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

Your recent experiment history is reviewed in full: what was being tested, how the tests were designed, whether the results were statistically valid, and what decisions they actually informed. Most teams discover they've been running underpowered tests and calling winners too early.

Each reviewed experiment is evaluated for the most common validity failures: insufficient sample, early stopping, metric switching, novelty effect, and multiple comparison inflation. You'll know exactly which past conclusions to trust and which to revisit.

Your user cycle and seasonal patterns are documented, so you know when your experiment results are being inflated by novelty and when seasonal timing is introducing confounding effects you can't distinguish from a real treatment effect.

Your team aligns on the one metric that matters most for growth — and the handful of driver metrics that reliably move it. This ends the debate about what you're actually optimising for and gives every experiment a clear hierarchy of metrics to measure against.

Given your actual traffic and conversion rates, you'll know how long experiments need to run to reach statistical significance before you launch them — not after you've already been running for six weeks.

A written record of every validity issue found across your reviewed experiments, with each problem documented and its practical implication explained. Your team has the evidence to support changing how experiments are run going forward.

A single, written framework that defines your primary metric, guardrail metrics, and the decision rules your team follows when a test concludes. Ship, iterate, or kill — defined in advance and signed off by leadership.

Pre-configured for your actual volumes and baseline rates, so your team can calculate the correct run time for any proposed experiment without doing the statistics manually.

Three fully specced experiments, ready to run: each with a written hypothesis, control and variant definition, primary metric, guardrail metrics, minimum runtime, and decision criteria. Your team starts testing immediately rather than spending weeks in design.

Your experiment backlog is scored, structured, and sequenced into a quarterly calendar. Leadership has visibility into what's being tested and when — and your team always knows what to work on next.

A standardised format for documenting every experiment result — hypothesis, data, outcome, and decision. Over time, this becomes the institutional memory of what actually works for your product.

A reusable template for presenting experiment results to your team and leadership, structured to communicate clearly and drive a decision rather than a discussion.

The model used to prioritise your experiment backlog is documented and handed over, so your team can score new ideas using the same criteria rather than defaulting to whoever argues loudest.

A live training session covering experiment design, statistical validity, how to use the metric hierarchy, and how to run the weekly review rhythm — recorded for anyone who joins the team later.

A documented cadence for how your team runs experiments week by week: how to review active tests, how to interpret results, how to prioritise the next test, and how to keep the programme from stalling.

Worked examples that help your team apply the decision rules correctly in practice. Statistical concepts are easier to internalise through examples than through documentation alone.

A six-week window covering your first real experiments after the sprint. Two dedicated coaching sessions review live results and help your team navigate the decisions that arise as the methodology gets applied to real data.