Experiment Backlog Template Checklist
A structured backlog template for CRO teams to capture hypotheses, score them with ICE or RICE, and track every test from idea to result — without rebuilding the spreadsheet every quarter.
Experiment Backlog Template
A structured tracker — usually a spreadsheet — for logging CRO test ideas, scoring them, and recording results in one place.
An experiment backlog template is the single source of truth for every test idea your CRO program touches. Each row is a hypothesis: what you're changing, where, why you think it'll move the metric, how confident you are, and — once it's run — what actually happened.
The value isn't the spreadsheet itself. It's the discipline. A good backlog forces you to write a real hypothesis before booking dev time, score ideas against each other so the loudest voice doesn't win, and keep a permanent record of what's been tested so you don't re-run the same checkout button experiment six months later.
Most CRO programs start with a Trello board, outgrow it in a quarter, migrate to a custom Notion database, outgrow that, and eventually settle on a spreadsheet with twelve columns and conditional formatting. You can skip the three years of rebuilding.
The template below captures what working backlogs actually contain — not what theoretical frameworks suggest. It's deliberately spreadsheet-shaped because the people who maintain backlogs (CRO specialists, growth PMs, agency leads) live in spreadsheets and need to sort, filter, and pivot without a license seat conversation.
The two failure modes of every backlog
Backlogs die in two ways. First: nobody scores ideas, so prioritisation becomes a vibes meeting and the HiPPO wins. Second: nobody updates results, so the backlog becomes a graveyard of ideas with no learning attached. The template solves both — but only if you enforce the score column on intake and the result column on close-out. Pick a weekly ritual (15 minutes, Monday) to maintain it, or it will rot.
What goes in each column
Identity columns come first: a short ID (EXP-074), a one-line title, the surface being tested (PDP, cart drawer, checkout step 2), and the primary KPI (conversion rate, AOV, add-to-cart rate). Keep titles to under 80 characters — if you can't summarise the test in one line, the hypothesis isn't sharp enough yet.
The hypothesis column is the heart of the backlog. Use the format: "Because we observed [evidence], we believe that [change] will cause [outcome], measured by [metric]." The evidence half is what stops you running tests on hunches — if you can't cite a session recording, a funnel drop, or a survey response, the idea isn't ready to score yet.
Scoring columns are where prioritisation happens. ICE (Impact, Confidence, Ease, each 1-10) is the fastest framework and works for backlogs under 50 ideas. RICE (Reach × Impact × Confidence / Effort) is better once you're juggling tests across multiple surfaces or audience segments because Reach forces you to think about traffic volume — a brilliant test on a page 2% of visitors see is a worse bet than a mediocre test on the PDP.
Execution columns track the work: status (Backlog → Designed → Built → Running → Analysed → Archived), owner, start date, planned duration, and dev effort estimate. Add a "blocked by" column if you regularly wait on creative or engineering — it makes the bottleneck visible in your weekly review without anyone having to flag it.
Results columns close the loop: outcome (winner / loser / inconclusive), lift percentage, statistical confidence, sample size, and a one-paragraph learning. The learning column is the most-skipped and the highest-value field in the whole template — six months in, your backlog becomes a searchable institutional memory of what works on your store.
Frequently asked questions
Use ICE when your backlog has fewer than 50 ideas and most tests run on the same high-traffic pages. Switch to RICE once you're testing across surfaces with very different traffic levels — Reach prevents you from over-investing in low-traffic page experiments. Many teams run ICE for intake and re-score the top 20 with RICE before quarterly planning.
Healthy backlogs sit at 30-80 active ideas. Below 30, you're not generating enough hypotheses and will eventually run dry. Above 100, scoring becomes noise and ideas go stale — archive anything older than six months that hasn't been touched, or move it to a "someday" tab.
One person, always. Usually the CRO specialist or growth lead. The backlog can take contributions from anyone (designers, support, paid media managers often have the best hypotheses), but a single owner enforces formatting, runs the weekly review, and decides what gets scored next. Distributed ownership produces inconsistent rows and dead backlogs.
For programs under €10M revenue or fewer than 100 tests a year, yes. Spreadsheets sort, filter, pivot, and export with zero training. Dedicated backlog tools start paying off when you're running 4+ tests in parallel across multiple brands or need approval workflows. Until then, the spreadsheet is faster.
Anchor it to evidence. "Because session recordings show 40% of mobile users abandon at the shipping step, we believe surfacing the free-shipping threshold earlier will reduce abandonment, measured by checkout completion rate." Skip the evidence half and you're guessing. The hypothesis column doubles as a filter — if a row can't pass this format, it's not ready to score.
Six states cover most workflows: Backlog (idea logged, not scored), Prioritised (scored, awaiting design), In Build (design or dev underway), Running (live test), Analysed (results recorded), Archived (closed). Avoid more than seven — every extra status creates ambiguity about where a test actually is.
Run a 15-minute weekly review where you (1) score new intake, (2) update statuses on running tests, and (3) write learnings for anything finished that week. Also archive anything that's been in Backlog for over 90 days without being scored — if it hasn't earned attention by then, it won't.
Yes, permanently, in the Archived state with a clear result and learning written up. Losers are the most valuable rows you have — they stop your future self and any new hire from re-running the same idea. Searchable losers is half the reason the backlog exists.
The backlog is the planning layer; your testing tool runs the experiments and GA4 (or your analytics) confirms the lift. Link to the test in your A/B platform from the backlog row, and paste the final lift number plus confidence into the results columns when the test ends. The backlog is the only place where pre-test hypothesis and post-test learning live together.
Yes — add a column for "audience segment" and treat each personalisation variant as a row. The hypothesis format still works: "Because [segment] shows [behaviour], we believe [tailored experience] will lift [metric]." Just be aware that personalisation rarely reaches statistical significance per segment, so your confidence column will lean on directional evidence more than p-values.
Test ideas before you ship them
Run unlimited A/B tests, attach hypotheses to outcomes, and build a searchable archive of what works — and what doesn't.