Growth OS — The Compounding Product Operating System for B2B SaaS

WHAT THIS TYPICALLY REVEALS

Your growth ceiling has a specific cause. This finds it and fixes it.

Your biggest growth leak is hiding in a step nobody flagged.

The step with the lowest completion rate typically is not the one teams debate in standups. The data tends to point somewhere upstream — a step that looked fine in aggregate but bleeds revenue quietly.

Effort resets every sprint because there is no shared decision framework.

Each sprint starts from scratch. Learnings live in Confluence. Nobody reads Confluence. Without a decision log that retains insights and feeds them into the next cycle, velocity never accelerates.

Teams running 2 experiments per quarter lose 18 decisions per year to opinion.

Experiment velocity is not a nice-to-have at this stage. A team running 20 tests per year versus 2 is making 18 more decisions from evidence instead of debate.

Churn shows up as a cancellation email. It should show up 60 days earlier.

Most SaaS teams find out about churn after the cancellation. A prediction model scores risk weekly and gives your CS team 30–60 days of lead time to intervene.

WHY THIS IS DIFFERENT

Analytics agencies don't run experiments. Consultants don't build churn models. Fractional hires leave when hours run out. This is the full function — and your team keeps it.

Analytics agencies do not run experiments. Growth consultants do not build churn models. Fractional leaders do not monitor 15 competitors. None of them hand you dashboards, a churn model, a query library, and an experiment log your team can operate independently.

Growth OS is the complete function. Four capabilities running simultaneously — analytics, experiments, churn prediction, competitive intelligence — each one feeding the others. Month 2 is sharper than month 1 because the decision log retains everything it learns. At month 3, your team owns the infrastructure, the frameworks, and the decision log. It does not depend on Jake being there.

You are not buying consulting hours. You are buying a growth function that starts in weeks, gets sharper every month, and stays with you permanently.

TIMELINE

Each month builds on the last. By month 4, the dashboards, models, and experiments run faster than you thought possible.

MONTH 1

Instrument

Analytics audit against your business model. Dead tracking removed. Instrumentation gaps mapped. Tracking plan written. Competitive intelligence live in Slack. Your data starts telling the truth.

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MONTH 2

Interpret

Data read in context. Activation drop-offs, retention signals, and expansion patterns identified. Hypotheses formed from evidence. First experiments designed and ready to run.

→

MONTH 3

Decide

3–6 experiments running in parallel. Churn model deployed. Results are definitive — no more “we think it worked.” Roadmap decisions backed by tested frameworks.

→

MONTH 4+

Compound

Each month faster than the last. Every result feeds the next experiment. Decision frameworks documented. New hires onboard into documented frameworks. The OS runs independently.

Month 4 experiments find revenue faster because every previous result sharpens the next hypothesis

WHAT YOU GET

Month 2 churn data designs month 3 experiments. Month 3 results shape month 4's roadmap. That's compounding.

Month 1 · Foundation

Analytics Infrastructure

Every event audited against your business model. Dead tracking removed. Instrumentation gaps mapped. Tracking plan written so the team can trust what they are looking at.

Event-by-event audit against revenue logic, not just counts
Only the events that predict revenue, retention, or expansion — noise eliminated
Gaps mapped with priority and implementation guidance
Written tracking plan your engineers can maintain

Month 2 · Intelligence

Behavioural Patterns

Data read in context of how your business model works. Activation drop-offs, retention signals, and expansion patterns identified before they become quarterly planning arguments.

Activation funnel visible end-to-end with specific drop-off points
Retention patterns identified by segment and cohort
Expansion signals mapped to product behaviour
Hypotheses ranked by confidence and potential impact

Month 3 · Experiments

Experiment Engine

Experiments designed from the intelligence layer. Sample sizes calculated before launch. Results are definitive — no more “we think it worked.”

