Brief and access
You share the hypothesis, screenshots of each variant, traffic split, assignment method, and a daily export of primary and guardrail metrics. We confirm whether the test window matches a full usage cycle for your app.
Method
A repeatable path from messy experiment exports to a decision your product team can defend.
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How an Experiment Performance Review moves
We treat A/B testing performance analytics as a craft of judgment under uncertainty — not a scoreboard to rubber-stamp.
Design integrity check
We look for leakage between arms, uneven exposure by platform, early stopping, and metrics that do not match the decision you intend to make. Problems here often explain “surprising” lifts before any fancy math begins.
Signal versus noise
Primary lift is read alongside sample quality and guardrails. Segment cuts are treated as context unless they were planned. Conflicting stories are written in plain language so stakeholders stop arguing past each other.
Decision-ready readout
You receive a written recommendation — ship, iterate, or keep control — with the practical costs of each path. A short live walkthrough follows so product, growth, and engineering hear the same verdict.
What we need from you
- Hypothesis written before launch (or an honest reconstruction)
- Variant descriptions or screenshots
- Assignment and exposure notes
- Daily metric export for the test window
- Names of people who will decide the outcome
What you receive
- Integrity findings in everyday language
- Primary and guardrail interpretation
- Explicit recommendation with rationale
- Follow-up Q&A for stakeholders
Ready to walk a test through this method?
Start with an Experiment Performance Review, or book a Test Design Consultation if you are still shaping variants.
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