
Ab Test Store Listing
Design and interpret App Store Product Page Optimization tests on icon, screenshots, and preview video to lift install conversion.
Overview
A/B Test Store Listing is an agent skill for the Launch phase that helps design, run, and read App Store Product Page Optimization experiments on icon, screenshots, and preview video.
Install
npx skills add https://github.com/eronred/aso-skills --skill ab-test-store-listingWhat is this skill?
- Apple Product Page Optimization matrix: icon, screenshots, and preview video up to 3 variants each
- Initial assessment checklist: App ID, conversion rate, daily impressions, and test element
- Documents PPO limits—description, title, and subtitle are not testable via Apple's native tool
- Cross-links to screenshot-optimization and metadata-optimization sibling ASO skills
- Statistical guidance: ~90% confidence and impression-driven duration planning
- Up to 3 variants per testable PPO element
- Minimum ~90% confidence called out for PPO decisions
- 6+ testable publisher contexts when counting listing, on-chain, and exchange categories in sibling docs—here: 3 visual e
Adoption & trust: 1.6k installs on skills.sh; 1.5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your App Store page gets impressions but installs lag and you are unsure how to run a valid Apple PPO test on creatives.
Who is it for?
Indie iOS developers with measurable App Store traffic who want structured PPO experiments on visual assets.
Skip if: Android Play Store listing experiments, brand-new apps with near-zero impressions, or teams seeking automatic metadata title/subtitle tests Apple PPO does not support.
When should I use this skill?
User wants A/B tests, product page optimization, CPP, or to test icon, screenshots, or preview video for App Store conversion.
What do I get? / Deliverables
You leave with a test plan, variant constraints, and interpretation notes aligned to App Store Connect PPO so you can ship a winning listing creative confidently.
- A/B test design with variants and duration guidance
- Interpretation framework for PPO results
- Pointers to metadata or screenshot skills for non-PPO elements
Recommended Skills
Journey fit
Launch/aso is the canonical home for App Store Connect PPO and CPP experiments that directly affect store conversion—not backend build or post-launch analytics alone. ASO subphase covers native Apple A/B tooling, traffic requirements, and listing creative tests called out in the skill.
How it compares
ASO experiment playbook skill—not a replacement for screenshot design tools or paid UA creative testing off-store.
Common Questions / FAQ
Who is ab-test-store-listing for?
Solo and indie iOS builders improving App Store conversion who already publish on App Store Connect and can read baseline metrics.
When should I use ab-test-store-listing?
During Launch ASO when you mention A/B tests, CPP, product page optimization, or want to test icons, screenshots, or preview videos against organic store traffic.
Is ab-test-store-listing safe to install?
It is editorial ASO guidance only; review the Security Audits panel on this Prism page and never paste App Store Connect secrets into untrusted copies of the skill.
Workflow Chain
Then invoke: screenshot optimization, metadata optimization
SKILL.md
READMESKILL.md - Ab Test Store Listing
# 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 convers