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jikime-marketing-ab-test

Design, execute, and analyze A/B tests for marketing optimization.

5|1|Updated Jan 24, 2026
Search Tags:#hypothesis testing#marketing analytics#experimentation#ab testing#statistical significance#conversion rate optimization
One-click install
npx skills add https://github.com/jikime/jikime-adk --skill jikime-marketing-ab-test
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jikime-marketing-ab-test
Source: https://github.com/jikime/jikime-adk/tree/main/templates/.claude/skills/jikime-marketing-ab-test
Command: npx skills add https://github.com/jikime/jikime-adk --skill jikime-marketing-ab-test

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you design statistically sound A/B tests, calculate necessary sample sizes, and analyze experiment results to make data-driven decisions for marketing and product optimization.

Core Features & Use Cases

  • Hypothesis Formulation: Guides you in creating clear, testable hypotheses.
  • Sample Size Calculation: Provides tools and resources to determine the right sample size for reliable results.
  • Test Design & Execution: Offers best practices for setting up and running A/B, A/B/n, and multivariate tests.
  • Results Analysis: Helps interpret test outcomes, identify winners, and understand the impact.
  • Use Case: A marketing manager wants to test a new headline on a landing page to increase conversion rates. This Skill will help them formulate a hypothesis, calculate how many visitors are needed, set up the test, and analyze the results to determine if the new headline performs better.

Quick Start

Use the jikime-marketing-ab-test skill to design an A/B test for a new landing page headline, aiming for a 15% lift in conversion rate with a baseline of 3%.

Frequently Asked Questions about jikime-marketing-ab-test

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate sample size for an A/B test with a 3% baseline conversion rate?▼

To calculate sample size for A/B testing, you need your baseline conversion rate, minimum detectable lift, and desired statistical significance level. This Skill guides you through these inputs to determine the exact visitor count needed for reliable results.

What is the difference between A/B/n and multivariate testing for conversion rate optimization?▼

A/B/n testing compares multiple distinct variants against a control, while multivariate testing examines combinations of elements within a single page. This Skill helps you choose the right methodology based on your traffic volume and optimization goals.

How do I formulate a testable hypothesis for landing page experimentation?▼

A testable hypothesis for experimentation clearly defines the change, the target metric, and the expected outcome. This Skill provides structured guidance to formulate hypotheses that ensure statistically valid and actionable marketing insights.

Can I track secondary and guardrail metrics alongside my primary conversion rate optimization goals?▼

Yes, you can track primary, secondary, and guardrail metrics during A/B testing. This Skill helps you define these metric categories to ensure your experiment results do not negatively impact other critical business indicators.

When should I avoid running an A/B test due to traffic limitations?▼

You should avoid A/B testing when your traffic cannot meet the calculated sample size within a reasonable timeframe, as results will lack statistical significance. This Skill helps you assess feasibility by calculating required visitor counts upfront.

How do I interpret statistical significance after my marketing A/B test concludes?▼

Interpreting statistical significance requires analyzing whether the observed conversion rate difference between variants meets your confidence threshold. This Skill guides you through result interpretation to identify true winners and quantify their impact.

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