content-experimentation-best-practices

name	content-experimentation-best-practices
description	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.
license	MIT
metadata	{"author":"sanity","version":"1.0.0"}

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