| name | aso-conversion |
| description | Conversion rate optimization for app store listings. Analyzes first-impression elements, screenshot narrative, social proof signals, and psychology-based messaging to maximize install rate. Triggers on: "conversion", "install rate", "CRO", "conversion rate".
|
| user-invokable | true |
| argument-hint | <app-id> |
ASO Conversion — Conversion Rate Optimization
Capabilities
- First-impression audit (what users see before tapping "Read More")
- Screenshot narrative arc analysis
- Social proof signal assessment
- Psychology-based messaging evaluation
- Category conversion benchmarking
- Conversion funnel analysis (impression → page view → install)
Conversion Benchmarks (2024-2025)
| Platform | Average Conversion |
|---|
| iOS App Store | ~25% |
| Google Play | ~27% |
Category variance is significant (10-115%+ range).
First-Impression Elements
What users see WITHOUT scrolling or tapping "Read More":
iOS Search Results
- App icon
- App name
- Subtitle
- Rating (stars + count)
- First 3 screenshots (CRITICAL)
- Price / "Get" button
Google Play Search Results
- App icon
- App title
- Developer name
- Rating (stars)
- Price / "Install" button
Full Listing (Above Fold)
- Icon + name + developer
- Rating + review count
- Screenshots (scrollable)
- Short description (Android) / first 3 lines of description (iOS)
Scoring (0-100)
| Factor | Weight | What to assess |
|---|
| First 3 screenshots | 25% | Clear value prop, hook, visual quality |
| Title clarity | 20% | Does it explain what the app does? |
| Rating strength | 20% | ≥4.5 = strong, ≥4.0 = acceptable, <4.0 = hurting |
| Social proof | 15% | Review count, awards, editor's choice, download count |
| Subtitle/short desc | 10% | Benefit-oriented, compelling |
| Icon appeal | 10% | Distinctive, professional, recognizable |
Psychology Triggers to Evaluate
- Social proof: High rating, large review count, "X million users"
- Authority: Awards, press mentions, editor's choice badges
- Loss aversion: "Don't miss out", limited features in free tier
- Specificity: Concrete numbers ("Track 50+ habits") vs vague claims
- Benefit framing: Focus on outcomes, not features
Output Format
# Conversion Analysis: [App Name]
## Score: XX/100
## First-Impression Audit
| Element | Current | Assessment | Recommendation |
|---------|---------|-----------|----------------|
| Icon | [description] | [score] | [improvement] |
| Title | "[current]" | [score] | [improvement] |
| Subtitle/Short Desc | "[current]" | [score] | [improvement] |
| Rating | X.X (Y reviews) | [score] | [improvement] |
| Screenshot 1 | [description] | [score] | [improvement] |
| Screenshot 2 | [description] | [score] | [improvement] |
| Screenshot 3 | [description] | [score] | [improvement] |
## Screenshot Narrative Analysis
[Flow assessment: hook → features → proof]
## Social Proof Signals
[What's present, what's missing]
## Psychology Triggers
[Which are used, which could be added]
## Quick Wins
[Highest-impact, lowest-effort changes]
Available Tools
Read, Bash, Write, Glob, Grep
