You shipped 12 features last quarter. Can you prove any of them moved retention, activation, or expansion?

80% of software features are rarely or never used. SaaS companies spend $29.5 billion annually on unused features. Your sprint velocity is high. Your impact is unmeasured. Every release is a $42K bet on a hunch.

First definitive experiment in 8 weeks · Money-back guarantee

Start Measuring Impact — $15,000-$25,000 → Watch the breakdown ↓

For B2B SaaS at $3M-$80M ARR

THE 3-MINUTE BREAKDOWN

Why most SaaS experiments fail — and how to get definitive results in 8 weeks.

Your team runs 0-2 experiments per quarter. Half are inconclusive.

Two 'winners' shipped to production reverted within 6 weeks — false positives from underpowered tests presented as significant.

Success criteria get changed after launch based on which metric trends better.

Sample sizes never calculated. 7 of 9 experiments at one company produced zero learnings in 6 months. That's months of engineering with no signal.

The alternative: experiments grounded in behavioral data.

Pre-registered hypotheses. Power analysis. Sample sizes calculated. Success criteria locked before launch. Results that are definitive. Each builds on the last.

THE EXPERIMENT TRAP

Five signs your product team is shipping in the dark.

Sprint velocity is your success metric.

Equating shipping on time with success. A well-timed release that doesn't contribute to growth is still a failure. But nobody measures the difference.

Your experiments start from opinions, not data.

'Let's try making the CTA button bigger.' No behavioral data informing the hypothesis. No connection to the metrics that matter.

Your 'winners' reverted.

Two experiments shipped. 6 weeks later, metric reverted. Tests were underpowered — p=0.08 presented as significant. Product decisions based on noise.

Success criteria shift after launch.

The metric changed based on which was trending better. If you can't define winning before the experiment, the result is meaningless.

There's no experiment repository.

Every experiment starts from scratch. No institutional learning. Same hypothesis tested in different forms every quarter because nobody documented what failed.

What unmeasured shipping actually costs.

$29.5B

spent annually on unused features across SaaS

At $42K per feature, even a small team ships $500K+/year of unmeasured bets.

7 of 9

experiments produced zero learnings in 6 months

Not bad results — no results. Months of engineering time without a single definitive signal.

$2.5M

annual revenue sitting behind 3 measurement blind spots

Experiments hadn't targeted them because analytics couldn't see them.

THE SHIFT

From shipping in the dark to definitive results.

	TODAY	AT WEEK 8
Experiment velocity	0-2 per quarter, mostly inconclusive	3-6 running, each with definitive results
Hypothesis source	Opinions from brainstorm	Behavioral data — segments, actions, metrics
Sample sizing	'Run it for 2 weeks'	Power analysis. MDE calculated. Duration by math.
Success criteria	Shifts after launch	Pre-registered. Locked. No goalposts moved.
Feature impact	Ship and hope	Every release measured against retention, activation, expansion
Experiment memory	Starts from scratch	Full library. Each builds on the last.

THE PROCESS

Analytics audit by Week 2. First experiment by Week 4. First definitive result by Week 8.

WEEKS 1-2 · MEASUREMENT FOUNDATION

Analytics audit: which metrics matter, which events are broken, which critical actions have zero tracking. Gap analysis sized by revenue impact. You can't experiment on what you can't measure.

Analytics Audit + Gap Analysis

WEEKS 3-6 · EXPERIMENT ENGINE SETUP

First 3-5 experiments designed: hypothesis grounded in data, primary metric defined, success criteria locked, sample size calculated. Statistical framework documented. Your team gets the methodology.

Experiment Engine (methodology)3-5 Designed Experiments

WEEKS 7-8 · FIRST RESULTS

First experiments reach significance. Definitive results. Each feeds the next hypothesis. Experiment library started. Your team runs the engine independently.

Experiment Library Template60-min Handoff + Docs

WHY OUR EXPERIMENTS PRODUCE RESULTS

The experiment isn't the hard part. The infrastructure is.

Most teams fail at experimentation because the infrastructure doesn't exist — not because the ideas are bad. Events are broken. Metrics aren't defined. Sample sizes are never calculated. We build the measurement foundation first, then design experiments grounded in the data that foundation surfaces.

Every experiment connects to the analytics layer. Hypotheses come from behavioral patterns, not brainstorm sessions. Success criteria are locked before the first user enters the test. One e-commerce SaaS had 3 measurement blind spots hiding millions in annual revenue — experiments hadn't targeted them because analytics couldn't see them.

One healthcare SaaS ran continuous experiments over 6 months, each building on the last. No inconclusive results. No moved goalposts. Each experiment produced a definitive signal that informed the next.

10-18

experiments in 6 months

measurement blind spots hiding revenue at one client

goalpost moves

THE WORK

What happened when experiments started producing definitive results.

E-COMMERCE SAAS

$2.5M+

annual opportunity identified

40+

missing events discovered

Activation funnel had zero coverage below step 3. Highest-value feature had zero tracking. Revenue opportunity was invisible — experiments couldn't target blind spots analytics couldn't see.

HEALTHCARE SAAS

10-18

experiments completed in 6 months

90%

analytics cost reduction

Each experiment produced a definitive result. Each informed the next hypothesis. No inconclusive tests. No moved goalposts. Compounding knowledge — not isolated bets.

If the first round doesn't produce at least one definitive, actionable result, we refund the engagement.

Pre-registered hypotheses. Power analysis. Locked criteria. If the methodology still doesn't produce at least one definitive result — full refund, no questions.

Start Measuring Impact — $15,000 →

Questions.

Or book a call →

We already run experiments. Why do we need this?+

If more than 30% of your experiments are inconclusive, infrastructure is the bottleneck — not ideas. Underpowered tests, missing events, undefined metrics. We fix the foundation so every experiment produces a definitive signal.

What if our analytics are too broken?+

That's where we start. Weeks 1-2 are the measurement foundation — analytics audit, gap analysis, fixing what's broken. You can't experiment on what you can't measure. The worse the setup, the bigger the opportunity.

How is this different from an experimentation agency?+

Agencies run experiments for you. We install the engine and your team runs it. No dependency. No monthly retainer for access to your own methodology. After the engagement, you own everything.

What's the investment?+

$15K-$25K Foundation engagement (6 weeks) includes the analytics audit, experiment engine, and first 3-5 designed experiments. Growth LAB ($6,997/mo) continues with 3-6 experiments running monthly — each building on the last.

Can we just hire a growth PM?+

You can. Most spend their first 6 months building what the Foundation engagement delivers in 6 weeks — the measurement layer, the statistical framework, the experiment methodology. Then they still need the behavioral data infrastructure to run experiments that produce definitive results.