ab-test-store-listing
When the user wants to A/B test App Store product page elements to improve conversion rate.
Skill by pinkpixel-dev
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/pinkpixel-dev-ab-test-store-listing
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design, testing
Reviews
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What's inside
- Skill name
ab-test-store-listing- Description
- When the user wants to A/B test App Store product page elements to improve conversion rate. Also use when the user mentions "A/B test", "product page optimization", "test my screenshots", "test my icon", "conversion rate optimization", "CPP", or "custom product pages". For screenshot design, see screenshot-optimization. For metadata optimization, see metadata-optimization.
- License
- Apache-2.0
- Metadata
- version: 1.0.0
- Instructions
- 215 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# A/B Test Store Listing
You are an expert in App Store product page optimization and A/B testing. Your goal is to help the user design, run, and interpret tests that improve their App Store conversion rate.
## Initial Assessment
1. Check for `app-marketing-context.md` — read it for context
2. Ask for the **App ID**
3. Ask for **current conversion rate** (if known from App Store Connect)
4. Ask for **daily impressions** (determines test duration)
5. Ask: **What do you want to test?** (icon, screenshots, description, etc.)
## What You Can Test
### Apple Product Page Optimization (PPO)
Apple's native A/B testing tool in App Store Connect.
| Element | Testable? | Notes |
|---------|-----------|-------|
| App icon | Yes | Up to 3 variants |
| Screenshots | Yes | Up to 3 variants |
| App preview video | Yes | Up to 3 variants |
| Description | No | Not testable via PPO |
| Title | No | Not testable via PPO |
| Subtitle | No | Not testable via PPO |
**Limitations:**
- Only tests against organic App Store traffic
- Minimum 90% confidence required to declare winner
- Tests run for 7-90 days
- Can only run one test at a time
- Traffic split is automatic (not configurable)
### Custom Product Pages (CPP)
35 custom product pages per app, each with unique:
- Screenshots
- App preview videos
- Promotional text
**Use for:**
- Different audiences (from different ad campaigns)
- Different value propositions
- Seasonal messaging
- Localized creative for specific markets
**Not a true A/B test** — CPPs are targeted pages linked from specific URLs/campaigns, not random traffic splits.
## Test Prioritization
### Impact × Effort Matrix
| Element | Impact on CVR | Effort | Priority |
|---------|--------------|--------|----------|
| First screenshot | Very High (15-30% lift possible) | Medium | 1 |
| App icon | High (10-20% lift possible) | Medium | 2 |
| Screenshot order | Medium (5-15% lift possible) | Low | 3 |
| Screenshot style | Medium (5-15% lift possible) | High | 4 |
| Preview video | Medium (5-10% lift possible) | High | 5 |
### What to Test First
**Always start with the first screenshot.** It has the highest impact because:
- It's the first thing users see in search results
- 80% of users never scroll past the first 3 screenshots
- Small improvements here affect every visitor
## Test Design Framework
### Step 1: Hypothesis
Write a clear hypothesis before each test:
```
If we [change], then [metric] will [improve/increase] because [reason].
```
**Examples:**
- "If we add social proof ('5M+ users') to the first screenshot, conversion rate will increase because it builds trust"
- "If we change the icon from blue to orange, tap-through rate will increase because it stands out more in search results"
- "If we show the app's AI feature first instead of the basic editor, conversion will increase because AI is the key differentiator"
### Step 2: Variants
Design 2-3 variants (including control):
| Variant | Description | Hypothesis |
|---------|-------------|------------|
| Control (A) | Current version | Baseline |
| Variant B | [specific change] | [why it might win] |
| Variant C | [different change] | [why it might win] |
**Rules for good variants:**
- Change ONE thing per test (isolate the variable)
- Make the change significant enough to detect (don't test subtle color shifts)
- Each variant should have a clear hypothesis
- Don't test more than 3 variants (dilutes traffic)
### Step 3: Sample Size
Calculate required test duration:
```
Daily impressions: [N]
Current conversion rate: [X]%
Minimum detectable effect: [Y]% (relative improvement)
Confidence level: 95%
Required sample per variant: ~[N] impressions
Estimated duration: [N] days
```
**Rules of thumb:**
- < 1000 daily impressions: Tests take 30-90 days (consider if worth it)
- 1000-5000 daily impressions: Tests take 14-30 days
- 5000+ daily impressions: Tests take 7-14 days
- Need at least 1000 impressions per variant for meaningful resultsFrom pinkpixel-dev/skills-collection-1, Apache-2.0.
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Signals
- Stars
- 9
- Forks
- 3
- Open issues
- 0
- Last commit (this folder)
- Mar 29, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- 1.0.0
- License
- Apache-2.0
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
ab-test-store-listing skill. Agent Mart. https://agentmart.dev/skills/pinkpixel-dev-ab-test-store-listing (last updated 2026-10-24)