3–6 experiments running in parallel each month
Pre-registered hypotheses grounded in your data
No more inconclusive experiments — every test is sized to produce a decision
Results that feed directly into the next roadmap decision

Month 4+ · Compounding

Decision Frameworks

Every experiment result builds a library of tested frameworks. New hires onboard into documented frameworks. Roadmap planning moves from debate to decision in an afternoon.

Experiment library with documented results and learning
Your team makes the same quality decisions without external help
Onboarding materials so the frameworks run without Jake
Monthly growth review: results, what they mean, what runs next

Here is what this looks like in practice: Month 1, analytics confirm that free-to-paid conversion drops at a specific feature gate. Month 2, behavioural analysis shows the drop correlates with users who never completed a key workflow. Month 3, an experiment tests a guided completion flow. Month 4, the results inform three more roadmap decisions. Each layer makes the next one faster.

THE SYSTEM

Eight frameworks. Analytics feeds experiments. Experiments feed product decisions. Competitive intel feeds positioning. Each one makes the others sharper.

DISCOVER

Full audit across all 6 dimensions — analytics, product, churn, competitive, revenue ops, and GTM. Every leak sized by revenue impact — so you fix the right things first.

Weeks 1–6 · Foundation audit

Complete growth audit across market intelligence, customer intelligence, analytics, product, revenue ops, and GTM
Every weakness sized by revenue at stake — not a list of suggestions, a ranked map of opportunities
Prioritised opportunity roadmap: what to fix, in what order, with quantified impact for each
Baseline competitive position, customer sentiment, analytics gaps, and revenue leaks documented

INTEL

15+ competitors tracked continuously. You see pricing moves, feature launches, and messaging shifts days after they happen.

Ongoing · Competitive intelligence

Competitive Intelligence Database: 15+ competitors fully indexed (features, pricing, positioning, GTM)
Real-time Slack alerts when competitors ship features, change pricing, announce funding, or hire key people
Monthly INTEL Brief: 10-page competitive narrative — what happened, what it means, what to do about it
Quarterly battle card refresh: competitor-by-competitor positioning your sales team can use on calls

SIGNAL

Every sales call, support ticket, churn exit, and NPS response processed into structured decisions — not unread folders.

Ongoing · Customer intelligence

Unified customer voice pipeline: NLP on every ticket, call, survey, review, and churn exit interview
Every comment tagged by sentiment, JTBD category, persona, lifecycle stage, and urgency
Monthly SIGNAL Report: which jobs are underserved, what predicts churn, what best customers share
Real-time Slack alerts when support themes spike or sentiment drops in a segment
Quarterly JTBD refresh: are customer jobs shifting? Are new personas emerging?

MEASURE

Analytics rebuilt around your users' key value moments. Dashboards your team opens because they answer the questions that matter.

Built in month 1 · Running continuously

Clean event taxonomy: every event audited against your revenue logic, dead tracking removed
10–15 production dashboards built around your business model
Written tracking plan your engineers can maintain without asking Jake
Cohort analysis, drop-off analysis by funnel stage, power analysis infrastructure for experiments
ML models: churn prediction scoring, expansion opportunity identification

BUILD

Activation flows redesigned, onboarding rebuilt, product changes shipped — directly inside your product, from week 2.

Continuous · Production design + engineering

Production-ready Figma specs grounded in customer intelligence and competitive gaps
Onboarding redesigns, activation flow improvements, feature changes — shipped, not recommended
Every change fully instrumented so you can measure impact from day one
Staged rollout with documented results added to decision library

IGNITE

Statistically rigorous experiments running continuously. 10–20/year. Each result sharpens the next — no more inconclusive reads.

3–6 experiments in parallel · Monthly results

Pre-registered hypotheses grounded in SIGNAL, INTEL, and analytics data — not opinions
Power analysis on every test: sample size calculated upfront, results that are definitive
Monthly IGNITE Result Cards: statistically sound, board-ready, one page per experiment
Permanent experiment library: every result documented with learnings that feed the next cycle
10–20 experiments/year building institutional knowledge your team keeps permanently

RETAIN

Your CS team gets a weekly at-risk list — 30–60 days before accounts would have cancelled. Expansion triggers running in parallel.

