EXPERIMENT VELOCITY — $4,997 · 3-WEEK SPRINT

Jake McMahon — ProductQuant

8+ years B2B SaaS · Behavioural Psychology + Big Data (Masters)

Every experiment your team runs ends with a clear decision. Ship it, kill it, or re-run — no more “directionally positive.”

A 3-week sprint that fixes your experiment engine — the first 3 tests designed to produce clear ship-or-kill decisions, and the operating rhythm to sustain 10–20 tests per year.

Start the Sprint → See what’s included ↓

3 experiments designed to produce clear ship-or-kill decisions — or full refund · 3-week delivery

WHAT YOU HAVE AT THE END

Experiment audit Every structural problem in your current setup identified and documented

Metric hierarchy One North Star per test, guardrails defined, decision rules agreed

Sample size calculator Pre-configured for your traffic volume and baseline rates

3 test designs Hypothesis, metric, and sample size locked before launch

90-min team session Framework walked through, questions answered, team runs it independently

$4,997 · fixed price · 3-week sprint

YOU ALREADY KNOW THE EXPERIMENT PROGRAM ISN’T PRODUCING ANSWERS

Tests that never reach significance

“We ran 8 tests last year. Got one clear winner. Four ‘directionally positive’ results we shipped anyway. And three that needed more data we never got.”

VP Product — B2B SaaS, $8M ARR

Every readout becomes a negotiation, not a decision

“Product wants to ship. Data science says it didn’t reach significance. Growth says the trend is clear enough. The variant gets shipped based on who had the most conviction in the room, not the data.”

Head of Growth — Series B

Winners that don’t move revenue

“We ship test winners regularly. I couldn’t tell you which ones actually moved the needle on revenue versus just moving a dashboard number.”

CPO — B2B SaaS

Only 2–3 tests per quarter — the program never compounds

“We want to run 20 tests a year. We’re running 8. The gap isn’t ideas — it’s how long it takes to turn an idea into something engineering will actually build.”

Product Manager — Series A

WHAT THIS TYPICALLY UNCOVERS

The infrastructure is generating noise — not the ideas.

Most inconclusive results come from underpowered samples, not weak ideas.

The test ran for two weeks because that’s when the sprint ended. But the sample needed four weeks to reach significance. The “directionally positive” result wasn’t a signal — it was noise the team shipped anyway.

Primary metrics chosen after the test starts produce ambiguous readouts.

When the metric isn’t agreed before launch, the readout becomes a debate about which number to look at. Decision rules defined in advance turn every readout into a clear ship-or-kill call.

Winners that don’t move revenue usually optimised a disconnected leading indicator.

The test showed a lift in the primary metric. Revenue didn’t move. The metric didn’t connect to what actually matters — or a guardrail metric was degraded and nobody caught it because guardrails weren’t defined.

The bottleneck is design-to-launch time, not a shortage of test ideas.

Each test requires a two-week design phase before engineering will scope it. At 2–3 tests per quarter, the program never builds compounding insight. A repeatable design framework cuts that cycle to days.

WHY THIS IS DIFFERENT

Most experiment consulting ends with a methodology document. This one ends with tests already designed and a team that knows how to run the next one.

A typical engagement produces a hypothesis template, a sample size spreadsheet, and a session on statistical significance. Your team reads it, agrees it makes sense, and the next test is designed the same way the last one was — because the document didn’t fix the infrastructure problem.

This sprint works differently. The audit identifies the specific structural problems in your current setup — underpowered samples, late metric selection, no guardrail definitions — before the framework is built. The framework is then built around your actual traffic volume and product cycle. And the first 3 tests are designed through that framework — so the team learns by doing, not by reading a document they’ll never open again.

The goal is not a methodology. The goal is every experiment your team runs producing a clear ship-or-kill decision, so product improvements compound instead of stalling on inconclusive results.

TIMELINE

Three weeks. Audit, rebuild, and three tests running — each designed to produce a clear ship-or-kill decision.

WEEK 1

Audit + Framework

Review past tests: design, sample sizes, metric selection, result calls. Structural problems identified. Sample size calculator built for your traffic. Metric hierarchy and decision rules agreed with your PM and growth lead.

→

WEEK 2

Design + Prepare

First 3 experiments designed through the framework. Hypothesis documented, primary metric agreed, sample size calculated. Each test scoped for engineering with variant design reviewed against the hypothesis.

→

WEEK 3

Handover + Launch

90-minute team session. Framework walked through. Decision rules practised. Experiment backlog structured and scored. Your team runs the program from day 22 without needing a consultant for every test.

Week 4: your team launches the first test with confidence it will produce a clear answer.

