A working experiment program — one test live, the infrastructure built, and a backlog of 20 more scored and ready.

Your team leaves with a shared, agreed definition of the one metric that matters most for growth and the handful of driver metrics that reliably move it — ending the internal debate about what you're actually optimising for.

Every critical measurement gap in your current analytics setup is identified and documented, so you stop making product decisions on incomplete or misleading data.

Between 20 and 30 structured experiment hypotheses, each tied to a real metric and a clear business rationale — enough to keep your team running meaningful tests for months without starting from a blank page.

Before your first experiment runs, you know exactly how long tests need to run to reach statistical significance — so you stop calling tests early and making decisions on noise.

An honest picture of where your team is ready to run experiments independently and where they need support, so the program is scoped to what your organisation can actually sustain.

A single, written document that defines your North Star metric, the driver metrics your team can directly influence, and the guardrail metrics that protect against unintended harm — used in every review and prioritisation conversation going forward.

A detailed record of what your analytics stack currently tracks, what it's missing, and what needs to be fixed before experiments can produce reliable results.

Typically between 5 and 7 fully specced experiments, each with a hypothesis, control and variant definition, success metric, and minimum runtime — ready to hand to your team and start immediately.

A complete experiment management system set up in Notion with your first experiments already loaded, so your team has one place to plan, run, and review every test from day one.

Your full backlog of ideas is scored by expected impact, confidence, and ease of implementation — so your team always knows what to work on next without spending hours debating prioritisation.

A planned sequence of experiments across the next six months, aligned to your growth priorities — giving leadership visibility into what's being tested and why, without needing to be involved in every decision.

Every experiment your team runs is captured in a structured log with hypothesis, results, and decision — building institutional knowledge that doesn't disappear when team members leave.

A repeatable agenda that keeps your weekly experiment review focused, time-efficient, and decision-oriented rather than turning into an open-ended status meeting.

A structured training session on experiment design, statistical validity, and how to use the Notion OS — recorded so new team members can get up to speed without a separate onboarding process.

The scoring model used to rank your hypothesis backlog is fully documented so your team can apply it independently as new ideas are generated over time.

A written guide covering how to keep the program running after the engagement ends — including how to add new hypotheses, how to handle inconclusive tests, and when to revisit the metrics hierarchy.

For the first two months after delivery, direct access to get answers, review experiment designs before they go live, and troubleshoot anything that comes up as your team builds the habit.

Three structured sessions where your experiment lead or growth team can work through specific challenges, review live results, and get feedback on upcoming test designs. Email access for questions between sessions.