Does it help with post-campaign analysis?

Yes, it provides frameworks for analysis using z-tests or Bayesian uplift and includes templates for post-test debriefs to document learnings.

How does it ensure statistical significance?

The skill includes guidance on sample sizing, typically recommending a minimum of 500 recipients per variant or the use of a power calculator to ensure results are reliable.

What is the Email A/B Testing skill for Claude Code?

It is a specialized capability that provides frameworks and templates for designing, executing, and analyzing scientific experiments within marketing workflows.

What elements of an email can I test with this skill?

You can test subject lines, preview text, CTA placement, personalization depth, journey timing, and creative asset variations.

Email A/B Testing & Experimentation

Name: Email A/B Testing & Experimentation
Author: gtmagents

bygtmagents

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营销自动化

Optimizes email marketing performance through structured experimentation frameworks and data-driven A/B testing methodologies.

关于

This skill empowers Claude to design, execute, and analyze scientific A/B tests for email marketing campaigns, ranging from simple subject line variations to complex journey cadences. It provides a rigorous framework covering hypothesis generation, statistical significance calculations, and metric selection to ensure marketing decisions are based on data rather than intuition. Whether you are optimizing CTA placement or testing persona-based segmentation, this skill helps you build repeatable experimentation playbooks that drive higher conversion rates and revenue growth.

主要功能

Primary metric selection and risk guardrail monitoring
Statistical significance and sample size calculation guidance
Variant design for subject lines, creative, and CTA placement
29 GitHub stars
Structured hypothesis development with uplift rationale
Post-test analysis using z-test or Bayesian uplift methods

使用场景

Optimizing automation triggers and wait times within email journeys
Testing persona-based vs. behavior-based segmentation hypotheses
Validating new subject lines and preview text to increase open rates

关于

主要功能

Primary metric selection and risk guardrail monitoring
Statistical significance and sample size calculation guidance
Variant design for subject lines, creative, and CTA placement
29 GitHub stars
Structured hypothesis development with uplift rationale
Post-test analysis using z-test or Bayesian uplift methods

使用场景

Optimizing automation triggers and wait times within email journeys
Testing persona-based vs. behavior-based segmentation hypotheses
Validating new subject lines and preview text to increase open rates