A/B Test Design, Build & Experiment Roadmap
Sizes A/B tests to your actual traffic, specifies assignment and exposure logging, and ranks a test roadmap you can actually run.
Before & after
Same prompt, same agent — the only difference is the skill.
A/B testing signup flow is smart. Before running a test, I'd want to understand your current conversion rate and sample size to make sure the experiment is statistically valid. What specific element are you testing—button color, copy, form fields, or something else? What's your traffic volume per day? Do you have a hypothesis about what would improve conversions? How long are you willing to run the test? Statistical power matters here, and I can help you calculate sample size, but I need more context about your baseline metrics.
A/B TEST PLAN: SIGNUP CONVERSION OPTIMIZATION HYPOTHESIS If we shorten the signup form from 6 fields to 3 (email, password, name only), then conversion rate will improve from 4.2% to 5.8% (+38% lift) because users abandon long forms. TEST SETUP Control → Current signup (6 fields: email, password, name, company, role, phone) Variant → Shortened signup (3 fields: email, password, name) Secondary variant → (Skip: company role ask until post-signup) STATISTICAL REQUIREMENTS Current conversion → 4.2% (baseline) Desired lift → +1.6pp (38% relative lift) Daily traffic to signup → 2400 users Statistical power → 90% (industry standard) Significance level → 0.05 (95% confidence) Sample size needed per variant → 4,200 conversions total Estimated test duration → 21 days (at current traffic) METRIC DEFINITIONS Primary metric → Signup completion rate (submitted form / visitor) Secondary metrics: • Form abandonment rate (started but didn't submit) • Time to complete (seconds from form load to submit) • Email verification rate (clicked confirmation link) • Signup-to-first-login rate (onboarding completion) SUCCESS CRITERIA Win threshold → Variant exceeds control by 1.6pp with p < 0.05 Stall threshold → No significant difference after 21 days Loss threshold → Variant drops below 3.5% (stop test immediately) RUN SCHEDULE Start → Monday (avoid weekend noise) Duration → 3 weeks (21 days) Daily check-in → Monitor for bugs, traffic anomalies Mid-test analysis → Day 10 (readout only, don't act) Final analysis → Day 21 (decision point) IMPLEMENTATION CHECKLIST Code → Variant form deployed to production, 50/50 traffic split Tracking → Events fired: form_started, field_completed, form_submitted QA → Test both paths before launch, verify tracking fires Notification → Slack alert if conversion drops 10% in variant RISK MITIGATION Monitor email delivery → Verification emails sent for both variants Load testing → Variant handles 1200 req/sec (peak load) Rollback plan → Kill variant within 5 min if >20% failure rate detected POST-TEST DECISIONS If variant wins → Deploy to 100%, estimate annual impact ($X revenue) If tie → Run secondary test on field order or copy variation If variant loses → Document learnings, try different hypothesis next month ILLUSTRATIVE NUMBERS: Not investment or business advice.
About this skill
Your test has run for five weeks and still isn't significant. Your backlog has twelve ideas for a page that can only power three tests a quarter. You're not sure the split is even working. This skill works out the numbers before anything launches, specifies how to build the test so the results can be trusted, and fits your roadmap to the traffic you actually have.
What it does
- Calculates sample size and duration in full weeks from your baseline, the smallest lift worth shipping, and the weekly traffic that actually reaches the page, with the math shown
- Says plainly when a test can't be powered, then offers options: a higher-funnel metric, a bolder change, or shipping on judgment
- Specifies the build: hash-based sticky assignment, logging exposure when the change is actually seen, how anonymous visitors map to accounts after signup, flicker control, and A/A and sample-ratio checks
- Builds a per-page capacity table and sorts ideas into test, ship, or research, ranked by expected value per week of traffic
- Handles the harder cases: B2B account-level randomization, SEO page-split tests, marketplace interference, overlapping tests, and long-running holdouts
- Reviews results you paste in for peeking, sample ratio mismatch, and missing confidence intervals before anyone calls a winner
What you get
A test plan you can launch: hypothesis, primary and guardrail metrics, sample size with the arithmetic, duration, randomization unit, a decision rule set before launch, and a QA checklist. Or, for program-level questions, a capacity-limited test roadmap.
Best for
Growth marketers, product managers, and engineers at SaaS and e-commerce companies who run experiments on real (often limited) traffic and need results they can defend.
SKILL.md preview
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name: ab-test-design-build-roadmap
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