Experiment Reporting Template Checklist
A repeatable format for writing up A/B test results so stakeholders trust the program — covering hypothesis, variants, segment cuts, recommendation, and learnings.
Experiment Reporting Template
A standard write-up format for A/B test results — hypothesis, variants, segment cuts, recommendation, and learnings.
An experiment reporting template is the fixed structure your team uses to communicate every test outcome to stakeholders, regardless of whether the variant won, lost, or came back flat. The point is consistency: the same sections, in the same order, with the same statistical guardrails noted, so a merchandiser, a CFO, and a developer can all read the same artifact and draw the right conclusion.
A good template forces discipline. It makes you write the hypothesis before you ship, declare the primary metric before you peek, and record what you learned even when the test was a draw. Over a year, the stack of reports becomes the institutional memory of your experimentation program — and the reason leadership keeps funding it.
Most experimentation programs lose credibility not because the tests are bad, but because the write-ups are inconsistent. One week the Slack message is a screenshot of a 12% lift; the next week it's a 40-slide deck; the week after that, nothing at all because the test was flat.
Stakeholders can't tell signal from noise across that format chaos. A reporting template fixes the surface so the substance — what changed, what moved, what to do next — is the only variable. It's the artifact that turns a string of tests into a program.
The losing-tests trap
If your template only gets used when a variant wins, you're not running an experimentation program — you're collecting marketing wins. Losses and flats are where the real learning lives, and they need the same structured write-up. Set the rule: every concluded test gets a report, full stop.
The seven sections every report should contain
Section 1 — Hypothesis and context. Lead with the one-sentence hypothesis in the form "We believe [change] will cause [metric] to move because [reason], based on [evidence]." Then two lines of context: what page, what audience, what the baseline conversion rate was. This frames everything that follows.
Section 2 — Variants. Show the control and each variant as a screenshot or short clip, side by side. Caption the specific change in plain language ("removed the second discount banner", "swapped trust badges for review count"). Stakeholders skim; the visual is the anchor.
Section 3 — Primary metric result with statistical context. Report the lift, the confidence interval, the sample size per variant, and the test duration. Section 4 — Secondary metrics and guardrails: AOV, revenue per visitor, return rate, page load time. Section 5 — Segment cuts: mobile vs desktop, new vs returning, paid vs organic. This is where most surprises hide.
Section 6 — Recommendation. Ship, kill, iterate, or re-run — pick one and justify it in two sentences. Section 7 — Learnings. What did this test teach you about your customer that the next test should build on? This is the section everyone wants to skip and the one that compounds across a year.
Frequently asked questions
One page or one Notion doc — roughly 400-600 words plus screenshots. If it's longer, you're either over-explaining the hypothesis or burying the recommendation. Keep the depth in linked appendices for the analysts who want the raw data.
Write for the least technical stakeholder who'll read it — usually the head of e-commerce or a brand director. CRO specialists and analysts can dig into the appendix; everyone else needs hypothesis, result, and recommendation in 90 seconds.
Yes, always. A flat result is information: it tells you the change didn't move the needle at your traffic level, which often means the hypothesis was wrong or the effect is smaller than your minimum detectable effect. Hiding flats trains the team to chase only winners.
Call it out explicitly in the recommendation section — "checkout conversion +4.2%, but return rate +1.8 points on mobile". The decision is then a business trade-off, not an analytics one. Document who made the call and why; future-you will need that context.
Device (mobile vs desktop), traffic source (paid vs organic), and visitor type (new vs returning) at minimum. For apparel and beauty stores add first-time vs repeat purchasers. The point isn't to data-mine for a winning segment — it's to surface where the variant behaved differently than the average.
Pre-declare the segments you'll cut by in the test plan, before the experiment ships. Anything beyond that list goes in an "exploratory" section clearly labelled as hypothesis-generating, not decision-grade. The template should have separate boxes for confirmatory and exploratory cuts.
One searchable place — Notion, Confluence, or a dedicated experimentation tool — with consistent tagging by page, hypothesis theme, and outcome. A folder of PDFs in someone's Drive is where institutional learning goes to die. Tagged reports become the searchable knowledge base for the next test.
Yes, but annualised and clearly caveated. Multiply the per-visitor revenue lift by your trailing 12-month visitor count, and note that real-world impact depends on the variant holding up at full traffic. Finance teams will do this math anyway — better that you frame it correctly than they back-of-envelope it.
The brief is written before the test and describes what you're going to do; the report is written after and describes what happened. Many teams keep them as two halves of the same document — the brief sections (hypothesis, variants, metrics, segments) get filled in pre-launch, the report sections (results, recommendation, learnings) get filled in post-launch.
Review it once a quarter. If three reports in a row hit the same gap — say, no one's looking at the page-speed guardrail — add a section. If a section gets skipped every time, kill it. The template should evolve with what your stakeholders actually need to make decisions.
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