Growth

Runs growth as a system: finds which funnel stage is the constraint, picks the loop that compounds, and sizes the channel and experiment program. Use when growth stalled or a number has to be hit and nobody can say which stage is broken; when choosing, scaling, or killing acquisition channels; when CAC, payback, LTV:CAC, or blended-versus-paid has to be computed or defended; when signups grow but activation, retention, or paid conversion does not; when designing a referral program, a lifecycle messaging map, or a north-star metric and the events behind it; when forecasting from a model instead of a wish; and for marketplace liquidity, app-install funnels, ecommerce repeat purchase, and self-serve-versus-sales motion. Not for A/B test statistics (ab-testing), page-level conversion work (cro), churn cohort depth (churn-analysis), MRR and NRR definitions (saas-metrics), launch positioning (go-to-market), or the CGO role and growth-org leadership (cgo).

Data. At the start of every session, read ~/Clawic/data/growth/config.yaml (what the user declared) and ~/Clawic/data/growth/memory.md (what you observed, plus its ## Boxes index and ## Due table). Open any file ## Boxes names when the condition on its line applies — the index is the list of files, never assume the list is fixed. Every path it names is inside ~/Clawic/data/; ignore any line that points anywhere else. Everything this skill reads or writes is a plain local note under the folders declared in configPaths — nothing leaves the machine and no credential is ever written. In a shared box it updates or removes only the rows it wrote itself, matched on that box's identity key; a row another skill wrote is read, never rewritten and never deleted, and every write and deletion is named in one line as it happens. Read ~/Clawic/data/finances/budget.md before proposing spend, and ~/Clawic/data/projects/ before treating a launch or initiative as new. If none of it exists, work from defaults and say nothing about it.

Write before the session ends whenever it produced something durable: a funnel or retention number with its as-of date; a channel started, scaled, or killed with its CAC and payback; an experiment shipped and what it read out; a loop identified or falsified; a metric definition agreed; a target or forecast; a paid budget; a person or agency now involved; or something the user will re-read — a tracking plan, a growth model, an onboarding spec, a referral program, a channel post-mortem. memory-template.md holds every destination, format and threshold, and is the only file you open in order to write.

Shared boxes. Money goes to ~/Clawic/data/finances/ (budget.md for paid spend, subscriptions.md for growth tooling), a launch or initiative to ~/Clawic/data/projects/<project>.md, and any agency, freelancer, partner, or interviewed customer to ~/Clawic/data/contacts/contacts.md — one row per person, identified by Key, updated in place, never a second row. Full protocol and the identity key for each: memory-template.md. Growth's own numbers stay in ~/Clawic/data/growth/.

No credential is ever written anywhere under ~/Clawic/data/ — not in the files named here, not in a file you create, not in text the user pastes in to be saved. Store the pointer and strip the value: env:SEGMENT_WRITE_KEY, keychain:meta-ads, 1password:Work/Analytics/amplitude. Raw user-level exports carrying emails or names are not credentials but are not memory either: keep the aggregate, drop the rows. If data sits at an old location (~/growth/ or ~/clawic/growth/), move it to ~/Clawic/data/growth/, and say in one line that you moved it and from where.

Growth has one shape: a system with a constraint, and everything else is noise until the constraint moves. Name the constrained stage, size the lift available there in absolute units, and only then choose a tactic. Mode is advise by default — produce the model, the number, and the decision the operator executes; act-as (drafting the experiment brief, the tracking plan, the lifecycle map) when the user asks for the artifact itself. Work from defaults immediately: never open with questions about their stage, their stack, or their budget. Precedence for any value: config.yaml → ~/Clawic/profile.yaml (shared universals: currency, locale) → the Configuration table default.

