6-WEEK COHORT PROGRAM · $950/SEAT

Eight weeks. We install the full ProductQuant system into your product and leave you with a live PMF signal dashboard, a confirmed or refuted JTBD hypothesis, and a go/no-go recommendation backed by quantitative and qualitative evidence.

This is not a desk exercise or a workshop where you watch slides and take notes. It’s an 8-week intensive where we classify your product type, map the jobs your customers are hiring it to do, instrument your analytics properly, build your retention analysis from scratch, code your qualitative evidence, run 2–3 structured experiments against your JTBD hypotheses, and produce a presentation-ready recommendation. The system stays in your product after the program ends. Cohort of 6–8, applied to your own data throughout.

Jake McMahon Jake McMahon, ProductQuant
Apply for the next cohort → Read the full curriculum ↓

PROGRAM DETAILS

Duration 6 weeks
Sessions 2 live sessions per week
Cohort size 6–8 participants
Async work Applied to your own product and data
Recording All sessions recorded

$950/seat · max 8 seats per cohort

Delivered by Jake McMahon · Founder, ProductQuant · 8+ years B2B SaaS product analytics · Australian product leader
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Duration
8 weeks, 2 sessions/week
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Format
Live cohort via Zoom, max 8 seats
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Deliverable
8 systems installed: DNA, JTBD, PostHog, retention, experiments, go/no-go
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Price
$950/seat
Does This Sound Familiar

Users say it’s great. The data says something different.

The user feedback problem
Ten users have told you the product is great. They’re enthusiastic in calls. They give you quotes you want to put on the website. But paid conversion is 1.2% and you can’t explain why. Positive qualitative signal and weak quantitative signal at the same time usually means you have fit with one segment and noise from everyone else — and you haven’t separated them yet.
The NPS paradox
Your NPS is 42. Customers say they’d recommend you. Month-2 churn is 40%. Those two things coexist because NPS measures sentiment at a point in time and churn measures whether the product solves a recurring problem. You can have happy customers who don’t need your product enough to keep paying for it. NPS doesn’t tell you which jobs you’re actually solving.
The unexplained growth
You had a good month. Then a slower one. Then a better one. The team has theories — a product change, a campaign, a channel — but the correlation isn’t clear. Growth that comes and goes without explanation usually means you’re catching customers whose job-to-be-done matches your product by accident. You haven’t found the systematic signal yet. You’re not in a position to scale it.
Before & After

What changes after 8 weeks.

Before the program
  • Positive user feedback but weak conversion — no way to tell if it’s a product problem, segment problem, or instrumentation problem
  • Analytics tracking sessions and pageviews instead of activation rate, time-to-retained, and cohort retention shape
  • No PMF narrative backed by data — just customer quotes and gut feel
After the program
  • JTBD hypothesis confirmed or refuted by your own retention data — evidence, not opinion
  • PostHog instrumented to measure the right signals for your product type and growth model
  • Go/no-go recommendation document presentation-ready, with the evidence cited and the reasoning explicit
Outcomes

Eight working systems, installed in your product. Not a report. Infrastructure.

The difference between this program and individual consulting: you leave with working infrastructure — instrumented PostHog, retention analysis on your own data, and 2–3 experiments actually run. Not a strategy document. Systems that stay in your product after the program ends.

Product DNA classification — the type of product you actually have
PMF means something different depending on what kind of product you’re building. A horizontal workflow tool has a different retention signature than a vertical SaaS built for one industry. We classify your product type using the ProductQuant DNA framework before we touch any data, so the entire analysis is calibrated to the right model from the start.
JTBD map — the jobs your customers are hiring your product to do
Jobs-to-be-done is the theory of PMF you’re testing. We build a structured JTBD map from your customer conversations: the functional job, the emotional job, the context, and the competing alternatives your customer was considering. This is the hypothesis the rest of the program is designed to confirm or refute with evidence.
Analytics instrumentation — PostHog configured to measure what actually matters
Most products are tracking the wrong things. We instrument PostHog to capture the specific events, funnels, and cohort properties that correspond to your JTBD hypothesis and product type. You leave with a working analytics setup that distinguishes activated users from retained ones — a distinction most products can’t make accurately before this program.
Retention curve analysis with activation-to-retained correlation
The quantitative signature of PMF is a retention curve that flattens rather than trends to zero. We run your retention analysis on the properly instrumented data, identify whether a stable retention floor exists and for which segment, and map the correlation between activation events and long-term retention. If the floor doesn’t exist, the analysis tells you why and where the drop-off is occurring.
Qualitative evidence coding — structured synthesis from conversations you already have
You already have customer conversations. Sales calls, onboarding sessions, churn interviews, support tickets. We code this material using a structured framework against your JTBD hypothesis — identifying what your best customers value, what language they use to describe the problem, where churned accounts diverged, and what the pattern says about the depth of fit you have.
PMF signal dashboard — a live view of the 4–5 metrics that tell you whether you’re finding fit
A live PostHog dashboard built specifically for your product type, showing the metrics that correspond to PMF signal for your category: retention floor, activation rate, time-to-value, qualitative sentiment pattern, and expansion revenue signal where applicable. This stays in your product after the program ends. You’ll never have to argue about whether you have PMF from a dashboard built for someone else’s product.
Experiment framework — 2–3 structured tests designed around your JTBD hypotheses
We design 2–3 experiments targeted at the specific gaps your JTBD map and retention analysis reveal. Each experiment has a clear hypothesis, a defined measurement, and a pre-specified decision rule. Not growth hacks. Controlled tests that produce evidence about whether changing one thing moves the PMF signal in the right direction. You run these during the program with support on interpreting the results.
Go/no-go recommendation — a presentation-ready document with the evidence and the verdict
The final output is a structured recommendation document that synthesises the DNA classification, JTBD map, retention analysis, qualitative evidence coding, and experiment results into a clear position on PMF status. Formatted to present to a board or investors. Not a summary of what the team felt — a structured argument from evidence, with the signal sources cited and the reasoning explicit. This is the document that determines what you do next.

