Template

Experiments Backlog & Prioritization Template

An interactive template for capturing experiment ideas, hypotheses, metrics, owner, status, and a simple prioritization rubric. Includes guidance, a filled example, fields for impact/effort scoring, and metadata to make entries searchable and evidence-ready.

Interactive Tool

Experiments Backlog Entry

Turn ideas into a prioritized, actionable experiment pipeline

Use this form to record experiment basics, core measures, expected impact, and ownership so your team can prioritize, start, and learn from experiments without losing context.

Filled example

FieldExample
Experiment nameHomepage CTA wording A/B test
HypothesisChanging CTA from 'Start free' to 'Get my guide' will increase signups from organic visitors by making benefit clearer.
Primary metricSignup rate (percent of visitors who register)
Baseline → TargetBaseline 2.1% → Target 3.0%
Impact (1-5)4
Effort (1-5)1
PriorityHigh (impact high, effort low)
StatusBacklog → Ready
OwnerPriya K.
Learning summary (after run)CTA 'Get my guide' improved signups by 0.9pp. Users cited clearer value in session recordings.

Quick prioritization rubric: Use the impact and effort scales below to choose a priority. Consider computing a simple ratio (impact ÷ effort). Example heuristics: ratio ≥ 2 -> High, 1 ≤ ratio < 2 -> Medium, ratio < 1 -> Low. Also weigh risk, strategic fit, and dependencies when finalizing.

A short, descriptive title (one line).
One-paragraph summary of what you will try and why.
If we [action], then [measurable outcome] because [reason]. Be explicit about cause and measurement.
How success will be measured (metric name and unit). Example: 'Signup rate (% of visitors)'.
Current baseline value and time window (e.g., '2.1% last 30 days').
The target value you hope to reach (clear and testable).
Other metrics to monitor so a win on the primary metric doesn't cause harm elsewhere (e.g., retention, revenue per user, error rate).
Estimate the likely impact on the primary metric. 1 = negligible, 5 = transformational.
Estimate effort including design, implementation, QA, and analysis. 1 = small (~a day), 5 = large (multi-sprint).
Use the impact and effort fields to assign priority. Simple rubric: (impact ÷ effort) >= 2 => High; 1 <= ratio < 2 => Medium; ratio < 1 => Low. Adjust for strategy, risk, and dependencies.
Person accountable for running the experiment and reporting the learning.
Current lifecycle state of the experiment.
Use ISO format YYYY-MM-DD or leave blank until scheduled.
Planned end or analysis date (YYYY-MM-DD).
Reference the decision this experiment informs (e.g., 'Q3 pricing decision') or a parent project.
Assigned after the experiment completes to indicate reliability of results.
What happened, why you think it happened, evidence quality, and recommended next steps. Keep it concise and link to full analysis if available.
Comma-separated tags to help discovery (e.g., onboarding, pricing, performance).
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