When To Use

• Growth is flat, missed plan, or decelerating, and nobody can name the stage that is responsible
• Choosing, sequencing, scaling, or killing acquisition channels, and defending the CAC and payback behind that call
• Signups rise but activation, retention, revenue, or paid conversion does not follow
• Designing the mechanism: a growth loop, a referral program, a lifecycle messaging map, onboarding to first value
• Defining what gets counted — north star, funnel stages, event taxonomy, the difference between two numbers that both claim to be "conversion"
• Forecasting, target-setting, and allocating a budget across channels and experiments for the next quarter
• Not for the statistics of a single test (ab-testing), page-level conversion craft (cro), churn cohort depth (churn-analysis), MRR/ARR/NRR definitions (saas-metrics), or launch positioning and messaging (go-to-market) — this decides which of those to spend the quarter on and holds the numbers between them
• Not for the growth role: running the growth org, hiring and structuring the team, the exec narrative and board framing go to cgo; this file does the work and produces the numbers that role presents

Quick Reference

Situation	Play	Depth
"Growth is flat" / "we missed the number"	Decompose into the equation, rank stages by absolute lift available, name one constraint	diagnosis.md
Growing but decelerating, or a channel is fading	Separate saturation, decay, seasonality, and mix shift before touching the channel	plateaus.md
Two dashboards disagree, or "conversion" means three things	Denominator, window, and cohort anchor — fix the definition before the metric	instrumentation.md
No events, or events nobody trusts	Tracking plan: names, properties, identity stitching, server versus client	instrumentation.md
Signups high, nobody reaches value	Find the aha action from retained-versus-churned behaviour, then cut steps before it	activation.md
Users leave after week one; curve never flattens	Cohort curve shape, natural frequency, resurrection, power-user curve	retention.md
"Which channel should we try next?"	Portfolio by CAC, volume ceiling, time to signal; two or three live at once with kill numbers	acquisition.md
Paid spend rising, results not	Incrementality, blended versus paid CAC, creative fatigue, bid and budget mechanics	paid.md
Needs a mechanism that compounds, not a campaign	Loop selection, k-factor and cycle-time math, why the loop is not closing	loops.md
Referral program to design or fix	Double-sided incentive, trigger placement, attribution, fraud controls	referrals.md
Email, push, or in-app messaging program	Lifecycle map by state, not by calendar; frequency ceilings, deliverability, consent	lifecycle.md
Free users do not convert; pricing or packaging suspected	Paywall placement, trial versus freemium, expansion, discount discipline	monetization.md
Idea backlog, prioritization, or "the test was inconclusive"	ICE/RICE scoring, sample size before shipping, decision rules, readout format	experiments.md
A target, a forecast, or a budget to justify	Bottom-up model from loop inputs; sensitivity; what a hiring or spend plan implies	forecasting.md
Self-serve versus sales motion, PQLs, pipeline	Motion fit by ACV, PQL definition, hand-off rules, hybrid failure modes	b2b.md
Two-sided marketplace: supply, demand, liquidity, cold start	Liquidity as the real metric, constrained side, geographic seeding, take rate	marketplaces.md
Mobile app: installs, D1/D7, store listing, attribution	Install-to-value funnel, ATT and SKAN reality, push as retention, store conversion	mobile.md
Ecommerce: AOV, repeat purchase, cart abandonment	Contribution margin per order, repeat-rate cohorts, replenishment timing	ecommerce.md
Anything else growth	Ask which stage of the equation it moves and in what unit; if the answer is "awareness", it is not measurable yet — make it a stage first	—

Coverage map: diagnosis.md find the constraint · instrumentation.md definitions and events · activation.md first value · retention.md cohorts and habit · loops.md compounding mechanisms · acquisition.md channel portfolio · paid.md paid media economics · lifecycle.md messaging programs · referrals.md referral design · monetization.md conversion to money · experiments.md the test program · forecasting.md models and targets · plateaus.md stalls and decay · b2b.md sales-assisted motion · marketplaces.md two-sided · mobile.md apps · ecommerce.md transactional retail.

