A/B Testing & Experimentation
A real experimentation program — prioritized backlog, statistical rigor, and a shipping cadence.
Most A/B testing programs fail because they run one test at a time, with no prioritization and no statistical discipline. We build experimentation as a function, not a one-off.
Tooling-agnostic — we work in VWO, Optimizely, Statsig, GrowthBook, or your in-house platform.
The problem we solve
Why teams come to us for a/b testing & experimentation
Most experimentation programs run one test at a time, call winners off underpowered samples, and forget what was learned a quarter later. Leadership stops trusting the numbers, and the program quietly dies.
The problem isn't the tool — it's the operating model around it.
What changes
What ThickLabel changes
- A prioritized backlog with ICE or PIE scoring, not whoever talked loudest in standup.
- Pre-registered hypotheses and sample-size math so wins aren't noise.
- A searchable learnings repository — every test, every result, every reason.
- A weekly cadence that ships tests instead of debating them.
Deliverables
What's included
- Experimentation framework and operating model
- Hypothesis library and prioritization (ICE/PIE)
- Test design and pre-registration
- Statistical analysis and learnings repository
- Weekly readouts and quarterly reviews
- Team enablement and program governance
Process
How the engagement runs
- 01
Program audit
Review existing tooling, prior tests, and how decisions get made. Identify where statistical or operational rigor is missing.
- 02
Operating model and backlog
Set up the hypothesis library, prioritization model, test-design template, and review cadence.
- 03
Run and ship
Run tests with proper power calculations and guardrail metrics. Ship winners, document losers, refresh backlog weekly.
- 04
Learning system
Build the repository, monthly readouts, and quarterly review so insights compound across teams instead of evaporating.
FAQ
Common questions
Which platforms do you work in?
VWO, Optimizely, Statsig, GrowthBook, LaunchDarkly, and in-house systems. Tool-agnostic by design.
Do we need huge traffic to run experiments?
No — but program design changes. Low-traffic environments need different prioritization, sequential testing, and qualitative pairing. We adjust accordingly.
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Learn more →Want a read on your experimentation program?
Share your last 10 tests and the decisions they drove. We'll come back with where statistical or operational rigor would change the answer.