app-store-optimization（应用商店优化）

App Store Optimization (ASO)

ASO tools for researching keywords, optimizing metadata, analyzing competitors, and improving app store visibility on Apple App Store and Google Play Store.

Table of Contents

• Keyword Research Workflow
• Metadata Optimization Workflow
• Competitor Analysis Workflow
• App Launch Workflow
• A/B Testing Workflow
• Before/After Examples
• Tools and References

Keyword Research Workflow

Discover and evaluate keywords that drive app store visibility.

Workflow: Conduct Keyword Research

1. Define target audience and core app functions:

- Primary use case (what problem does the app solve) - Target user demographics - Competitive category

1. Generate seed keywords from:

- App features and benefits - User language (not developer terminology) - App store autocomplete suggestions

1. Expand keyword list using:

- Modifiers (free, best, simple) - Actions (create, track, organize) - Audiences (for students, for teams, for business)

1. Evaluate each keyword:

- Search volume (estimated monthly searches) - Competition (number and quality of ranking apps) - Relevance (alignment with app function)

1. Score and prioritize keywords:

- Primary: Title and keyword field (iOS) - Secondary: Subtitle and short description - Tertiary: Full description only

1. Map keywords to metadata locations
2. Document keyword strategy for tracking
3. Validation: Keywords scored; placement mapped; no competitor brand names included; no plurals in iOS keyword field

Keyword Evaluation Criteria

Keyword Placement Priority

See: references/keyword-research-guide.md

Metadata Optimization Workflow

Optimize app store listing elements for search ranking and conversion.

Workflow: Optimize App Metadata

1. Audit current metadata against platform limits:

- Title character count and keyword presence - Subtitle/short description usage - Keyword field efficiency (iOS) - Description keyword density

1. Optimize title following formula: [Brand Name] - [Primary Keyword] [Secondary Keyword]
2. Write subtitle (iOS) or short description (Android):

- Focus on primary benefit - Include secondary keyword - Use action verbs

1. Optimize keyword field (iOS only):

- Remove duplicates from title - Remove plurals (Apple indexes both forms) - No spaces after commas - Prioritize by score

1. Rewrite full description:

- Hook paragraph with value proposition - Feature bullets with keywords - Social proof section - Call to action

1. Validate character counts for each field
2. Calculate keyword density (target 2-3% primary)
3. Validation: All fields within character limits; primary keyword in title; no keyword stuffing (>5%); natural language preserved

Platform Character Limits

Description Structure

PARAGRAPH 1: Hook (50-100 words)
├── Address user pain point
├── State main value proposition
└── Include primary keyword

PARAGRAPH 2-3: Features (100-150 words)
├── Top 5 features with benefits
├── Bullet points for scanability
└── Secondary keywords naturally integrated

PARAGRAPH 4: Social Proof (50-75 words)
├── Download count or rating
├── Press mentions or awards
└── Summary of user testimonials

PARAGRAPH 5: Call to Action (25-50 words)
├── Clear next step
└── Reassurance (free trial, no signup)

See: references/platform-requirements.md

Competitor Analysis Workflow

Analyze top competitors to identify keyword gaps and positioning opportunities.

Workflow: Analyze Competitor ASO Strategy

1. Identify top 10 competitors:

- Direct competitors (same core function) - Indirect competitors (overlapping audience) - Category leaders (top downloads)

1. Extract competitor keywords from:

- App titles and subtitles - First 100 words of descriptions - Visible metadata patterns

1. Build competitor keyword matrix:

- Map which keywords each competitor targets - Calculate coverage percentage per keyword

1. Identify keyword gaps:

- Keywords with <40% competitor coverage - High volume terms competitors miss - Long-tail opportunities

1. Analyze competitor visual assets:

- Icon design patterns - Screenshot messaging and style - Video presence and quality

1. Compare ratings and review patterns:

- Average rating by competitor - Common praise themes - Common complaint themes

1. Document positioning opportunities
2. Validation: 10+ competitors analyzed; keyword matrix complete; gaps identified with volume estimates; visual audit documented

Competitor Analysis Matrix

Gap Analysis Template

App Launch Workflow

Execute a structured launch for maximum initial visibility.