Weekly delivery · Churn prevention + expansion

ML churn model: 85+ behavioural features, weekly scoring, ranked by probability times revenue at risk
Weekly at-risk list to CS every Monday with specific context (not just “at risk” — why, and what to do)
Intervention Trigger Map: each risk pattern mapped to a specific CS action, in-app message, or email
Expansion signal identification: which accounts are approaching upgrade triggers
Monthly RETAIN Report: save rates, expansion MRR attribution, churn trend analysis

CONVERT

Sales messaging extracted from your best winning conversations. Tested against each competitor. Updated every month.

Monthly · Sales enablement + conversion

Conversion audit: every customer touchpoint scored (landing pages, emails, calls, demos, proposals)
Battle cards per competitor: discovery questions, demo scripts, objection handling — refreshed quarterly
Monthly sales call analysis: which talk tracks close, which lose, coaching notes for the team
A/B tested messaging: headlines, email subjects, demo scripts validated through IGNITE
Quarterly messaging refresh as SIGNAL feeds new customer language back into positioning

WHAT YOU ARE GETTING

What your team owns after month 6 — and what runs every week until then.

Built in month 1 — yours permanently

One-time deliverables completed in the first 4–6 weeks.

Deliverable	Standalone cost
Full analytics audit + event taxonomy rebuild against your business model	$15,000
10–15 production dashboards (activation, retention, feature adoption, product health, revenue, expansion)	$12,000
Written tracking plan + event specifications your engineers can maintain	$5,000
JTBD mapping, behavioural persona development, customer segmentation	$8,000
Competitive landscape analysis (15+ competitors, feature matrix, positioning gaps)	$10,000
Churn prediction model (85+ behavioural features, deployed and validated)	$15,000
Decision frameworks + experiment backlog (prioritised by impact)	$5,000
One-time build total	$70,000

Running every month — each cycle sharper than the last

Ongoing systems that get sharper each cycle.

What runs every month	Standalone value
3–6 experiments designed, managed, and statistically analysed	$6,000/mo
Churn model updated + weekly at-risk list to CS every Monday	$5,500/mo
Competitive monitoring — weekly Slack alerts, monthly brief, quarterly battle cards	$2,500/mo
Customer intelligence pipeline — NLP on tickets, calls, churn exits	$4,000/mo
Monthly Growth OS Report (board-ready, 8–12 slides)	$2,000/mo
Monthly behavioural analysis + decision framework updates	$3,000/mo
Expansion signal identification + intervention effectiveness tracking	$2,500/mo
Experiment library + decision log (institutional learning, permanent)	$1,500/mo
Monthly recurring total	$27,000/mo

6-month engagement value: $70K build + $162K ongoing = $232,000. Growth OS price: from $30K/mo · from $180K total.

Based on agency rates for equivalent scope.

THE HONEST COMPARISON

The alternatives cost more and compound less.

The alternatives are hiring, agencies, or fractional leaders. Here is what each actually gives you.

	Growth OS	VP of Growth hire	Growth agency	Fractional leader
Time to impact	Weeks	3–6 months to hire, then ramp	Weeks — one channel only	2–4 weeks, limited hours
What they cover	Analytics, experiments, churn, competitive, strategy	Depends on the hire	One channel	Strategy only
Changes in your product?	Yes	Only if technical	No	No
Work compounds?	Each month feeds the next	Only if they stay	Resets when contract ends	Resets when hours run out
What you keep	Everything — dashboards, research, frameworks, docs	Whatever they documented	Campaign assets	Recommendations
Annual cost	from $360K/yr	$200–350K + equity	$120–360K	$60–180K

FROM ENGAGEMENT

A B2B SaaS platform at $8M ARR had four people with opinions about why activation was low. Each quarter, the theory changed. The analytics existed but nobody trusted the numbers — events were firing but the taxonomy had drifted from the business model. Month 1 rebuilt the instrumentation. Month 2 identified the specific onboarding step that accounted for the majority of the drop-off. Month 3 ran three experiments, two of which produced clear ship-or-kill decisions. By month 4, the team had stopped debating the bottleneck and started iterating on it.

