revenue-optimizer

# Revenue Optimizer

Build revenue features and monetization systems. Analyze existing codebases to understand features, calculate costs, and create data-driven pricing strategies.

## Workflow

1. **Discover** - Scan codebase for features, services, and integrations
2. **Cost Analysis** - Calculate per-user and per-feature costs from services
3. **Design** - Create pricing tiers based on value + cost data
4. **Implement** - Build payment integration, pricing logic, and checkout flows
5. **Optimize** - Add conversion optimization and revenue tracking

## Feature Discovery

Scan codebase to build feature inventory:

```
Feature Discovery Process:
1. Scan routes/endpoints → identify user-facing features
2. Scan components/pages → map UI features
3. Scan service integrations → identify cost-generating features
4. Scan database models → understand data entities
5. Cross-reference → map features to their cost drivers
```

Look for these patterns:
- **Routes/Controllers**: Each endpoint = potential feature
- **React/Vue components**: Feature-specific UI modules
- **Service clients**: AWS SDK, OpenAI, Stripe, Twilio, etc.
- **Background jobs**: Compute-intensive operations
- **Storage operations**: S3, database writes, file uploads

Example feature inventory output:
```
Features Discovered:
├── Core Features (low cost)
│   ├── User authentication (Cognito/Auth0)
│   ├── Dashboard views (read-only)
│   └── Basic CRUD operations
├── Premium Features (medium cost)
│   ├── PDF export (uses Puppeteer/Lambda)
│   ├── Email notifications (SendGrid)
│   └── File storage (S3)
└── High-Value Features (high cost)
    ├── AI analysis (OpenAI API)
    ├── Video processing (FFmpeg/Lambda)
    └── Real-time sync (WebSockets)
```

## Cost Analysis

Analyze services to calculate true costs per user/feature. See [references/cost-analysis.md](references/cost-analysis.md) for detailed patterns.

### Service Detection

Scan for these cost sources:
- **Config files**: `.env`, `config/`, secrets