UA & Marketing Strategy¶

Skill ID: ivx-ua-marketing-strategy

name: ivx-ua-marketing-strategy description: >- End-to-end UA economics, ASO, geo-targeting, and marketing strategy generation for IntelliVerseX SDK games. Produces a complete, data-driven marketing plan covering feature-to-cohort mapping, advanced ASO with competitor keyword gap analysis, ROAS-ranked country priority lists, payback period models (7/14/30 day), ad creative briefs, and Content-Factory pipeline integration for automated report generation. Use when the user says "marketing strategy", "ASO optimization", "user acquisition", "UA economics", "geo targeting", "ROAS analysis", "payback period", "CPI analysis", "ad spend plan", "country targeting", "keyword strategy", "store optimization", "app store listing", "competitor analysis", "ad creative brief", "marketing audit", "growth strategy", "CAC analysis", "LTV model", "retention monetization", or needs help with any UA, ASO, or marketing planning workflow. version: "1.0.0" author: "IntelliVerse-X team@intelli-verse-x.ai" allowed-tools: - Read - Write - Edit - Glob - Grep - Shell - WebSearch

Overview¶

This skill generates a complete, data-driven User Acquisition and Marketing Strategy for any game built with the IntelliVerseX SDK. It produces an actionable plan covering every layer of the marketing funnel — from store listing optimization through paid acquisition to payback economics — all grounded in the game's actual features, economy, supported languages, and competitive landscape.

Game Data (GDD, Features, Economy, Languages)
       │
       ▼
┌── Analysis Pipeline ────────────────────────────────────────────────┐
│                                                                      │
│  1. Feature Inventory ──────────► Feature-to-Cohort Mapping          │
│  2. Language Inventory ─────────► ROAS-Ranked Country Matrix         │
│  3. Economy Data ───────────────► Sink/Source Ratio + LTV Model      │
│  4. Competitor Research ────────► Keyword Gap Analysis                │
│  5. Platform Benchmarks ────────► CPI × Retention → Payback Model    │
│                                                                      │
└────────────────────────────┬─────────────────────────────────────────┘
                             │
        ┌────────────────────┼──────────────────────┐
        ▼                    ▼                      ▼
  ASO Playbook      UA Economics Plan       Ad Creative Briefs
  (per locale)      (per country tier)      (per cohort × platform)
        │                    │                      │
        └────────────────────┼──────────────────────┘
                             ▼
                    Unified Marketing Plan
                    (.md + .json + .pdf + .docx)

Content-Factory Pipeline Integration¶

This skill maps directly to the game_marketing_audit pipeline in Content-Factory.

Trigger via CLI¶

python -m pipelines.runner run \
  --config configs/pipelines/game_marketing_audit.yaml \
  --args '{
    "game_name": "Quiz-Verse",
    "game_genre": "trivia",
    "platforms": ["Android", "iOS", "WebGL"],
    "languages": ["en","es","pt","hi","ar","de","fr","ja","id","ko","ru","zh"],
    "features_file": "path/to/QUIZVERSE_COMPLETE_FEATURES_LIST.md",
    "economy_file": "path/to/COIN_ECONOMY_SYSTEM.md",
    "gdd_file": "path/to/GDD.md",
    "competitors": ["Trivia Crack", "Quizlet", "Lumosity", "QuizUp"]
  }'

Trigger via REST¶

curl -X POST http://localhost:8001/pipelines/game_marketing_audit \
  -H "Content-Type: application/json" \
  -d '{
    "brand_id": "intelliversex",
    "game_id": "quiz-verse",
    "competitors": ["Trivia Crack", "Quizlet", "Lumosity"],
    "research_competitors": true
  }'

Output Structure¶

.working_dir/game_marketing_audit/
├── marketing_audit.json          # Structured data (Pydantic model)
├── marketing_audit.md            # Human-readable report
├── exports/
│   ├── UA_Economics_and_Geo_Targeting.pdf
│   └── UA_Economics_and_Geo_Targeting.docx
└── assets/
    └── payback_chart.png         # Visual payback period chart

Workflow: Step-by-Step¶

Step 1: Gather Game Data¶

Before running the analysis, collect these inputs:

Step 2: Feature-to-Cohort Mapping¶

Map every game feature to the audience cohort it attracts most.

