage across your free base and ranked accounts by the behaviors that actually precede a buy: repeat use, multiple seats on one domain, and hitting the wall on a paid feature.
Receipt
Ranked your free base. 42 accounts show buy-ready behavior: 3+ active seats on one domain, weekly use, and a paid-feature limit hit in the last 14 days.
D
Okay, that list is way more useful. When do I reach out?
T
Time it· Trigger Outreach
On the signal, not the calendar. The strongest reason to reach out is a fresh spike, so I flagged the accounts that jumped this week. You talk to them while the usage is hot, not next quarter.
Receipt
8 of the 42 spiked this week: a new seat added or a paid-feature limit hit in the last 3 days. Those are today's calls.
D
Good. What do I actually say? The templated stuff gets ignored.
S
Say it· Cold Email Writer
Open with what they are already doing in the product, not a pitch. A product lead can tell in one line whether you looked. Here is the first touch for the top account, built on their real usage.
Receipt
Drafted the first touch: it opens with the seat they just added and the feature they hit the limit on, one idea, one ask. No template smell.
D
Love it. Can you make it a real sequence so I am not doing this one at a time?
S
Sequence· Cadence Builder
Tuned to a warm product lead, not the cold playbook. Every touch has a point and there is a clean break-up. You stay in control of who gets it; the motion runs the timing.
Receipt · the play, running
A 4-touch sequence per account, each touch anchored to their usage, over 10 days with a clean exit. 8 hot accounts loaded, ready to send on your yes.
── 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 · PRODUCT-LED MOTION
The Product Channel
~47%
qualified win rate, the highest of any channel
$1M+
in sales-qualified pipeline, built from zero
Best GRR + NRR
of any channel, it retained and expanded best too
414% of plan
the pipeline the channel actually delivered
WHAT I DID
01
Map the events first. I mapped what users actually did in the product to what it meant for a sale. Not every event, the ones that predicted buying.
02
Centralize the signal. I centralized our Snowflake product data with enrichment and signal data by implementing Common Room and Deepline, so a usage spike at an account arrived already enriched, already scored, already routed.
03
Build the motion on top. The signals worth acting on, the play that worked them, and the routing that put a real user in front of a real rep with a real reason to talk.

If you sell a product people can use before they buy, you already have a product-led channel. The question is never whether it exists. It is whether your usage data has been turned into context a rep can act on. Map the events first. The motion comes after.

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
01
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.
02
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.
03
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
01
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.
02
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.
03
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. 4 skills, bundled.

The whole playbook installs as a single plugin in Claude Code, no copy-pasting 4 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 Turn product usage into booked pipeline step by step, and run each skill as we go.
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