WHAT YOU GET

Six deliverables. The audit, the framework, the first three tests, and the handover — so your team runs the program from day 22.

Week 1 · Audit

Experiment Audit Report

Every structural problem in your current experiment setup identified and documented. Most programs have 3–4 structural problems producing most of the inconclusive results. This finds them before the framework is built — so the framework fixes your actual problems, not hypothetical ones.

Review of past tests: design, sample size, metric selection, result calls
Statistical validity of past results — what actually passed and what was noise
Novelty effect and seasonality risks flagged per test window
The specific problems to fix before the next test runs

Week 1 · Framework

Metric Hierarchy + Decision Rules

One primary metric per test, agreed before it starts. Guardrail metrics documented so a test that lifts the primary metric but degrades retention or revenue gets caught. Decision rules that turn every readout into a clear ship-or-kill call — not a negotiation.

North Star metric defined and tied to revenue impact
Guardrail metrics for each experiment area: activation, retention, pricing
Decision rules: when a result is a winner, when it is a null, when to re-run
Readout template: how to communicate a result so it gets acted on

Week 1 · Infrastructure

Sample Size Calculator

Pre-configured for your actual traffic volume and baseline conversion rates. Answers the question “how long does this test need to run?” before it launches — so the team can decide whether to run it now or wait for more traffic. No more ending tests early because the sprint is over.

Calculator configured for your baseline rates and traffic volume
Runtime estimate per test area: onboarding vs. pricing vs. feature adoption
Minimum detectable effect guide: what lift is worth running a test to detect
Traffic split logic for multi-variant tests and holdout groups

Week 2 · Execution

First 3 Experiment Designs

Three experiments designed to produce clear ship-or-kill decisions. Each one built through the framework your team will use going forward — so they learn the process by doing it, not by reading documentation.

Hypothesis, primary metric, and success criteria documented per test
Sample size and minimum runtime calculated before each test is scoped
Variant design reviewed — tests what the hypothesis says it tests
Engineering spec: what needs to be built and what measurement requires

Week 2–3 · Pipeline

Experiment Backlog + Quarterly Calendar

A structured experiment backlog with priority scoring, a result archive so learnings compound, and a quarterly calendar template so the team knows what to run next and in what order. Your PM maintains this independently after the sprint.

Hypothesis backlog with priority scoring: learning value, traffic needed, dependencies
Test tracker: live tests, results, decisions, and what to run next
Quarterly calendar: how many tests to schedule per quarter and when
Result archive: every test result documented so learnings compound, not repeat

Week 3 · Handover

90-Minute Team Session

A working session with your PM, growth lead, and anyone who calls test results. The framework walked through with real examples from your audit. Decision rules practised. Your team leaves knowing how to design, run, and call experiments without starting from scratch every time.

How to take an idea and design a test that produces a clear answer
Decision rules practised on real past test results
How to score and sequence the backlog without another design phase
The weekly operating rhythm: readouts, backlog additions, next test prep

On the cost of inconclusive results: a sprint to design and run a test costs 2–4 weeks of PM and engineering time. If the result is inconclusive, that time produced no answer and no learning. The backlog idea goes back to the queue. At 2–3 tests per quarter with inconclusive results on most, the program produces activity but not compounding improvement. The sprint fixes the infrastructure so every test produces a usable answer.

FIT CHECK

The experiment program runs. The results don’t produce decisions.

GOOD FIT

B2B SaaS running experiments but getting inconclusive results or slow cadence

A/B testing or feature flags in place · fewer than 10 clear results per year

The situation

You’re already running A/B tests or feature flag experiments, but results regularly come back “directionally positive” or “needs more data.” The team disagrees about whether a result is real. Winners get shipped that don’t move revenue. You want to run 10–20 tests per year but the design-to-launch cycle is too slow.

What you leave with

An experiment engine that produces clear ship-or-kill decisions on every test
The first 3 tests designed correctly and ready for engineering
A repeatable framework your team runs independently — no ongoing dependency

Product improvements compound quarter over quarter instead of stalling on ambiguous readouts.

NOT A FIT

Pre-experiment, no testing tool, or happy with current cadence

Wrong stage or wrong problem

When this sprint doesn’t apply

If you’ve never run an experiment before, the starting point is a launch program — not a velocity sprint. If you don’t have an A/B testing or feature flag tool in place, there’s no infrastructure to fix. And if you’re happy with 2–3 tests per quarter and the results are clear, you don’t need this.

Better starting points

Experiment Launch

Start an experiment program from scratch with the right infrastructure.

Activation Deep Dive

Find where users drop off and rank the top fixes by impact.