Workflow: Launch App to Stores

1. Complete pre-launch preparation (4 weeks before):

- Finalize keywords and metadata - Prepare all visual assets - Set up analytics (Firebase, Mixpanel) - Build press kit and media list

1. Submit for review (2 weeks before):

- Complete all store requirements - Verify compliance with guidelines - Prepare launch communications

1. Configure post-launch systems:

- Set up review monitoring - Prepare response templates - Configure rating prompt timing

1. Execute launch day:

- Verify app is live in both stores - Announce across all channels - Begin review response cycle

1. Monitor initial performance (days 1-7):

- Track download velocity hourly - Monitor reviews and respond within 24 hours - Document any issues for quick fixes

1. Conduct 7-day retrospective:

- Compare performance to projections - Identify quick optimization wins - Plan first metadata update

1. Schedule first update (2 weeks post-launch)
2. Validation: App live in stores; analytics tracking; review responses within 24h; download velocity documented; first update scheduled

Pre-Launch Checklist

Launch Timing Considerations

See: references/aso-best-practices.md

A/B Testing Workflow

Test metadata and visual elements to improve conversion rates.

Workflow: Run A/B Test

1. Select test element (prioritize by impact):

- Icon (highest impact) - Screenshot 1 (high impact) - Title (high impact) - Short description (medium impact)

1. Form hypothesis: If we [change], then [metric] will [improve/increase] by [amount] because [rationale].
2. Create variants:

- Control: Current version - Treatment: Single variable change

1. Calculate required sample size:

- Baseline conversion rate - Minimum detectable effect (usually 5%) - Statistical significance (95%)

1. Launch test:

- Apple: Use Product Page Optimization - Android: Use Store Listing Experiments

1. Run test for minimum duration:

- At least 7 days - Until statistical significance reached

1. Analyze results:

- Compare conversion rates - Check statistical significance - Document learnings

1. Validation: Single variable tested; sample size sufficient; significance reached (95%); results documented; winner implemented

A/B Test Prioritization

Sample Size Quick Reference

Test Documentation Template

TEST ID: ASO-2025-001
ELEMENT: App Icon
HYPOTHESIS: A bolder color icon will increase conversion by 10%
START DATE: [Date]
END DATE: [Date]

RESULTS:
├── Control CVR: 4.2%
├── Treatment CVR: 4.8%
├── Lift: +14.3%
├── Significance: 97%
└── Decision: Implement treatment

LEARNINGS:
- Bold colors outperform muted tones in this category
- Apply to screenshot backgrounds for next test

Before/After Examples

Title Optimization

Productivity App:

Fitness App:

Subtitle Optimization (iOS)

Keyword Field Optimization (iOS)

Before (Inefficient - 89 chars, 8 keywords):

task manager, todo list, productivity app, daily planner, reminder app

After (Optimized - 97 chars, 14 keywords):

task,todo,checklist,reminder,organize,daily,planner,schedule,deadline,goals,habit,widget,sync,team

Improvements:

• Removed spaces after commas (+8 chars)
• Removed duplicates (task manager → task)
• Removed plurals (reminders → reminder)
• Removed words in title
• Added more relevant keywords

Description Opening

Before:

MyTasks is a comprehensive task management solution designed
to help busy professionals organize their daily activities
and boost productivity.

After:

Forget missed deadlines. MyTasks keeps every task, reminder,
and project in one place—so you focus on doing, not remembering.
Trusted by 500,000+ professionals.

Improvements:

• Leads with user pain point
• Specific benefit (not generic "boost productivity")
• Social proof included
• Keywords natural, not stuffed

Screenshot Caption Evolution

Tools and References

Scripts

References

Assets

Platform Limitations

Data Constraints

Platform Behavior

When Not to Use This Skill

Related Skills

Proactive Triggers

• No keyword optimization in title -- App title is the #1 ranking factor. Include top keyword in title.
• Screenshots don't show value -- Screenshots should tell a benefit story, not just show UI.
• No ratings strategy -- Below 4.0 stars kills conversion. Implement in-app rating prompts at positive moments.
• Description keyword-stuffed -- Natural language with keywords beats keyword stuffing. Target 2-3% density.

Output Artifacts

Communication

All output passes quality verification:

• Self-verify: source attribution, assumption audit, confidence scoring
• Output format: Bottom Line first, then What (with confidence), Why, How to Act
• Every finding tagged with confidence level: verified, medium confidence, or assumed

