| name | ab-testing |
| displayName | A/B Testing |
| tagline | Design, run, and analyze A/B tests for marketing campaigns and product features. |
| description | Helps design statistically rigorous A/B tests for marketing campaigns,
landing pages, and product features. Calculates required sample sizes,
defines success metrics, sets up test variants, and analyzes results
with confidence intervals. Prevents common testing mistakes like
peeking and underpowered tests.
|
| department | ["Marketing","Product","Data & Analytics"] |
| use_cases | ["Experimentation","Conversion Optimization","Campaign Optimization"] |
| tools_required | ["Google Analytics MCP"] |
| agents_compatible | ["Claude / Claude Code","Cursor","Windsurf","ChatGPT","Any MCP-compatible agent"] |
| author | Webrix |
| verified | true |
| updatedAt | "2026-05-08T00:00:00.000Z" |
| version | 1.0.0 |
| exampleInput | Design an A/B test for our pricing page.
Goal: increase trial signups by 15%.
Current conversion rate: 3.2%.
|
| exampleOutput | A/B Test Plan — Pricing Page
HYPOTHESIS
Simplifying the pricing page from 4 tiers to 3 will increase
trial signups by 15% (from 3.2% to 3.68%).
TEST DESIGN
Control (A): Current 4-tier pricing page
Variant (B): Simplified 3-tier pricing page
Metric: Trial signup rate
Traffic Split: 50/50
SAMPLE SIZE
Required: 12,400 visitors per variant
Duration: ~18 days (at 1,400 visitors/day)
Confidence: 95%
Power: 80%
RESULTS (after 18 days)
Control: 3.2% (398/12,438)
Variant: 3.9% (487/12,487)
Lift: +21.9% ✅ Statistically significant (p=0.003)
|
A/B Testing
Design, run, and analyze A/B tests for marketing campaigns and product features.
Integrations: Google Analytics
When to Use
- The user wants to test different versions of a page or campaign
- Data-driven decisions are needed for feature or design changes
- The user mentions "A/B test", "experiment", or "conversion optimization"
Steps
Step 1: Define Hypothesis
Formulate a testable hypothesis with expected outcome.
Step 2: Calculate Sample Size
Determine required traffic and test duration for statistical significance.
Step 3: Set Up Variants
Configure control and variant with tracking.
Step 4: Analyze Results
Evaluate statistical significance and make recommendations.
Output
Deliver:
- Test plan with hypothesis and sample size
- Variant configuration details
- Results analysis with confidence intervals