WHAT GROWTH OS INCLUDES

Six disciplines. Every one running inside your business.

Growth OS isn't a project with a scope list. It is the full operating system — research, analytics, machine learning, activation, revenue, and competitive intelligence — embedded and compounding every month.

Every decision inside Growth OS is grounded in what your actual customers say, do, and want — not what the team thinks. We run a unified customer intelligence pipeline: NLP across every sales call, support ticket, churn exit, and NPS response, structured into a monthly SIGNAL Report that tells you which jobs are underserved, what predicts churn, and what your best customers have in common. Updated every month. Integrated into every experiment brief.

Unified customer voice pipeline — NLP on tickets, calls, churn exits, NPS
Every comment tagged by sentiment, JTBD category, persona, lifecycle stage, and urgency
Monthly SIGNAL Report: underserved jobs, churn predictors, best-customer patterns
Real-time Slack alerts when support themes spike or sentiment drops in a segment
JTBD mapping, behavioural persona development, and customer segmentation
Quarterly JTBD refresh: are customer jobs shifting? New personas emerging?
Baseline customer sentiment and competitive perception documented in DISCOVER audit
Willingness-to-pay research from structured interviews and usage pattern analysis

What your customers actually need — processed, not filed

Most companies collect customer feedback and do very little with it. Growth OS turns the entire signal stream into structured decisions, every month. The SIGNAL Report feeds every experiment brief and every roadmap call.

Month 1 rebuilds your analytics from the ground up — event taxonomy audited against your revenue logic, dead tracking removed, 10–15 production dashboards built around your actual business model. From month 2, IGNITE runs 3–6 experiments in parallel: pre-registered hypotheses, power analysis upfront, statistically sound results. Every experiment produces a ship-or-kill decision. Every result goes into a permanent library your team keeps after the engagement ends.

Full event taxonomy audit — every event validated against your revenue logic
10–15 production dashboards: activation, retention, feature adoption, revenue, expansion
Written tracking plan your engineers can maintain without asking
Cohort analysis, drop-off analysis by funnel stage, conversion funnel diagnosis
Power analysis infrastructure — sample size calculated before every test starts
3–6 experiments in parallel, 10–20 per year
Pre-registered hypotheses grounded in SIGNAL, INTEL, and analytics data
Monthly IGNITE Result Cards: statistically sound, board-ready, one page per experiment
Permanent experiment library: every result documented with learnings that feed the next cycle

Numbers you can act on, and experiments that compound

No directional signals. No inconclusive reads. Every experiment starts with a hypothesis grounded in customer data and ends with a decision. The library that builds month over month is yours permanently.

Growth OS builds a churn prediction model with 85+ behavioural features — trained on your data, deployed and validated in month 1, then scored weekly. Every Monday your CS team receives a ranked at-risk list: not just who is at risk, but why, and exactly what to do. Expansion signals run in parallel — accounts approaching upgrade triggers flagged before the window closes. Updated monthly as new behavioural data comes in.

