COHORT PROGRAM — $950/SEAT · 4 WEEKS

Data-Driven PMF Validation

You have customers and data. The signal is mixed — some things work, others don’t, and you’re not sure whether to fix the product or double down on a segment. In 4 weeks, you’ll replace team assumptions with evidence and build a 90-day plan grounded in real PMF signal.

Jake McMahon, ProductQuant

Join the waitlist → See the curriculum ↓

For founders at early-to-growth stage B2B SaaS with $200K–$3M ARR.

WHAT’S INCLUDED

Live Sessions 8 sessions, 75 min each, 2×/week

PMF Hypothesis Map 3 core hypotheses defined, scoped, and tested

JTBD Validation Real frequency data from your own calls, not team recall

Segment Clarity Which customer segment has the strongest PMF signal

90-Day Action Plan What to fix, double down on, and stop building

$950/seat · cohort-based · limited seats

The PMF problem this cohort is designed to solve

You think you have PMF. You’re not sure.

Some customers love the product. Others churn after two months. Retention is uneven across segments but you’re not sure which segment is the signal and which is the noise. “PMF” feels like a feeling, not a measurement.

Your roadmap is built on team recall, not customer evidence.

The team believes Feature X is the core value driver because it came up a lot in calls. But nobody has actually counted how many calls, and nobody coded them systematically. The priority stack is a consensus memory, not an analysis.

You’re not sure whether to fix things or double down.

Should you fix activation for your current ideal customer profile (ICP), or go harder on the segment where retention is strongest? Without a clear PMF signal by segment, you’re paralysed — or you run both tracks at half speed.

WHO THIS IS FOR

Built for founders ready to replace intuition with evidence.

Right fit

Founders at B2B SaaS companies with $200K–$3M ARR
You have customers, some product data, and a sense that something is off
You have sales call recordings or customer conversations you’ve never systematically coded
Your team has strong opinions about priorities but weak evidence for them
You can commit ~5 hrs/week for 4 weeks (sessions + async)

Not the right fit

Pre-revenue or fewer than 10 customers — not enough signal to analyse yet
You already have a clear PMF signal and just need to scale — the scale motion is different
You have no sales call recordings or customer interviews to work from
You want someone to do the PMF analysis for you — this cohort teaches the process

WHAT PMF ACTUALLY IS

Three ways teams fool themselves into thinking they have it.

MYTH 01

“Our NPS is high so we have PMF.”

NPS measures satisfaction, not retention. High NPS scores do not prevent churn — companies can score well on satisfaction surveys while still losing a significant share of customers each year. PMF shows up in retention curves, not surveys.

MYTH 02

“Customers are using the product so we have PMF.”

Usage without retention isn’t PMF. The signal is whether customers who use a specific set of features in a specific way stay and expand — not whether they log in.

MYTH 03

“Revenue is growing so we must have PMF.”

Early revenue can come from founder-led sales that won’t repeat. PMF means the value proposition works without the founder in the room. It shows up in expansion revenue and referral from customers you didn’t directly close.

WHAT IT ACTUALLY IS

A specific segment retaining, expanding, and referring at a measurable rate.

PMF is segment-specific. You can have it for one ICP and not for another. The work of this cohort is finding which segment it’s strongest for — and building the evidence to prove it.

WHAT YOU’LL LEAVE WITH

Five deliverables built on your own sales calls and product data.

PMF hypothesis map (3 core hypotheses defined, scoped, and tested)

Your current beliefs about ICP, value proposition, and feature priorities — structured as testable claims with the evidence for and against each one. The foundation the rest of the cohort is built on.

Validated JTBD framework with real frequency data

Jobs-to-be-done coded from your own sales calls or customer interviews. Not team recall — actual frequency counts from systematically reviewed conversations. You’ll know which jobs your product is actually hired for, and how often.

Feature priority correction

Which features actually drive retention vs. which ones the team thinks do. Evidence-based priority stack built from product data, not from who makes the strongest case in sprint planning.

Segment clarity — where your strongest PMF signal lives

Using product and sales data to identify which customer segment has the strongest retention, expansion, and PMF signal right now. Not a gut call — a documented comparison with the evidence for each segment.

90-day action plan: what to fix, what to double down on, what to stop building

A concrete plan built from your validated evidence. What broken PMF signals to fix in the next 90 days, which segment to double down on, and which low-evidence features to deprioritise.

THE CURRICULUM

4 weeks from hypothesis to evidence-backed 90-day plan.

WEEK 1

What PMF Actually Means for Your Product

SESSION 1

PMF is not a feeling

What a real PMF signal looks like in product and revenue data. The 3 ways teams fool themselves into thinking they have it: the NPS trap, the usage trap, and the revenue trap. What to look for instead.

SESSION 2

Mapping your PMF hypotheses

Structuring what your team currently believes about your ICP, value proposition, and feature priorities as testable claims. Not just “our customers are SMBs” but “SMB HR teams with 50–200 employees retain at 80%+ because of X” — with the evidence that would confirm or deny it.

Async — before Week 2

Complete the PMF hypothesis map for your product. Identify your top 3 untested beliefs — things the whole team assumes are true but has never verified with data. Bring them to Week 2.

