The Problem
CCFish, a Cocos Creator 2.4.15 mobile game published across iOS, Telegram Mini Apps (TMA), and web platforms, faced a classic growth bottleneck: manual App Store Optimization (ASO). With hundreds of keywords to track across multiple app stores and regional markets, the marketing team spent upwards of 20 hours per week just researching keywords, writing descriptions, and updating metadata. Every new app store release or feature update meant rewriting localized descriptions from scratch. The result was inconsistency, missed keyword opportunities, and a ceiling on organic downloads that no amount of manual effort could break through.
The core challenge was not that the team lacked good writers. It was that ASO at scale is fundamentally a data problem, not a creative one. With keyword volumes in the hundreds, manual research could never keep pace with shifting search trends, algorithm updates, and competitor metadata changes. CCFish needed a system that could ingest keyword data, generate optimized metadata, and iterate faster than any human team could.
The Solution
The CCFish team built an LLM-powered marketing automation pipeline that generates App Store descriptions, keyword sets, and screenshot copy automatically. Instead of a human writer researching each keyword one by one, the system ingests a feature list, market positioning notes, and competitor data, then outputs fully formatted ASO metadata ready for review. The key insight was not to replace human judgment but to eliminate the repetitive, template-driven work that consumed most of the marketing team's time.
```python
Simplified pipeline entry point
from aso_pipeline import ASOEngine
engine = ASOEngine(
model="gpt-4o",
locale="en-US",
app_name="CCFish"
)
descriptions = engine.generate(
features=["multiplayer", "daily events", "leaderboards"],
keywords=["fishing game", "multiplayer fishing", "idle fishing"],
target_stores=["ios", "tma", "web"],
count=3
)
for desc in descriptions:
print(f"Title: {desc.title}")
print(f"Subtitle: {desc.subtitle}")
print(f"Description: {desc.body[:100]}...")
```
Architecture Overview
The entire pipeline runs on Cloudflare Workers with D1 as the key-value store for keyword rankings and metadata history. This architecture was chosen for three reasons: global low-latency execution near app store API endpoints, zero cold-start management (Workers are serverless), and D1's SQL-based queries for historical trend analysis.
| Component | Role | Technology |
|-----------|------|------------|
| Keyword Extractor | Parses competitor listings and app store search data | Cloudflare Worker + Jina AI embeddings |
| Description Generator | Produces localized app metadata | Cloudflare Worker + OpenAI API |
| Ranking Tracker | Polls app store keyword positions daily | Cloudflare Cron Trigger + D1 |
| A/B Test Runner | Deploys metadata variants to sandbox stores | Cloudflare Worker + internal API |
| Dashboard | Real-time ASO metrics and keyword heatmaps | Cloudflare Pages + D1 Views |
Implementation
Step 1: Keyword Extraction
The first step is automated keyword discovery. The pipeline scrapes competitor app listings from the iOS App Store and Google Play Store (where applicable), extracts high-volume search terms using TF-IDF scoring, and feeds the top candidates to the LLM for relevance filtering. The LLM scores each candidate on three axes: relevance to CCFish gameplay, search volume potential, and competitive difficulty.
```sql
-- D1 schema for keyword tracking
CREATE TABLE keyword_rankings (
id INTEGER PRIMARY KEY AUTOINCREMENT,
keyword TEXT NOT NULL,
store TEXT NOT NULL CHECK(store IN ('ios', 'tma', 'web')),
region TEXT NOT NULL DEFAULT 'US',
rank INTEGER,
volume INTEGER,
tracked_at TEXT NOT NULL DEFAULT (datetime('now')),
UNIQUE(keyword, store, region, tracked_at)
);
```
Step 2: Description Generation
Once keywords are identified, the LLM generates multiple description variants. Each variant targets a different keyword cluster. For example, a "multiplayer fishing" variant leads with social features, while a "relaxing fishing game" variant emphasizes casual play and stress relief. The prompt template includes the app's core features, target keyword list, character limits per store, and desired tone parameters.
```python
variants = [
{
"primary_keyword": "multiplayer fishing game",
"tone": "competitive",
"emphasis": "leaderboards, tournaments, chat"
},
{
"primary_keyword": "relaxing fishing game",
"tone": "casual",
"emphasis": "scenic graphics, idle mechanics, daily rewards"
},
]
```
Step 3: A/B Testing Loop
Each generated variant is deployed to a sandbox store listing for 48 hours. The A/B test runner compares impression-to-install conversion rates and keyword rank movement. Winning variants are promoted to production automatically; losers are archived with failure notes for the model to learn from in future generations. This creates a virtuous cycle where each iteration improves on the last.
Step 4: Continuous Optimization
Once per week, the pipeline re-runs keyword extraction, compares current rankings against the last 30 days of D1 data, and regenerates descriptions that have dropped below a configurable rank threshold. This closed-loop optimization means CCFish never sits on stale metadata. The pipeline also flags seasonal opportunities — for example, surfacing "summer fishing" keywords in June before competitors catch on.
Results
After six weeks of running the automated pipeline, CCFish saw measurable improvements across all tracked metrics:
| Metric | Before Pipeline | After Pipeline | Improvement |
|--------|----------------|----------------|-------------|
| Organic installs/week | 1,200 | 3,850 | +220% |
| Keywords in top 10 | 18 | 52 | +188% |
| Time spent on ASO/week | 20 hours | 3 hours | -85% |
| Description variants tested/month | 2 | 24 | +1,100% |
| Avg keyword rank (lower is better) | 34 | 12 | -65% |
The most dramatic improvement was in time savings. The marketing team went from spending the equivalent of two full workdays per week on ASO metadata to just three hours of review and approval. This freed up capacity for higher-value work: community management, influencer outreach, and feature planning.
Keyword ranking improvements were particularly strong in long-tail keywords. "Idle fishing game" jumped from rank 41 to 6 on the iOS App Store within three weeks, driven by precisely targeted description copy. The pipeline's ability to rapidly iterate on keyword-specific variants meant CCFish could dominate niche search terms that manual processes would never have prioritized.
Key Takeaways
1. **Automation doesn't mean losing control** — the LLM generates variants, but humans still review and approve. The bottleneck shifts from creation to curation, giving the team leverage without sacrificing quality.
2. **Serverless architecture (Workers + D1) keeps costs near zero** — the entire pipeline runs for less than $50/month, making it accessible for indie game teams with limited budgets.
3. **A/B testing at speed is the real differentiator** — generating 24 variants per month instead of 2 means CCFish can optimize for seasonal events, algorithm changes, and competitor moves in real time.
4. **LLMs excel at template-based localization** — the model handles language, tone, and keyword density far faster than a human translator, and can adapt to multiple store formats (iOS, TMA, web) in a single pass without context switching.
5. **Closed-loop feedback is essential** — without continuous re-ranking and keyword re-discovery, the pipeline would produce stale output. D1's time-series data makes this feedback loop possible with minimal engineering overhead.
Getting Started
If you're managing a multi-store mobile game and spending more than five hours per week on ASO, the CCFish approach is worth replicating. Start small: pick your top 20 keywords, write a simple LLM prompt that generates description variants targeting those keywords, and A/B test one variant per week. You don't need the full Cloudflare pipeline on day one — a single Python script and an OpenAI API key can prove the concept in an afternoon.
The automation gap in mobile game marketing is widening. Teams that adopt LLM-powered pipelines today will have a compounding advantage — more data, better metadata, higher rankings — that manual-only teams simply cannot match. CCFish is a case study in how indie studios can punch above their weight class with the right automation architecture.