ML churn model: 85+ behavioural features, trained and validated on your data
Weekly scoring — every account ranked by churn probability times revenue at risk
Weekly at-risk list delivered to CS every Monday with context and recommended action
Intervention Trigger Map: each risk pattern mapped to a specific CS action, in-app message, or email
Expansion propensity scoring — accounts approaching upgrade triggers identified early
Feature adoption signals: which usage patterns predict retention and which predict departure
Revenue and retention forecasting built from behavioural cohort data
Monthly RETAIN Report: save rates, expansion MRR attribution, churn trend analysis
Model updated monthly as new behavioural data and intervention outcomes are recorded

Churn risk identified 30–60 days before it becomes visible

The model doesn't just flag accounts — it tells your CS team what the signal is and what to do about it. Intervention playbooks built per risk pattern. Expansion signals running in parallel so the team is acting on both ends of revenue at the same time.

Growth OS finds exactly where signups stop — and builds what fixes it. Production-ready Figma specs grounded in customer intelligence and competitive research. Onboarding redesigns and activation flow improvements shipped directly into your product, not handed over as recommendations. Every change fully instrumented from day one so you see the result. Staged rollouts with documented outcomes added to the decision library.

First-value-moment mapping: where users first understand what your product does for them
Onboarding funnel instrumentation and drop-off diagnosis by step
Production-ready Figma specs grounded in customer intelligence and competitive gaps
Activation redesigns and flow improvements shipped — not recommended
In-app guidance, tooltip design, and friction removal
Free-to-paid conversion sequencing built from behavioural analysis
Activation metric definition, dashboard, and cohort tracking
Every change fully instrumented so impact is measurable from day one
Staged rollouts with results documented and added to the decision library

More signups reaching value — built and shipped, not advised

The difference from a consultant: we ship the fix. Production Figma specs, instrumented changes, staged rollouts with measured outcomes. Activation improvements compound as each month's data refines the next experiment.

Growth OS runs a conversion audit across every customer touchpoint — landing pages, emails, demo calls, proposals — and builds the sales enablement layer from your actual winning conversations. Monthly sales call analysis: which talk tracks close, which lose, coaching notes for the team. A/B tested messaging validated through IGNITE. Battle cards per competitor refreshed quarterly. Pricing model analysis and upgrade trigger identification running continuously.

Conversion audit: every customer touchpoint scored (pages, emails, calls, demos, proposals)
Monthly sales call analysis: which talk tracks close, which lose, and why
Coaching notes for the sales team based on winning conversation patterns
A/B tested messaging: headlines, email subjects, demo scripts validated through IGNITE
Quarterly messaging refresh as SIGNAL feeds new customer language into positioning
Pricing model analysis and upgrade trigger identification
Expansion and upsell playbooks built from usage pattern data
Net revenue retention (NRR) modelling and plan structure review
Expansion MRR attribution tracked monthly through RETAIN

More revenue from the users you already have

Revenue growth without needing more signups. The sales enablement layer is built from your actual winning conversations and tested through the experiment program — not assembled from templates.

Growth OS starts with a full DISCOVER audit: every growth dimension assessed — analytics, product, churn, competitive, revenue ops, and GTM — every weakness sized by revenue at stake. Then competitive intelligence runs continuously: 15+ competitors fully indexed, real-time Slack alerts when they move on pricing, features, or hiring, monthly INTEL Brief with the competitive narrative and what to do about it. Monthly Growth OS Report (8–12 slides, board-ready) keeps your leadership aligned on what's running and what it's producing.

Full DISCOVER audit: analytics, product, churn, competitive, revenue ops, and GTM
Every weakness sized by revenue at stake — not a list, a prioritised opportunity roadmap
15+ competitors continuously indexed: features, pricing, positioning, GTM
Real-time Slack alerts when competitors ship features, change pricing, or announce funding
Monthly INTEL Brief: competitive narrative — what happened, what it means, what to do
Quarterly battle card refresh: per-competitor positioning your sales team uses on calls
Monthly Growth OS Report: 8–12 slides, board-ready, results and next cycle priorities
Permanent decision library: every experiment result, intervention, and strategic call documented
Written tracking plan and analytics governance your team inherits and maintains after the engagement

The operating layer that keeps every other discipline connected

DISCOVER scopes the full opportunity. INTEL keeps you ahead of market moves. The monthly report and decision library turn six months of work into institutional knowledge — a permanent asset your team keeps after Growth OS ends.