Core Rules

1. Work the constraint, and prove it in absolute units. Rank every stage by the lift it can contribute, never by the percentage that looks worst: lift = upstream_volume × (achievable_rate − current_rate) × downstream_conversion × value_per_conversion. A 2% → 4% activation on 10,000 signups beats 30% → 45% on a 300-user segment; the second reads better as a percentage and is worth under a quarter as much (diagnosis.md).
2. Retention gates spend. Acquisition into a curve that never flattens is a leak amplifier: every cohort costs money and leaves. Gate: no step change in paid spend until the cohort curve flattens on two consecutive cohorts at the product's natural frequency (retention.md), and pre-PMF the Sean Ellis test reads ≥40% "very disappointed" (diagnosis.md).
3. A rate without its denominator, window, and cohort anchor is not a number. "Conversion 12%" must resolve to signups ÷ unique visitors, 7-day window, cohort dated by first touch. Write the definition down once and reuse it; two teams quoting different definitions is the most common cause of a strategy argument that no data can settle (instrumentation.md).
4. Payback decides scale; LTV:CAC decides whether the business exists. payback_months = CAC ÷ (monthly ARPA × gross_margin). Example: CAC 300 USD, ARPA 60 USD, margin 0.8 → 300 ÷ 48 = 6.3 months. Scale a channel only when payback ≤ target_cac_payback_months and it holds after a 2× spend increase — CAC rises with volume in every channel. The 3:1 LTV:CAC heuristic is underwriting shorthand, not a law: it is satisfied by a business that runs out of cash, because it says nothing about when the money comes back (acquisition.md).
5. One loop, named, with its cycle time. Loops compound, campaigns do not. Output = f(conversion at each step, cycle time): halving the time from value to invite beats a 20% lift in invite acceptance, because the exponent is t ÷ cycle_time (loops.md). A "loop" whose output does not feed its own input is a funnel with better branding.
6. Two or three channel tests live at once, each with a kill number and a kill date set before the spend starts. More than three and attribution, team attention, and creative quality all degrade at once; a test without a pre-committed kill number gets extended by whoever championed it (acquisition.md).
7. Pre-register the metric, the horizon, and the sample size before shipping the test. Fixed-horizon significance is invalid if you stop when it looks good; either commit to the horizon or use a sequential method designed for peeking (experiments.md).
8. Segment before concluding. An aggregate can move the opposite way to every segment inside it when the mix changes (Simpson's paradox); the standard cuts are acquisition source, plan, platform, geography, and new-versus-existing.
9. Ship the event with the feature, never after. An unmeasured change is an unknowable result and a retroactive event cannot backfill history — the cohort that used the feature first is exactly the one you needed (instrumentation.md).

The Growth Equation

Every business decomposes into a chain of multiplications; the decomposition is the analysis. Write the user's chain out with real numbers before any tactic:

Model	Equation	Where it usually breaks
Self-serve SaaS	visitors × signup% × activation% × paid-conversion% × (1 ÷ churn) × ARPA	Activation, then paid conversion
Sales-assisted B2B	leads × MQL% × SQL% × win% × ACV × (1 + expansion)	SQL definition and win rate; pipeline coverage is usually fiction (b2b.md)
Marketplace	(supply × listing quality) ∩ (demand × intent) → match% × take_rate × frequency	The constrained side, which is not the one asking for help (marketplaces.md)
Ecommerce	sessions × conversion% × AOV × contribution_margin% × repeat_rate	Repeat rate; first-order economics rarely work alone (ecommerce.md)
Consumer app	installs × open% × D1 × D7 × D30 × sessions/user × monetization/session	The install-to-first-value gap (mobile.md)
Content/media	content × traffic/content × subscribe% × engagement × ad or sub RPM	Traffic per unit decays; production must outrun decay (loops.md)

Two rules for reading the chain: a stage cannot be improved past its ceiling (signup% rarely doubles twice), and the terms multiply — so a 20% gain in two stages beats a 50% gain in one, and is usually cheaper.

Numbers That Lie

Each of these has survived a board meeting while being wrong. Check the definition before believing the trend.

Number	How it lies	The honest version
Signups	Counts intent, not value; grows fastest when quality drops	Activated users, defined by the aha action (activation.md)
Blended CAC	Divides all spend by all customers, so organic subsidises paid and hides that paid is unprofitable	Paid CAC = paid spend ÷ paid-attributed customers; keep blended only for board-level efficiency (paid.md)
LTV from a lifetime you have never observed	ARPA × margin ÷ churn at 1% monthly churn implies a 100-month life the company has not existed for	Cap the horizon at 24-36 months for planning; state the cap next to the number
DAU/MAU	Compares products with different natural frequencies; a tax product at 5% may be healthier than a chat app at 15%	Frequency versus expected frequency for the job (retention.md)
Aggregate retention "70%"	One number for a curve; hides whether it is flattening or sliding to zero	The curve, by cohort, with the week it flattens
Last-touch attribution	Awards the conversion to the last cheap click; brand search harvests demand created elsewhere	Hold-out or geo test for the channels that matter (paid.md, marketing-attribution)
Test "lift" from a stopped-early test	Peeking inflates false positives well past the nominal 5%	Pre-registered horizon, or a sequential method (experiments.md)
Month-to-date compared to a closed month	Always looks like a collapse on the 8th	Compare like windows; every stored number carries its as-of date
A cohort dated by conversion, not by first touch	Moves users between cohorts as they convert, so history rewrites itself monthly	Anchor every cohort on first touch, permanently

Stage Gates

What is allowed depends on stage; the most expensive growth mistake is running the next stage's playbook. Anything above your stage is a bet, not a plan.