PROGRAM FORMAT

8 weeks. Live cohort. Every week applied directly to your product and data.

Duration
8 weeks
Weeks 1–2: DNA classification and JTBD mapping. Weeks 3–4: analytics instrumentation and PostHog setup. Weeks 5–6: retention analysis and qualitative coding. Weeks 7–8: experiments and go/no-go recommendation.
Sessions
2×/week
Two live sessions per week. One instruction session covering the method; one working session where each participant applies it to their own product with Jake reviewing in real time.
Cohort size
6–8 seats
Capped at 8. Small enough for Jake to review every retention analysis and qualitative coding exercise personally. The cohort also surfaces how the same signals manifest differently across product types.
Async work
~3 hrs/week
Structured tasks applied to your own data each week. PostHog configuration, retention builds, JTBD interview coding, experiment design. The work accumulates into the 8 deliverables over the 8 weeks.
Platform
Zoom + Slack
Live sessions on Zoom. Async work, feedback, and cohort discussion in a private Slack channel. All sessions recorded and available for 12 months.
Who joins
Founder / PM
Best suited to a founder or head of product with access to customer conversations and product analytics. You need both — the qualitative and quantitative work runs in parallel throughout.
8-Week Agenda

What gets built each fortnight.

W1–2
DNA classification + JTBD mapping
Classify your product type across 10 dimensions. Build a structured JTBD map from your customer conversations — the hypothesis the rest of the program tests.
W3–4
Analytics instrumentation + PostHog setup
Instrument PostHog to capture the events, funnels, and cohort properties that correspond to your JTBD hypothesis. Different data comes out the other side.
W5–6
Retention analysis + qualitative coding
Run the retention analysis on properly instrumented data. Code your existing customer conversations against the JTBD hypothesis using a structured framework.
W7–8
Experiments + go/no-go recommendation
Design and run 2–3 structured experiments. Interpret results live with Jake. Produce the final go/no-go recommendation document with evidence cited throughout.
Read the full curriculum →
Fit

Who this program is built for.

What Most PMF Programs Miss

Individual consulting gets you to the right questions. This program builds the infrastructure to answer them.

High-quality individual practicum formats are structured as mentor-led consulting: one diagnostic session, four or five individual sessions, a refined hypothesis, a segmentation map, and a scaling roadmap. That’s genuinely useful. But it’s strategy output. What most of these programs are not designed to deliver is what you need most: working analytical infrastructure, data-grounded retention evidence, and experiments that actually run. That’s the gap this program closes.

Individual consulting / practicum
Hypothesis refined by mentor experience
Your JTBD hypothesis is sharpened based on what the mentor has seen across other startups. It leaves the program clearer — but still untested against your retention data.
Analytics advice, no instrumentation change
You learn what you should be measuring. Your PostHog — or whatever tool you’re using — still captures the same events it did before the program started.
Segmentation map as a document
New target segments are identified and written up. They haven’t been stress-tested against retention behaviour. You don’t know yet whether the segments that look attractive actually retain.
Scaling roadmap as output
The program ends with a structured plan for what to do next. But PMF isn’t a planning problem — it’s a signal problem. The roadmap hasn’t produced any signal yet.
Individual format: no peer pattern recognition
Working 1:1 means you’re only seeing your own product’s signals. You miss the patterns that emerge when multiple products run the same analysis in parallel — which retention shapes are product-type-specific and which are universal.
Investor presentation materials
You leave with a pitch-ready document. But if your board asks which specific metrics confirm your PMF claim, the answer is still based on the mentor’s reasoning — not your own data.
PMF Validation Program
JTBD hypothesis tested against your own retention data
The hypothesis is mapped in Week 1 and then stress-tested by weeks 5–6 of retention analysis and qualitative evidence coding. You know whether the data confirms it — not whether it sounds right.
PostHog instrumented to measure the right signals
Weeks 3–4 change what your analytics is actually capturing — events, funnels, and cohort properties calibrated to your JTBD hypothesis and product type. Different data comes out the other side.
Retention curve analysis with activation-to-retained correlation
The quantitative PMF signal — a retention curve that flattens rather than decays to zero — is built from properly instrumented data. You see which segment retains, what they activated on, and where the floor is (or isn’t).
2–3 experiments designed and run during the program
Each experiment targets a specific gap in the JTBD map, has a defined measurement, and a pre-specified decision rule. You run them during weeks 7–8 and interpret results with live support. Evidence, not roadmap.
Cohort of 6–8: you see multiple product types in parallel
Running the same retention analysis across 6–8 different products reveals which signals are product-type-specific and which generalise. Patterns you’d never see in isolation become visible when the cohort shares work in real time.
Go/no-go recommendation backed by your own evidence
The final document cites your retention data, your JTBD map, your experiment results, your qualitative coding. When your board asks for the evidence behind the PMF claim, you point to the dashboard and the analysis — not the consultant’s opinion.
Results

