App Store Optimization

App Store Optimization (ASO)

Iron Law

NO STORE SUBMISSION WITHOUT COMPLETING ASO HEALTH CHECK FIRST — TARGET SCORE ≥ 70/100

Run aso_scorer.py before any first App Store or Play Store submission. A low score wastes review queue time and launch momentum.

When to Use

• Before first App Store or Play Store submission
• Before each major update (new features, new screenshots, new markets)
• When ratings drop — use review_analyzer.py to find root causes
• When downloads plateau — keyword and competitor audit needed
• Before expanding to new markets — localization ROI assessment

Platform Character Limits (enforced by metadata_optimizer.py)

Workflow

Step 1 — Keyword Research

Use keyword_analyzer.py to:
- Score candidate keywords by volume/competition/relevance
- Find long-tail opportunities (3–4 word phrases, lower competition)
- Identify which competitor keywords have gaps

Output: Ranked keyword list — primary (title/subtitle), secondary (keyword field), long-tail (description)

Step 2 — Metadata Optimization

Use metadata_optimizer.py to:
- Generate platform-specific title within character limit
- Write subtitle (Apple) / short description (Google)
- Craft conversion-focused full description
- Maximize Apple keyword field (100 chars, no wasted characters)
- Validate all character limits before writing

Apple keyword field rules: No spaces after commas, no plurals if singular exists, no words already in title, no competitor names.

Step 3 — Competitor Analysis

Use competitor_analyzer.py to:
- Extract top 10 competitor keyword strategies
- Identify visual asset approaches (icon style, screenshot structure)
- Find keyword gaps — terms they rank for that you don't target
- Spot positioning opportunities

Step 4 — ASO Health Score

Use aso_scorer.py to:
- Score 0–100 across 4 dimensions:
    Metadata Quality       (0–25): title, description, keyword density
    Ratings & Reviews      (0–25): average rating, volume
    Keyword Performance    (0–25): rankings in top 10/50/100
    Conversion Metrics     (0–25): impression-to-install rate
- Generate prioritized action list

Gate: Score ≥ 70 before proceeding to submission.

Step 5 — A/B Test Plan (icon + screenshots)

Use ab_test_planner.py to:
- Design test hypothesis and variants
- Calculate required impressions for statistical significance
- Define success metric (impression-to-install rate target)
- Recommend test duration

Step 6 — Review Sentiment Analysis (post-launch or before update)

Use review_analyzer.py to:
- Analyze sentiment distribution (positive/negative/neutral)
- Extract top complaint themes — rank by frequency
- Identify feature requests
- Generate response templates per complaint category
- Track sentiment trends across versions

Step 7 — Localization (international expansion)

Use localization_helper.py to:
- Identify high-ROI markets by tier:
    Tier 1: en-US, zh-CN, ja-JP, ko-KR, de-DE, fr-FR
    Tier 2: es-ES, pt-BR, ru-RU, it-IT
    Tier 3: nl-NL, pl-PL, tr-TR, sv-SE
- Adapt keywords per locale (not direct translation)
- Validate character limits per language (German ≈ 1.3× English length)
- Estimate localization ROI before investing

Step 8 — Launch Checklist

Use launch_checklist.py to:
- Generate platform-specific pre-launch checklist
- Validate Apple App Store compliance (metadata, screenshots, legal)
- Validate Google Play compliance (target API, content rating, privacy policy)
- Create update cadence plan
- Identify seasonal campaign opportunities

Scripts Reference

Integration with Release Workflows

This skill runs before the technical submission skills:

app-store-optimization (Phase 0)
        ↓
asc-* skills (iOS: signing → TestFlight → submission → monitoring)
gpd-* skills (Android: upload → beta → health check → staged rollout)

asc-submission-health checks technical compliance (build state, encryption, screenshots exist). This skill checks content quality (keyword strategy, conversion copy, ASO score). They are different layers — both are required before a production submission.

Anti-Patterns

• Don't skip the ASO health check before submission. Running aso_scorer.py after submission wastes review queue time — the gate is pre-submission.
• Don't use the same keywords in both the title and the Apple keyword field. Duplicate keywords waste the 100-character limit; keywords in the title already get indexed.
• Don't directly translate metadata between locales. Keyword intent differs by market — use localization_helper.py for locale-specific keyword research, not machine translation.
• Don't run A/B tests without confirming statistical significance first. Use ab_test_planner.py to calculate required impressions before declaring a winner.

Verify

After running aso_scorer.py, confirm:

• Score output shows ≥ 70/100 before proceeding to submission.
• All character limits validated: metadata_optimizer.py must exit without limit warnings.
• Apple keyword field: run grep "," <keyword_field> to confirm no spaces after commas and no duplicates with the title.

Limitations

• Keyword volume estimates are heuristic — no live Apple/Google API access
• Competitor data covers public store listings only
• A/B testing requires sufficient traffic for statistical significance (12,000+ impressions per variant)
• Store algorithms are proprietary and change without announcement

Documentation Sources