WEEK 2

Finding the Evidence

SESSION 3

Sales call analysis — the ProductQuant approach

How to code recorded sales calls for JTBD mentions, segment signals, and feature priorities. The specific coding framework used to find 85+ distinct jobs from 60 calls — three times what the team believed they had. Live coding session with a call shared by a cohort participant.

SESSION 4

Product data as PMF evidence

Cohort retention curves, activation patterns, feature adoption by segment, and revenue expansion signals. How to read each one as evidence for or against your PMF hypotheses — and what “strong PMF signal” actually looks like in the numbers.

Async — before Week 3

Code 10+ sales calls or customer conversations using the JTBD coding framework provided. Count frequencies for each job. Share your coded results in Slack before Session 5.

WEEK 3

Correcting the Assumptions

SESSION 5

Belief vs evidence — where do you hold up?

Comparing your Week 1 hypothesis map against your Week 2 coded evidence. Where the team’s assumptions hold up — and where they break. The specific framing for running this session with your full team, not just the cohort.

SESSION 6

Segment analysis

Using product and sales data to identify which customer segment has the strongest PMF signal. Cohort review of segment comparisons built by participants during the async week. Which segment would you bet on — and why?

Async — before Week 4

Build your segment comparison document. Which 2–3 customer segments do you have meaningful data on? Which one has the strongest retention, expansion, and PMF evidence? Submit before Session 7.

WEEK 4

The 90-Day Plan

SESSION 7

Feature priority correction

Reranking your roadmap based on validated evidence from Weeks 2–3. Which features actually drive retention for your strongest PMF segment. How to deprioritise features with weak evidence without killing team morale. How to present this to the rest of the team.

SESSION 8

The 90-day action plan

What to fix (broken PMF signals in your strongest segment), what to double down on (the features and motions with the strongest evidence), and what to stop building (low-evidence features that don’t show up in the data). Cohort review of each participant’s plan.

Async — before Session 8

Draft your 90-day action plan. What are the 3 highest-confidence moves from your evidence? Submit for Jake’s feedback before the final session. Session 8 opens with a review of each plan.

THE WORK

Real outcomes from the approach behind this cohort.

B2B SAAS — SALES CALL ANALYSIS

85+

distinct jobs found from 60 coded sales calls

3×

the original JTBD count the team believed they had

The team recalled ~28 jobs. Systematic coding of 60 calls found 85+. Feature priorities changed materially after the analysis.

B2B SAAS — FEATURE PRIORITY + MARKET SIZING

43%

actual frequency of top-priority feature (team assumed 88%)

$180M

market sizing corrected after real data analysis

Feature #2 had 43% actual frequency vs. 88% assumed. Market sizing was corrected by $180M after analysis of 9.4M NPI records. Both changed the roadmap.

FORMAT

Four weeks of structured evidence-building.

Duration

4 weeks

8 live sessions, 2×/week, 75 minutes each

Async work

~2 hrs/week

Coding sessions, hypothesis maps, and segment analysis applied to your own product and calls

Cohort size

12 seats max

Small enough that Jake reviews every JTBD analysis and 90-day plan personally.

Platform

Zoom + Slack

Live sessions on Zoom. Async work, JTBD coding, and cohort discussion in a private Slack channel.

Timezone

GMT-friendly

Sessions scheduled to work for EU and US East. Exact times confirmed with cohort on registration.

Recording

All sessions

Every session recorded. Access for 12 months after the cohort ends.

WHO’S TEACHING

Jake McMahon — ProductQuant

Jake McMahon

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

The sales call coding process in Week 2 came from a project where we systematically reviewed 60 recorded sales calls to find 85+ distinct jobs — three times what the team recalled. The feature priority correction in Week 4 came from finding that the team’s second-highest priority had 43% actual usage frequency against their assumed 88%.

This cohort is built to teach the process, not just show you the results. You’ll code your own calls, analyse your own product data, and build your own 90-day plan. I review every submission personally and push back when the evidence doesn’t support the hypothesis.

If you’re not willing to challenge your own assumptions, this cohort won’t be comfortable. If you are, you’ll leave with a PMF strategy grounded in evidence rather than consensus.

Questions.

Or get in touch →

What if I don't have sales call recordings? +

The coding process works with any structured customer conversations — recorded interviews, written notes from calls, or even email threads. If you have none, the async for Week 2 can be adapted to run 5–10 new customer interviews during the cohort. Get in touch before registering if you’re unsure.

Do I need product analytics set up? +

You need enough product data to look at retention by segment and feature adoption. That can be basic event tracking in PostHog, Amplitude, or Mixpanel. If your tracking is very sparse, Sessions 3–4 on product data evidence will still apply, but with fewer data points to work from.

How is this different from a standard JTBD course? +

Most JTBD frameworks stop at identifying jobs. This cohort continues to validation: comparing job frequency against product usage data, identifying which jobs your strongest-retaining segment is hiring for, and building a plan from the gap between what you believe and what the data shows.

When is the next cohort? +

Dates confirmed when enough waitlist registrations are in. Join the waitlist for first access with no obligation to enroll.

Is this on Maven? +

The cohort may be listed on Maven. Joining the waitlist here guarantees access regardless of platform and may include early-cohort pricing.