The Foundation

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

What this sprint doesn’t cover

The Experiment Velocity Sprint delivers the framework, the test designs, and the team capability. Your team does the building and running. If you need the full picture — including implementation and ongoing experiment management — that’s a different engagement.

Building the test variants — your engineering team implements the designs
Running the tests post-launch — the sprint hands over the framework and the team runs it
Ongoing experiment management — the system is designed so you don’t need a consultant for every test

For full implementation → Growth LAB

Jake McMahon — ProductQuant

Jake McMahon

8+ years building retention, activation, and growth programs inside B2B SaaS · Behavioural Psychology + Big Data (Masters)

I run this sprint myself — the setup audit, the framework design, the test designs, the team session. The pattern I see repeatedly is teams with good ideas producing inconclusive results because the infrastructure is generating noise. The problem is almost never the ideas.

The output is a system your team runs without me. A framework your PM can maintain and a prioritised backlog your team can execute is more valuable than a consultant who designs every test. The sprint transfers the capability, not just three test designs.

I won’t do this:

Call a “directionally positive” result a winner — it means the test was underpowered
Design tests without agreed primary metrics locked before they run
Build a backlog of ideas without hypothesis format and sample size requirements
Hand over a framework without briefing the team who will use it

What testing tools does this require?

The framework is tool-agnostic. It works with Optimizely, LaunchDarkly, VWO, or a simple feature flag setup. The framework defines how tests are designed and called — the tool handles the split and the data collection. If you don’t have an A/B testing tool, we scope the minimum setup needed as part of the audit.

Teams Jake has worked with

PRICING

One payment. Everything you need to run your first three experiments correctly.

$4,997

one-time · fixed price

3-week sprint

Experiment audit — every structural problem identified and documented
Metric hierarchy with North Star, guardrails, and decision rules per test
Sample size calculator configured to your traffic and baseline rates
3 experiments designed to produce clear ship-or-kill decisions
Experiment backlog structured, scored, and ready for your PM to maintain
Quarterly calendar template based on your actual traffic volume
90-minute team session — framework walked through, questions answered
Everything stays with your team permanently

Tool-agnostic. Works with any A/B testing or feature flag setup.

Book a 30-minute call →

3 experiment designs ready to produce clear ship-or-kill decisions — or full refund. If the audit reveals your setup can’t support valid experiments yet, we tell you in week 1 and scope what’s needed first. The deliverable either exists or it doesn’t.

Questions.

Or book a call →

We already run A/B tests — why do we need this? +

Running tests and running tests that produce clear answers are different things. If your results regularly come back “directionally positive,” “needs more data,” or “not significant but trending,” the setup is generating noise. The sprint audits the specific structural problems in your current setup and fixes them before the next test runs. If your tests already produce clear winners consistently, this sprint is not for you.

What does “directionally positive” actually mean? +

It means the test was underpowered. The result moved in the right direction but didn’t reach statistical significance — which means you can’t tell whether the lift is real or noise. Teams ship anyway because the test is over and the sprint slot is needed for the next thing. The framework fixes this by calculating the required sample size before the test runs, so you know whether the test can produce a clear answer given your current traffic — before you design and build it.

What’s an inconclusive result costing us? +

A sprint to design and run a test typically costs 2–4 weeks of PM and engineering time. If the result is inconclusive, that time produced no answer and no learning. The backlog idea goes back to the queue. The team debates it again next quarter. At 1–2 tests per quarter with inconclusive results on most, the program is producing activity but not compounding insight.

Do you run the tests or teach us to run them? +

Both. The first 3 tests are designed together with your PM and growth lead, so they learn the framework by doing it rather than reading a document. The backlog is built with your team. The team session covers the operating rhythm. After the sprint, your team runs the program — the system is designed so they don’t need me to be in the loop for every test.

What if we don’t have an A/B testing tool? +

If you don’t have a testing tool, we scope the minimum setup needed as part of the Week 1 audit. Most teams can run statistically valid tests with their existing feature flag setup or a lightweight tool. If a new tool is needed, we document exactly what to look for and what the configuration requires — so the tool decision is informed by what the framework needs, not the other way around.

What’s the guarantee? +

If the sprint doesn’t produce 3 experiment designs ready to run with correct metrics and sample sizes, you get a full refund. If the audit reveals your setup can’t support valid experiments yet, we tell you that in week 1 and scope what’s needed first. We don’t reach day 21 and deliver something that doesn’t meet the brief.

How do you get access to our data? +

Read-only access to your analytics and A/B testing tools. No write access is needed, and access can be revoked at any time. Most teams share access via a guest login or read-only API key. The data stays in your systems throughout. We review past test results, traffic volumes, and baseline conversion rates — nothing leaves your infrastructure.