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.