FIT CHECK

You have revenue, data, and a team — but growth has flattened and nobody knows why.

POST-SEED · $1–5M ARR

Shipped features. No decision framework.

Founder or CPO making product calls

The situation

You have built enough to know what works. But every roadmap decision is still a negotiation between intuitions. Effort never builds on itself — each sprint resets. You need a framework where each month's data informs the next decision.

What changes

Analytics tied to decisions your team actually makes, not vanity metrics
Experiment results that are definitive, not arguable
A decision framework that new hires can onboard into

Six months from now, product decisions take an afternoon, not a quarter.

SERIES A · $5–20M ARR

Data, but it is not driving decisions.

Head of Product or CPO + small team

The situation

PostHog or Mixpanel installed. Dashboards exist. But when did a dashboard last change a decision? The data exists in silos. Analytics measure what was easy to track — not what the business needs to know.

What changes

Every event audited against your business model logic
Instrumentation gaps identified and closed
Experiment velocity goes from 0–2/quarter to 3–6/month

The data you already have starts driving the decisions it was meant to drive.

SERIES B · $20–50M ARR

Growing team, no shared framework.

Multiple PMs, each running their own process

The situation

Each PM has their own way of deciding. Roadmap planning is a negotiation, not a framework. New hires take six months to understand how decisions get made. The Growth OS installs the shared taxonomy, dashboards, and decision log.

What changes

Decision frameworks extracted from what the data showed and documented
Experiment library with results every PM can reference
New hires onboard into a working system, not institutional memory

Product org becomes consistent without becoming slow.

NOT A FIT

Pre-product, no analytics, or growth is not the constraint yet

Wrong stage or wrong problem

When the Growth OS does not apply

If you have not shipped a product yet, there is no system to build on. If you are pre-revenue or below $1M ARR, a retained engagement at this price point is not the right first step. And if you need a single answer to one question — where does activation break, or what does churn look like — a sprint is a better fit than an operating system.

Better starting points

Activation Deep Dive

Find where users drop off and fix it first. $4,997, 2-week sprint.

Focused Sprint

One question, one answer, one deliverable your team acts on immediately.

The Foundation

Full product analysis covering activation, retention, competitive, and go-to-market.

What the Growth OS does not replace

Growth OS is a product growth operating system. It does not replace your engineering team, your sales org, or your marketing function. It makes each of them more effective by giving them better data, tested frameworks, and a shared decision system.

Engineering headcount — your team ships the product changes Jake designs
Sales process — the OS provides battle cards and messaging, your reps close the deals
Brand marketing — the OS covers product growth, not demand generation or brand campaigns

For a lighter engagement → Growth LAB

Jake McMahon — ProductQuant

Jake McMahon

8+ years building product systems inside B2B SaaS · Behavioural Psychology + Big Data (Masters)

I built the Growth OS because I kept seeing the same pattern: companies with real data, real teams, and real intent — where every sprint reset and nothing built on what came before. The problem was not effort. It was the absence of a shared decision framework that retained what it learned.

The Growth OS is not consulting. At the end of the engagement, you have a running operation your team owns. The analytics infrastructure, the experiment library, the decision frameworks — all of it stays. None of it depends on me being there.

I will not do this:

Deliver a strategy deck without the infrastructure to execute it
Build dashboards nobody opens because they do not answer real questions
Run experiments your team cannot learn from or replicate
Hand over frameworks that require Jake to keep running

What does “your team executes” actually mean?

Jake designs the frameworks, writes the tracking plan, designs the experiments, and manages the analysis. Your engineers implement event tracking and ship the product changes. Your PM team reviews results and signs off on roadmap decisions. It takes roughly 2–4 hours of your team's time per week to keep the OS running at full capacity.

