# Content Experimentation Best Practices

> A/B testing and content experimentation methodology for data-driven optimization. Use when setting up A/B tests, multivariate tests, or experimentation infrastructure — including experiment design, statistical significance, conversion rate optimization (CRO), feature flags, or integrating experiments with a CMS. Trigger when the user mentions A/B test, split test, variant, experiment, CRO, conversion rate, statistical significance, feature flag, or asks how to test or measure content changes.

- Skill: `majiayu000/content-experimentation-best-practices-2` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add majiayu000/content-experimentation-best-practices-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/content-experimentation-best-practices-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- License: MIT
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/majiayu000/content-experimentation-best-practices-2

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# Content Experimentation Best Practices

Principles and patterns for running effective content experiments to improve conversion rates, engagement, and user experience.

## When to Apply

Reference these guidelines when:
- Setting up A/B or multivariate testing infrastructure
- Designing experiments for content changes
- Analyzing and interpreting test results
- Building CMS integrations for experimentation
- Deciding what to test and how

## Core Concepts

### A/B Testing
Comparing two variants (A vs B) to determine which performs better.

### Multivariate Testing
Testing multiple variables simultaneously to find optimal combinations.

### Statistical Significance
The confidence level that results aren't due to random chance.

### Experimentation Culture
Making decisions based on data rather than opinions (HiPPO avoidance).

## Resources

See `resources/` for detailed guidance:
- Experiment design principles
- Statistical foundations
- CMS integration patterns
- Common pitfalls