Conversion Copywriting Sample Size and Experimentation Plan

Role: Conversion Rate Optimization Lead and Experimentation Methodologist

Context

• Funnel Baseline Conversion Rate: {{baseline_conversion_rate}}
• Monthly Traffic Volume: {{monthly_traffic_volume}}
• Minimum Detectable Effect: {{target_uplift_percentage}}
• Candidate Copy Variations: {{copy_variant_hypotheses}}
• Statistical Power Target: {{statistical_power_target}}
• Testing Window: {{test_window_days}}

Task

Construct an end-to-end mathematical experimentation plan to validate {{copy_variant_hypotheses}} against {{baseline_conversion_rate}}, determining variant allocations, sample sizes, and stopping boundaries within {{test_window_days}}.

Method

1. Calculate the required sample size per variant using {{baseline_conversion_rate}}, {{target_uplift_percentage}}, and {{statistical_power_target}}.
2. Evaluate total available sample against {{monthly_traffic_volume}} to determine test feasibility within {{test_window_days}}.
3. Determine the maximum allowable number of copy variations from {{copy_variant_hypotheses}} to prevent sample dilution.
4. Define exact randomization and traffic allocation splits between the control and challenger variations.
5. Establish sequential testing or Bayesian decision rules to control for false discovery rates and peeking bias.
6. Outline qualitative validation steps to ensure messaging clarity matches quantitative intent.
7. Create a decision matrix detailing implementation criteria based on final p-values and confidence intervals.

Constraints

• MUST explicitly state the required sample size per variation before testing starts.
• MUST NOT approve test execution if required sample size exceeds {{monthly_traffic_volume}} over {{test_window_days}}.
• Alpha level must default to 0.05 (two-tailed) unless specific Bayesian priors are specified.
• Hypotheses must connect linguistic variations in {{copy_variant_hypotheses}} directly to behavioral mechanics.

Output format

1. Statistical Power and Sample Sizing Summary (Key parameters and calculated sample needed)
2. Variation Traffic Split Blueprint (Control vs Variants allocation percentages)
3. Test Execution Schedule (Week-by-week protocol across {{test_window_days}})
4. Post-Test Winner Implementation Framework (Definite criteria for roll-out vs discard)

Self-review

• Confirm that calculated run duration fits within {{test_window_days}} given {{monthly_traffic_volume}}.
• Verify that statistical calculations account for all variants in {{copy_variant_hypotheses}}.
• Check that stopping rules strictly prevent premature decision-making.