Framework: Etermax Cohort Segmentation

For each feature, determine: 1. Which cohort it primarily serves 2. Which ad platform that cohort trusts for downloads 3. Which language/region the feature resonates most with 4. What ad creative angle works best

Step 3: Advanced ASO Analysis¶

3.1 Keyword Categories¶

For each supported language, produce three keyword tiers:

3.2 Competitor Gap Analysis¶

For each competitor, identify:

3.3 iOS Metadata Optimization¶

TITLE (30 chars max):
  [Game Name]: [Primary KW] & [Secondary KW]
  Every character counts. Do NOT waste on generic words.

SUBTITLE (30 chars max):
  [Tertiary KW] & [Quaternary KW]
  Do NOT repeat any word from the title — Apple indexes both.

KEYWORDS FIELD (100 chars max):
  Comma-separated, no spaces after commas.
  Do NOT repeat words from title or subtitle.
  Do NOT use plurals if singular is in title (Apple matches both).
  Pack with blue ocean and long-tail keywords.

FULL DESCRIPTION (4000 chars):
  First 252 chars appear before "more" tap — front-load keywords.
  2-3% keyword density for primary terms.
  Use bullet points for features (improves readability AND indexing).
  End with FAQ-style content for long-tail keyword capture.

3.4 Google Play Metadata Optimization¶

TITLE (50 chars max):
  More space than iOS. Use ALL of it. Every word is indexed.

SHORT DESCRIPTION (80 chars max):
  Google indexes this heavily. Front-load primary keywords.
  Include a number ("30 quiz modes") — numbers increase CTR.
  End with "Free!" if applicable — converts 10-15% better.

FULL DESCRIPTION (4000 chars):
  Google uses NLP to extract keywords — natural language works.
  Mention every game mode by name (each is a potential keyword).
  List all supported languages (increases discoverability).
  Include competitor-relevant terms naturally.
  Use line breaks and bullet points for scannability.

Step 4: UA Unit Economics Model¶

The payback model is the core economic framework:

CPI → Install → Retention Decay → ARPDAU × Retained Days = Cumulative Revenue
                                                                    │
                                                         Compare to CPI
                                                                    │
                                                    ┌───────────────┼───────────────┐
                                                    ▼               ▼               ▼
                                              7-Day Payback   14-Day Payback  30-Day Payback

4.1 Retention Decay Model¶

Use the standard exponential decay:

Retention(d) = D1_retention × e^(-λ × (d-1))

Where:
  D1_retention = Day-1 retention (typically 35-45% for casual/trivia)
  λ = decay constant (calibrate from D7 and D30 data)

Example (Quiz-Verse post-fix):
  D1 = 40%, D7 = 18%, D30 = 8%
  λ ≈ 0.115

4.2 Cumulative Revenue per Install¶

LTV(d) = Σ(i=0 to d) [Retention(i) × ARPDAU]

Example:
  ARPDAU = $0.15, D1 = 40%
  LTV(7) = $0.48
  LTV(14) = $0.63
  LTV(30) = $0.88
  LTV(90) = $1.32
  LTV(365) = $1.80

4.3 Payback Period Calculation¶

Payback Day = first d where LTV(d) ≥ CPI

If CPI = $0.30 → Payback ≈ Day 3 (Tier 1 country, scale aggressively)
If CPI = $0.80 → Payback ≈ Day 25 (Tier 2, steady growth)
If CPI = $2.00 → Payback ≈ Day 90+ (Tier 3, targeted only)

Step 5: ROAS-Ranked Country Matrix¶

Rank every targetable country by expected ROAS:

Ranking Formula:

ROAS_Score = (ARPDAU_country / CPI_country) × Language_Match_Multiplier × Market_Size_Factor

Where:
  Language_Match_Multiplier:
    1.5 if game has native localization for that country's language
    1.0 if English works (English-speaking or bilingual country)
    0.5 if no language match (add the language or skip)

  Market_Size_Factor:
    log10(daily_available_volume) / 5  (normalizes to 0-1)

Country Tiers:

Step 6: Ad Creative Briefs¶

For each cohort × platform × language combination:

Step 7: Generate Output Documents¶

The pipeline produces:

1. Structured JSON (marketing_audit.json) — Machine-readable, importable to dashboards
2. Markdown Report (marketing_audit.md) — Human-readable, versioned in git
3. PDF (optional) — Shareable with stakeholders
4. DOCX (optional) — Editable by marketing team

Document Sections (Output Schema)¶

The generated marketing plan contains these sections:

Section 1: Feature → Audience → Localization → Platform Pipeline¶

• Complete mapping of every game feature to its target audience
• Per-feature: languages that resonate, trusted download platform, content stickiness score

Section 2: Advanced ASO¶

• Current ASO score assessment (0-100)
• Competitor keyword gap analysis (must-win, blue ocean, long-tail)
• Optimized metadata per platform (iOS title/subtitle/keywords, Google Play title/short desc/full desc)
• Full keyword strategy per supported language with search volumes
• Competitor feature gap heatmap
• ASO execution playbook (phased, week-by-week)

Section 3: UA Unit Economics¶

• CPI by platform and cohort
• Retention → revenue accumulation model (D0 through D365)
• Payback period by CPI tier (7/14/30 day)
• CAC per paying user across platforms

Section 4: ROAS-Ranked Country Priority List¶

• Every targetable country ranked by expected ROAS
• Columns: language match, primary audience, CPI, ARPDAU, D7/D30 ROAS, LTV, rationale
• Tier classification (7-day / 14-day / 30+ day payback)
• Budget allocation by tier

Section 5: Ad Creative Briefs¶

• Per-tier creative strategy (volume, value, LTV optimized)
• Creative concepts per cohort × platform × language
• Refresh cadence and production budget

Section 6: Revenue Model¶

• ARPDAU variance by market tier (eCPM, IAP conversion, blended)
• Blended portfolio ARPDAU under different country mix scenarios
• Monthly revenue projections at various DAU levels

Section 7: Execution Roadmap¶

• Phase 1-4 timeline (Foundation, Expansion, Scale, Optimize)
• Weekly budget ramp
• KPI targets per phase
• Decision framework (scale/hold/kill thresholds)

Section 8: Seasonal Calendar¶

• Monthly ad spend multipliers
• Event-driven campaigns (holidays, exam seasons, cultural events)
• Cohort-specific seasonal hooks

Integration with Other IVX Skills¶

Hiro/Satori Integration¶

The marketing plan recommends specific backend configurations:

Satori Experiments (A/B Testing)¶

Satori Feature Flags¶

Satori Audiences¶

Checklist¶

• Game feature list collected (all modes, mechanics, differentiators)
• Economy data gathered (source/sink analysis, ARPDAU)
• Supported languages confirmed (each language unlocks countries)
• Competitor list defined (3-5 direct competitors)
• Content-Factory game_marketing_audit pipeline configured
• Pipeline executed and JSON/Markdown outputs reviewed
• ASO metadata deployed to App Store Connect and Google Play Console
• Localized store listings deployed for all supported languages
• App Preview Video recorded and uploaded
• In-app review prompt implemented
• Screenshots created (8 per platform, cohort-specific sets)
• Google Play Custom Store Listings created (1 per cohort)
• Satori experiments created for onboarding and pricing tests
• Feature flags created for seasonal/campaign toggles
• Tier 1 campaigns launched (7-day payback countries)
• D1/D7 retention monitored; kill/scale decisions applied
• Creative refresh scheduled (every 14-21 days)
• PDF/DOCX reports generated for stakeholder sharing