I build the experimentation engine — growth model, hypothesis backlog and clean A/B tests — so you know what actually moved the metric, and can prove it to your team and your board.
Four ways to build and run experimentation. Most teams start with a readiness audit or a hypothesis sprint before committing to a full program.
A running experimentation program that ships measurable wins — properly powered, properly analysed, and documented so learning accumulates.
Map how your product actually grows — the loops, the leverage points and the North-Star — then build the engine that moves them.
Find out whether your data, tooling and traffic can support trustworthy tests — before you build a program on sand.
A focused sprint that turns scattered growth ideas into a prioritized, testable backlog with clear expected impact.
You'll know which change moved the metric and by how much — and be able to show the working to your team and investors.
A prioritized queue of hypotheses tied to your growth model, rather than a graveyard of untested opinions.
Every test, win or loss, feeds a documented library — so your tenth experiment is smarter than your first.
I go deep on your product, market and goals before touching a single event.
We set clear targets and a tight scope so we're aiming at the same thing.
I become part of the team — in your tools and chat, doing the hands-on work.
Regular insight and iteration, with everything documented so it stays yours.
Sometimes yes, sometimes no — and I'll tell you honestly which. Low-traffic teams often get more from sequential testing, qualitative research and bigger swings than from classic A/B tests. The readiness audit answers this before you invest.
First tests usually run within two to four weeks of starting, assuming tracking is in decent shape. Meaningful compounding takes a few months — experimentation is a system, not a single win.
To a degree, yes — you can't measure a test you can't track. If the foundation isn't there, we usually fix the essentials first, which is where the Product Analytics track comes in.
Depends on your stack and stage. Statsig and GrowthBook suit most early-stage teams well; if you're already on Amplitude or PostHog, their built-in experimentation may be enough to start.
Either. I can run the program hands-on, or set up the system and coach your team to run it themselves. Most clients start with the former and move toward the latter.
Book a 20-minute intro call. Tell me what you're trying to move and I'll tell you honestly whether experimentation is the right lever.
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