Troubleshooting

Success Criteria

• Conversion Rate (Impression-to-Install): Target 25-30% on iOS and 27-33% on Google Play (2026 cross-category median: 25% iOS, 27.3% Google Play). Productivity and utility apps should aim for 40%+ given category norms
• Keyword Rankings: Maintain 10+ keywords in top-10 positions and 20+ keywords in top-50 positions within your primary market. Track ranking improvements week-over-week after each metadata update
• Rating Quality: Sustain an average rating of 4.5+ stars with 100+ ratings per month. Apps below 4.0 stars experience measurable conversion drops. Aim for 99%+ crash-free rate to protect ratings
• Metadata Utilization: Use 90%+ of available character space in title, subtitle (iOS), short description (Android), and keyword field (iOS). Under-utilized metadata is wasted ranking potential
• A/B Test Velocity: Run at least one store listing experiment per month. Achieve statistical significance (95% confidence) before implementing winners. Target 5-15% conversion lift per successful test cycle
• Localization Coverage: Localize metadata for at least 5 priority markets (US, China, Japan, Germany, UK) with native-speaker keyword research. Localized apps see 15-30% download increases in target markets
• Apple Search Ads Efficiency: Maintain a tap-through rate (TTR) above 5% and cost-per-acquisition (CPA) below category median. In 2026, Apple is expanding search ad inventory with additional inline placements -- optimize for the new Maximize Conversions bidding option

Scope & Limitations

In Scope:

• Keyword research, metadata optimization, and character limit validation for Apple App Store and Google Play Store
• Competitor ASO analysis using publicly available metadata, ratings, and reviews
• A/B test planning with sample size calculations and statistical significance testing
• Launch checklist generation, seasonal campaign planning, and localization strategy
• Review sentiment analysis and feature request extraction

Out of Scope:

• Real-time app store data fetching (scripts analyze static data you provide)
• Apple Search Ads or Google Ads campaign management (use platform dashboards)
• Creative asset design (icon, screenshots, video production)
• Cross-device attribution and install tracking (requires MMP integration such as AppsFlyer, Adjust, or Branch)
• In-app analytics and retention optimization (use Firebase, Mixpanel, or Amplitude)
• Revenue or subscription optimization (pricing strategy is a separate discipline)

Data Constraints:

• No official search volume API exists for either app store; volume estimates rely on third-party tools or heuristic scoring
• Competitor data is limited to publicly visible metadata, ratings, and reviews
• Historical ranking data requires external ASO tools (AppTweak, Sensor Tower, data.ai)
• Apple's June 2025 algorithm update now indexes screenshot text as a ranking factor -- this skill's scripts do not yet analyze visual text content

Integration Points

Tool Reference

keyword_analyzer.py

Type: Python library (imported, not CLI)

Classes:

• KeywordAnalyzer -- Core analysis class

Key Methods:

Convenience Function: analyze_keyword_set(keywords_data) -- Analyzes and ranks a full keyword set in one call.

metadata_optimizer.py

Type: Python library (imported, not CLI)

Classes:

• MetadataOptimizer(platform: str = 'apple') -- Platform must be 'apple' or 'google'

Key Methods:

Convenience Function: optimize_app_metadata(platform, app_info, target_keywords) -- Optimizes title, description, and keyword field in one call.

competitor_analyzer.py

Type: Python library (imported, not CLI)

Classes:

• CompetitorAnalyzer(category: str, platform: str = 'apple')

Key Methods:

Convenience Function: analyze_competitor_set(category, competitors_data, platform='apple') -- Full competitive analysis in one call.

aso_scorer.py

Type: Python library (imported, not CLI)

Classes:

• ASOScorer -- Calculates weighted ASO health score (0-100)

Key Methods:

Weights: metadata_quality 25%, ratings_reviews 25%, keyword_performance 25%, conversion_metrics 25%.

Convenience Function: calculate_aso_score(metadata, ratings, keyword_performance, conversion)

ab_test_planner.py

Type: Python library (imported, not CLI)

Classes:

• ABTestPlanner

Key Methods:

Convenience Function: plan_ab_test(test_type, variant_a, variant_b, hypothesis, baseline_conversion)

review_analyzer.py

Type: Python library (imported, not CLI)

Classes:

• ReviewAnalyzer(app_name: str)

Key Methods:

Convenience Function: analyze_reviews(app_name, reviews) -- Runs sentiment, themes, issues, and feature requests in one call.

launch_checklist.py

Type: Python library (imported, not CLI)

Classes:

• LaunchChecklistGenerator(platform: str = 'both') -- Platform: 'apple', 'google', or 'both'

Key Methods:

Convenience Function: generate_launch_checklist(platform, app_info, launch_date)

localization_helper.py

Type: Python library (imported, not CLI)

Classes:

• LocalizationHelper(app_category: str = 'general')

Key Methods:

Convenience Function: plan_localization_strategy(current_market, budget_level, monthly_downloads)