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
2. Generate seed keywords from:
   • App features and benefits
   • User language (not developer terminology)
   • App store autocomplete suggestions
3. Expand keyword list using:
   • Modifiers (free, best, simple)
   • Actions (create, track, organize)
   • Audiences (for students, for teams, for business)
4. Evaluate each keyword:
   • Search volume (estimated monthly searches)
   • Competition (number and quality of ranking apps)
   • Relevance (alignment with app function)
5. Score and prioritize keywords:
   • Primary: Title and keyword field (iOS)
   • Secondary: Subtitle and short description
   • Tertiary: Full description only
6. Map keywords to metadata locations
7. Document keyword strategy for tracking
8. 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
2. Optimize title following formula:

   [Brand Name] - [Primary Keyword] [Secondary Keyword]
   

3. Write subtitle (iOS) or short description (Android):
   • Focus on primary benefit
   • Include secondary keyword
   • Use action verbs
4. Optimize keyword field (iOS only):
   • Remove duplicates from title
   • Remove plurals (Apple indexes both forms)
   • No spaces after commas
   • Prioritize by score
5. Rewrite full description:
   • Hook paragraph with value proposition
   • Feature bullets with keywords
   • Social proof section
   • Call to action
6. Validate character counts for each field
7. Calculate keyword density (target 2-3% primary)
8. 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)
2. Extract competitor keywords from:
   • App titles and subtitles
   • First 100 words of descriptions
   • Visible metadata patterns
3. Build competitor keyword matrix:
   • Map which keywords each competitor targets
   • Calculate coverage percentage per keyword
4. Identify keyword gaps:
   • Keywords with <40% competitor coverage
   • High volume terms competitors miss
   • Long-tail opportunities
5. Analyze competitor visual assets:
   • Icon design patterns
   • Screenshot messaging and style
   • Video presence and quality
6. Compare ratings and review patterns:
   • Average rating by competitor
   • Common praise themes
   • Common complaint themes
7. Document positioning opportunities
8. 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
2. Submit for review (2 weeks before):
   • Complete all store requirements
   • Verify compliance with guidelines
   • Prepare launch communications
3. Configure post-launch systems:
   • Set up review monitoring
   • Prepare response templates
   • Configure rating prompt timing
4. Execute launch day:
   • Verify app is live in both stores
   • Announce across all channels
   • Begin review response cycle
5. Monitor initial performance (days 1-7):
   • Track download velocity hourly
   • Monitor reviews and respond within 24 hours
   • Document any issues for quick fixes
6. Conduct 7-day retrospective:
   • Compare performance to projections
   • Identify quick optimization wins
   • Plan first metadata update
7. Schedule first update (2 weeks post-launch)
8. 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)
2. Form hypothesis:

   If we [change], then [metric] will [improve/increase] by [amount]
   because [rationale].
   

3. Create variants:
   • Control: Current version
   • Treatment: Single variable change
4. Calculate required sample size:
   • Baseline conversion rate
   • Minimum detectable effect (usually 5%)
   • Statistical significance (95%)
5. Launch test:
   • Apple: Use Product Page Optimization
   • Android: Use Store Listing Experiments
6. Run test for minimum duration:
   • At least 7 days
   • Until statistical significance reached
7. Analyze results:
   • Compare conversion rates
   • Check statistical significance
   • Document learnings
8. 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

python keyword_analyzer.py --keywords "todo,task,planner"

python metadata_optimizer.py --platform ios --title "App Title"

python competitor_analyzer.py --competitors "App1,App2,App3"

python aso_scorer.py --app-id com.example.app

python ab_test_planner.py --cvr 0.05 --lift 0.10

python review_analyzer.py --app-id com.example.app

python launch_checklist.py --platform ios

python localization_helper.py --locales "en,es,de,ja"

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

ab_test_planner.py

aso_scorer.py

keyword_analyzer.py

keyword_analyzer.py

competitor_analyzer.py

aso_scorer.py

aso_scorer.py

attribution_analyzer.py

seo_optimizer.py

brand_voice_analyzer.py

Tool Reference

keyword_analyzer.py

Type: Python library (imported, not CLI)

Classes:

• KeywordAnalyzer

  -- Core analysis class

Key Methods:

analyze_keyword()

keyword: str

search_volume: int = 0

competing_apps: int = 0

relevance_score: float = 0.0

compare_keywords()

keywords_data: List[Dict]

find_long_tail_opportunities()

base_keyword: str

modifiers: List[str]

extract_keywords_from_text()

text: str

min_word_length: int = 3

calculate_keyword_density()

text: str

target_keywords: List[str]

Convenience Function:

analyze_keyword_set(keywords_data)

metadata_optimizer.py

Type: Python library (imported, not CLI)

Classes:

• MetadataOptimizer(platform: str = 'apple')

  -- Platform must be

  'apple'

  or

  'google'

Key Methods:

optimize_title()

app_name: str

target_keywords: List[str]

include_brand: bool = True

optimize_description()

app_info: Dict

target_keywords: List[str]

description_type: str = 'full'

'full'

'short'

'subtitle'

optimize_keyword_field()

target_keywords: List[str]

app_title: str = ""

app_description: str = ""

validate_character_limits()

metadata: Dict[str, str]

calculate_keyword_density()

text: str

target_keywords: List[str]

Convenience Function:

optimize_app_metadata(platform, app_info, target_keywords)

competitor_analyzer.py

Type: Python library (imported, not CLI)

Classes:

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

Key Methods:

analyze_competitor()

app_data: Dict

compare_competitors()

competitors_data: List[Dict]

identify_gaps()

your_app_data: Dict

competitors_data: List[Dict]

Convenience Function:

analyze_competitor_set(category, competitors_data, platform='apple')

aso_scorer.py

Type: Python library (imported, not CLI)

Classes:

• ASOScorer

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

Key Methods:

calculate_overall_score()

metadata: Dict

ratings: Dict

keyword_performance: Dict

conversion: Dict

score_metadata_quality()

metadata: Dict

score_ratings_reviews()

ratings: Dict

score_keyword_performance()

keyword_performance: Dict

score_conversion_metrics()

conversion: Dict

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:

design_test()

test_type: str

'icon'

'screenshot'

'title'

'description'

variant_a: Dict

variant_b: Dict

hypothesis: str

success_metric: str = 'conversion_rate'

calculate_sample_size()

baseline_conversion: float

minimum_detectable_effect: float

confidence_level: str = 'standard'

'high'

'standard'

'exploratory'

power: float = 0.80

calculate_significance()

variant_a_conversions: int

variant_a_visitors: int

variant_b_conversions: int

variant_b_visitors: int

track_test_results()

test_id: str

results_data: Dict

generate_test_report()

test_id: str

final_results: Dict

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:

analyze_sentiment()

reviews: List[Dict]

extract_common_themes()

reviews: List[Dict]

min_mentions: int = 3

identify_issues()

reviews: List[Dict]

rating_threshold: int = 3

find_feature_requests()

reviews: List[Dict]

track_sentiment_trends()

reviews_by_period: Dict[str, List[Dict]]

generate_response_templates()

issue_category: str

'crash'

'bug'

'feature_request'

'positive'

'negative_general'

Convenience Function:

analyze_reviews(app_name, reviews)

launch_checklist.py

Type: Python library (imported, not CLI)

Classes:

• LaunchChecklistGenerator(platform: str = 'both')

  -- Platform:

  'apple'

  ,

  'google'

  , or

  'both'

Key Methods:

generate_prelaunch_checklist()

app_info: Dict

launch_date: Optional[str]

validate_app_store_compliance()

app_data: Dict

platform: str = 'apple'

create_update_plan()

current_version: str

planned_features: List[str]

update_frequency: str = 'monthly'

optimize_launch_timing()

app_category: str

target_audience: str

current_date: Optional[str]

plan_seasonal_campaigns()

app_category: str

current_month: int = None

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:

identify_target_markets()

current_market: str = 'en-US'

budget_level: str = 'medium'

'low'

'medium'

'high'

target_market_count: int = 5

translate_metadata()

source_metadata: Dict[str, str]

source_language: str

target_language: str

platform: str = 'apple'

adapt_keywords()

source_keywords: List[str]

source_language: str

target_language: str

target_market: str

validate_translations()

translated_metadata: Dict[str, str]

target_language: str

platform: str = 'apple'

calculate_localization_roi()

target_markets: List[str]

current_monthly_downloads: int

localization_cost: float

expected_lift_percentage: float = 0.15

Convenience Function:

plan_localization_strategy(current_market, budget_level, monthly_downloads)