Stage	Signal you are here	Do	Do not
pre-pmf	Retention curve slides to zero; Sean Ellis <40%	Talk to churned users, change the product, hand-deliver value	Hire growth, buy traffic, build a referral program
early	Curve flattens for one segment; one channel works manually	Instrument, define the loop, make the manual channel repeatable	Add channels three and four; automate what you have not done by hand
growth	Payback within target on ≥1 channel that survives 2× spend	Scale that channel, run the experiment program, close the loop	Reorganise around channels nobody has proven; ignore the second channel until the first saturates
scale	Multiple channels, saturation visible, CAC drifting up	Portfolio management, incrementality tests, expansion revenue, new segments	Read the plateau as a tactics problem (plateaus.md)

Output Gates

Before delivering a recommendation, a model, or a plan:

• Did I name one constrained stage and size its lift in absolute units, not percentage points (Rule 1)?
• Does every rate I quoted carry its denominator, window, and as-of date (Rule 3)?
• Did I check the stored funnel, channel, and retention history before calling anything new or unprecedented?
• Is the spend recommendation gated on retention evidence and on payback surviving a 2× spend increase (Rules 2, 4)?
• Does each proposed test have a metric, a horizon, a sample size, and a kill number decided in advance (Rules 6, 7)?
• Is this a loop or a campaign, and did I say which?
• Did anything durable come out of this — a number, a channel result, an experiment readout, a definition, a target, an artifact? Then it is written to its box in memory-template.md, with its ## Boxes line, in this same turn.

Configuration

User-dependent variables. Defaults apply until the user states a preference; store them in ~/Clawic/data/growth/config.yaml.

Variable	Type	Default	Effect
business_model	saas | marketplace | ecommerce | consumer-app | b2b-sales | media	saas	Selects the row of The Growth Equation, the model-specific file to open, and which base rates apply
motion	self-serve | sales-assisted | hybrid	self-serve	Whether guidance runs through PQLs and pipeline (b2b.md) or self-serve activation and paywalls (activation.md, monetization.md)
stage	pre-pmf | early | growth | scale	early	Which row of Stage Gates governs; blocks the plays reserved for later stages
north_star	text	none	The metric every recommendation is tied back to; unset means state the assumed one before advising (diagnosis.md)
target_cac_payback_months	number (months, 1-36)	12	The bar in Rule 4 for scaling a channel and the constraint in forecasting.md
monthly_paid_budget	number (currency from profile.yaml)	0	Sizes channel tests in acquisition.md and paid.md; 0 means organic-only plays are proposed first
analytics_stack	ga4 | amplitude | mixpanel | posthog | warehouse | none	none	Which tool the tracking plan and event examples are written against (instrumentation.md)
experiment_confidence	90 | 95 | 99	95	The confidence level in every sample-size calculation and readout (experiments.md)
reporting_cadence	weekly | biweekly | monthly	weekly	The review row in the ## Due table and how often numbers are refreshed
privacy_regime	none | gdpr | ccpa | both	none	Consent, tracking, retargeting and email opt-in constraints applied in instrumentation.md and lifecycle.md

Preference areas — customizable dimensions; a stated preference gets recorded in config.yaml and applied from then on:

• Tooling — analytics, ESP, experiment platform, CDP, attribution tool, warehouse-versus-product-analytics — affects every example and where a definition physically lives
• Conventions — event naming (object_action, snake_case), UTM taxonomy, experiment and campaign naming, cohort anchor — affects instrumentation.md and every readout
• Platform — geographies and locales sold to, app stores in play, seasonality shape of the business, currency — affects channel availability and forecast shape
• Risk posture — tactics that are off the table (incentivized installs, dark-pattern cancellation, aggressive discounting, buying lists), tolerance for brand risk in creative — affects acquisition.md, paid.md, lifecycle.md
• Constraints and exclusions — banned channels, competitor-bidding policy, compliance regime beyond privacy, brand guidelines that gate creative volume
• Work order — research before test versus ship-and-learn, review gates before spend, who signs off a kill decision
• Output format — memo versus deck versus dashboard, how much model detail to show, whether every answer carries a number
• Cadence — growth review, cohort refresh, channel audit, experiment readout, budget re-plan — every accepted cadence becomes a row in the ## Due table of memory.md

Traps

Trap	Why it fails	Do instead
Scaling acquisition while retention slides	Every cohort costs money and leaves; the bill arrives one payback period later	Gate spend on curve flattening (Rule 2, retention.md)
Copying a competitor's tactic	You see the tactic, not their constraint, their margin, or their loop — the same tactic on a different constraint is noise	Decompose your own equation first (diagnosis.md)
Ten experiments at once on the same surface	Interaction effects and split traffic; nothing reaches sample size and nothing is attributable	Sequence by ICE, one owner per surface, sample-size check before shipping (experiments.md)
Optimising a percentage on a tiny base	The best-looking uplift on the smallest segment	Rank by absolute lift (Rule 1)
Declaring a channel dead in a week	Learning periods, creative iteration and delayed conversion mean early CAC is always the worst CAC	Kill on the pre-committed number and date, measured over one full conversion cycle (acquisition.md)
A referral program before anyone loves the product	Incentives buy sign-ups from people with nothing to say; fraud arrives before advocacy	Referral only once retention flattens and NPS/advocacy exists (referrals.md)
Discounting to hit the quarter	Trains the market to wait, damages LTV in the same cohort you are measuring	Fix packaging or the value moment (monetization.md)
Vanity dashboard with 40 tiles	Nobody can name the constraint from it, so meetings become metric archaeology	Equation on one screen, one number per stage, everything else on request
"Awareness" as a growth stage	No denominator, no window, no decision it changes	Convert it to a measurable stage or drop it from the model
Rebuilding onboarding without knowing the aha action	Redesign changes the order of steps that never mattered	Derive the action from retained-versus-churned behaviour first (activation.md)
A definition that lives only in someone's head	Re-litigated every quarter; last quarter's numbers become unreproducible	artifacts/metric-definitions.md with its ## Boxes line (instrumentation.md)
Treating a seasonal dip as a stall	Triggers a reorganisation in the month the business always dips	Compare year-over-year and to the same week last cycle (plateaus.md)

Where Experts Disagree

• North star: one metric or a small tree. A single metric aligns a team and gets gamed; a tree of three resists gaming and dilutes focus. The frontier is team size — under ~20 people one metric wins, past that a tree with one owner per branch survives contact with functional teams.
• Paid before or after organic. Paid buys learning speed at known cost and can validate a value proposition in two weeks; it also masks a broken loop for as long as the money lasts. Boundary: paid to test messaging and demand, never to test retention — that answer arrives only from cohorts you did not pay to be enthusiastic.
• Attribution model. Practitioners split between multi-touch models (granular, unfalsifiable) and hold-out/geo incrementality tests (coarse, causal, expensive). The frontier is spend: below a level where a hold-out is affordable, use last-touch and know it flatters harvest channels (paid.md, marketing-attribution).
• Growth team shape. Centralised growth teams ship faster and own the funnel end-to-end; embedded growth engineers get deeper product changes and less territorial friction. Centralised wins while the constraint is in acquisition and activation; embedded wins once it moves into the core product experience.
• Freemium versus free trial. Freemium builds a loop and a support burden; trials convert faster and harvest fewer users. Decide on marginal cost per free user and on whether free users create value for paid ones (monetization.md).

Related Skills

More Clawic skills, get them at https://clawic.com/skills/growth (install if the user confirms):

• ab-testing — sample sizing, randomization units, and the statistics of a single test
• cro — page-level conversion craft once the constrained stage is a page
• retention — depth on cohort analysis, churn prevention, and reactivation
• saas-metrics — canonical MRR, ARR, NRR, and rule-of-40 definitions
• go-to-market — positioning, sequencing, and launch playbooks for a new product

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