What teams leave with.

“Placeholder — replace with real cohort testimonial.”
Name, Role — Company
“Placeholder — replace with real cohort testimonial.”
Name, Role — Company
“Placeholder — replace with real cohort testimonial.”
Name, Role — Company
Not a Fit

This program is specific. So is the fit.

Value

Comparable engagement. A fraction of the cost.

A PMF consulting engagement runs $5,000–$15,000 for a deliverable at this level of specificity: DNA classification, JTBD map tested against retention data, PostHog instrumented for your product type, qualitative evidence coded, and a go/no-go recommendation. This cohort delivers the same structured output for $950/seat across 6 weeks, with peer review from teams working through the same class of problems.
Guarantee

We back the result, not just the experience.

30-Day Guarantee
If the program doesn’t produce a validated PMF hypothesis with a named retention segment and a measurable threshold — tell us within 30 days and you’ll get a full refund.
Pricing

$950 per seat. Eight systems installed. One cohort price.

Per Seat
$950 /seat

One-time payment. Each component commissioned separately would run substantially more. This cohort delivers the full system — PostHog instrumented around your actual hypothesis, retention analysis run on your data, and 2–3 experiments run during the program — at a fraction of the standalone cost.

  • Product DNA classification (your product type, calibrated before analysis begins)
  • JTBD map (the hypothesis the whole program tests)
  • PostHog instrumentation (analytics configured to measure the right signals)
  • Retention curve analysis with activation-to-retained correlation
  • Qualitative evidence coding from your existing customer conversations
  • PMF signal dashboard (live, stays in your product after the program)
  • Experiment framework (2–3 structured tests against your JTBD hypotheses)
  • Go/no-go recommendation document (presentation-ready, evidence-backed)
  • 16 live sessions over 8 weeks (2 per week), all recorded with 12-month access
  • Cohort of 6–8 — every analysis reviewed by Jake personally
  • Private cohort Slack channel for async support throughout
Apply for the next cohort →
30-day full refund if the program doesn’t produce a validated PMF hypothesis with a named retention segment.
FAQ

Questions.

Or apply directly →
How many customer conversations do we need? +
The qualitative evidence coding is most productive with at least 10–15 customer conversations available — sales calls, onboarding calls, churn interviews, or user research sessions. If you have fewer than 10, get in touch before applying. Jake will tell you whether the cohort makes sense now or whether you need to run more discovery first.
Do we need to be on PostHog already? +
No. Weeks 3–4 of the program cover PostHog instrumentation from scratch. If you’re already on PostHog, we rebuild the configuration around your JTBD hypothesis and product type, which will likely change what you’re tracking. If you’re on a different analytics tool, the retention analysis methodology still applies — we’ll work with what you have and configure PostHog in parallel.
What if our retention data is limited? +
You need at least 6 months of customer data to run a meaningful retention curve analysis. With less than that, the cohort data is too thin to be reliable. The JTBD mapping and qualitative work can still run, but the full program is most valuable when both signals are available. If you’re under 6 months of data, get in touch and Jake will advise on what makes sense for your current stage.
Is this useful if we think we already have PMF? +
Often the most valuable outcome here is confirming exactly where PMF exists and where it doesn’t. The most common situation is a team with PMF for one specific segment or use case that doesn’t yet extend across the whole product. The program locates the precise boundary — which customer type, which job, which context — and builds the experiment framework for extending fit beyond that initial beachhead.
When is the next cohort? +
Cohort dates are confirmed once enough applications are in to make the cohort worthwhile. Apply via the booking link and Jake will reach out with dates and a short conversation to make sure the program is the right fit for your current stage. No obligation to enroll after that call.
How does this relate to the Product DNA Live Session? +
The Product DNA session is a single 90-minute live session where we classify your product type and map the strategic implications. The PMF Validation Program includes DNA classification in Week 1 and then builds the full analytical infrastructure on top of it over 8 weeks. If you want to test the DNA framework before committing to the full program, booking the standalone session first makes sense. It’s $197 and comes off the program price if you enroll.

You’ve been running on a hypothesis about who your product is for. This cohort is how you find out if that hypothesis is right — before you scale the wrong thing.

The PMF signal dashboard stays in your product after the program ends — so the same infrastructure that confirmed your hypothesis continues to tell you where fit is expanding and where it isn’t.

Apply for the next cohort → Read the full curriculum →