Teams Jake has worked with

From $30K/month. Your team makes better growth decisions every month — because the data, experiments, and models compound.

from $30K/mo

6-month minimum · from $180K total

Scoped by product complexity, industry, and execution depth

Analytics audit and instrumentation against your business model
Written tracking plan your engineers can maintain
3–6 experiments designed and managed per month
Monthly behavioural analysis and pattern identification
Decision frameworks extracted from experiment results
Monthly Growth OS Report: results, frameworks, next cycle
Weekly async communication via Slack
Experiment library and decision log — yours permanently

Everything we build stays with you.

Start a conversation →

If you are not seeing measurable progress by month 2, we extend at no cost until you do. Measurable progress means at least 3 experiments that produce clear ship-or-kill decisions, a churn model giving CS 30+ days lead time on at-risk accounts, and dashboards driving weekly product decisions.

Questions.

Or book a call →

How is this different from the Growth LAB? +

Growth LAB is Jake providing analysis and strategy while your team executes. Growth OS is the same system but with more emphasis on building the decision infrastructure your team owns long-term. Both compound. The OS is designed specifically for companies that want to walk away with a fully documented, independently operable system at the end.

How quickly can we start? +

Kickoff within 2 weeks. Month 1 runs the analytics instrumentation and audit. Month 2 moves to interpretation and pattern identification. Month 3 moves to experiments and decisions. By month 4 the compounding cycle is fully running.

What does my team need to contribute? +

Roughly 2–4 hours per week. Jake handles the analysis, experiment design, and system documentation. Your engineers implement event tracking and ship product changes. Your PM team reviews results and makes final roadmap calls. The split is clear from week one.

What happens after 6 months? +

At month 6, you have a working system. Most clients continue month-to-month to keep the experiment engine running and the frameworks evolving. Some graduate to running it independently using the documented frameworks Jake built. Either path works — the system is designed to function without ongoing dependency on Jake.

We already have analytics tools. Do we need to change them? +

No. Growth OS builds on what you already have. Most clients are running PostHog, Mixpanel, or Amplitude. The first month audits what those tools are actually capturing and confirms it against your business model logic. The tools are not the problem. The instrumentation is.

Why not just hire a VP of Growth? +

A VP of Growth is $200K–$350K plus equity. Then 3–6 months to hire, another 6–12 months to ramp. You are looking at 18 months and $700K+ before you know if it is working. Growth OS starts in weeks. Full capacity from month 1. And everything we build stays with you — so when you do hire, they inherit a running system instead of building from scratch.

What is the ROI? +

It depends on where the bottleneck is and how much revenue is sitting on the other side of fixing it. A 10-point activation improvement at $1M ARR is worth a different number than the same improvement at $20M ARR. Month 1 quantifies the specific opportunity in your business — so the ROI case is built from your actual data, not a formula. Most engagements pay back within the first 6 months when a meaningful activation, retention, or expansion lever is found and pulled.

Know which lever to pull this quarter — and prove it worked before the next one starts.

Your growth ceiling has a specific cause. This finds it and fixes it.

Each month builds on the last. By month 4, the dashboards, models, and experiments run faster than you thought possible.

Instrument

Interpret

Decide

Compound

Month 2 churn data designs month 3 experiments. Month 3 results shape month 4's roadmap. That's compounding.

Eight frameworks. Analytics feeds experiments. Experiments feed product decisions. Competitive intel feeds positioning. Each one makes the others sharper.

What your team owns after month 6 — and what runs every week until then.

The alternatives cost more and compound less.

Six disciplines. Every one running inside your business.

You have revenue, data, and a team — but growth has flattened and nobody knows why.

What the Growth OS does not replace

From $30K/month. Your team makes better growth decisions every month — because the data, experiments, and models compound.

Questions.

A year from now, your team still makes faster, better growth decisions — because the system compounds.