Upload core/monetization.py with huggingface_hub

core/monetization.py

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+import asyncio
+from datetime import datetime
+from typing import Dict, List
+from core.models import Task
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class MonetizationEngine:
+    """
+    Monetization Engine for AutoSEO - handles all revenue generation logic
+    """
+    
+    def __init__(self):
+        self.subscription_tiers = {
+            "starter": {
+                "price": 299,
+                "features": ["basic_seo", "10_content_pieces", "monthly_report"],
+                "cost_per_month": 50  # Estimated cost for resources
+            },
+            "professional": {
+                "price": 799,
+                "features": ["full_seo", "30_content_pieces", "link_building", "weekly_report"],
+                "cost_per_month": 120
+            },
+            "enterprise": {
+                "price": 1499,
+                "features": ["advanced_seo", "100_content_pieces", "white_label", "priority_support"],
+                "cost_per_month": 250
+            }
+        }
+        
+        self.customers = []
+        self.revenue_data = {
+            "monthly_recurring_revenue": 0,
+            "performance_bonus_revenue": 0,
+            "one_time_revenue": 0,
+            "total_revenue": 0
+        }
+        
+        self.pricing_strategies = []
+        self.customer_ltv = {}
+        
+    async def calculate_mrr(self) -> float:
+        """Calculate Monthly Recurring Revenue"""
+        mrr = 0
+        for customer in self.customers:
+            tier = customer.get('tier', 'starter')
+            mrr += self.subscription_tiers[tier]['price']
+        self.revenue_data['monthly_recurring_revenue'] = mrr
+        return mrr
+    
+    async def calculate_customer_acquisition_cost(self) -> float:
+        """Calculate average customer acquisition cost"""
+        if not self.customers:
+            return 0
+        
+        # Simulated CAC calculation
+        total_acquisition_cost = len(self.customers) * 150  # $150 per customer acquisition
+        cac = total_acquisition_cost / len(self.customers)
+        return cac
+    
+    async def calculate_customer_ltv(self, customer_id: str) -> float:
+        """Calculate lifetime value for a specific customer"""
+        # Simulate LTV calculation based on tier and tenure
+        customer = next((c for c in self.customers if c['id'] == customer_id), None)
+        if not customer:
+            return 0
+            
+        tier = customer.get('tier', 'starter')
+        monthly_value = self.subscription_tiers[tier]['price']
+        
+        # Estimate tenure in months (could be based on actual data)
+        estimated_tenure_months = 12  # Average tenure assumption
+        
+        ltv = monthly_value * estimated_tenure_months
+        self.customer_ltv[customer_id] = ltv
+        return ltv
+    
+    async def calculate_roi_for_customer(self, customer_id: str) -> Dict[str, float]:
+        """Calculate ROI for a specific customer's SEO investment"""
+        # Simulate ROI calculation based on traffic and conversion improvements
+        roi_data = {
+            "investment": self.subscription_tiers.get("professional", {}).get("price", 799) * 12,  # Annual subscription cost
+            "traffic_increase_value": 25000,  # Estimated value of traffic increase
+            "conversion_increase_value": 15000,  # Estimated value of conversion improvement
+            "total_return": 40000,
+            "roi_percentage": 333  # (40000-799*12)/(799*12)*100
+        }
+        return roi_data
+    
+    async def generate_performance_bonus(self, customer_id: str, performance_metrics: Dict) -> float:
+        """Generate performance bonus based on results achieved"""
+        # Calculate bonus based on traffic, ranking, or revenue improvements
+        bonus = 0
+        
+        # Example: 10% of revenue increase as bonus
+        revenue_increase = performance_metrics.get("revenue_increase", 0)
+        bonus = revenue_increase * 0.10
+        
+        self.revenue_data['performance_bonus_revenue'] += bonus
+        return bonus
+    
+    async def calculate_white_label_licensing_revenue(self, licenses_sold: int) -> float:
+        """Calculate revenue from white-label licensing"""
+        price_per_license = 4999  # Annual license fee
+        revenue = licenses_sold * price_per_license
+        self.revenue_data['one_time_revenue'] += revenue
+        return revenue
+    
+    async def calculate_niche_site_revenue(self, sites_sold: int) -> float:
+        """Calculate revenue from selling niche authority sites"""
+        avg_site_value = 25000  # Average value of a niche site
+        revenue = sites_sold * avg_site_value
+        self.revenue_data['one_time_revenue'] += revenue
+        return revenue
+    
+    async def calculate_affiliate_revenue(self, referrals: int) -> float:
+        """Calculate affiliate revenue from tool/service referrals"""
+        commission_per_referral = 50  # Average commission per referral
+        revenue = referrals * commission_per_referral
+        self.revenue_data['one_time_revenue'] += revenue
+        return revenue
+    
+    async def get_revenue_projection(self, months: int = 12) -> Dict[str, List[float]]:
+        """Project revenue for the next N months"""
+        current_mrr = await self.calculate_mrr()
+        
+        # Simulate growth projections
+        projected_revenue = {
+            "mrr": [],
+            "cumulative_revenue": []
+        }
+        
+        cumulative = 0
+        for month in range(1, months + 1):
+            # Simulate 10% monthly growth
+            projected_mrr = current_mrr * (1.10 ** (month - 1))
+            projected_revenue["mrr"].append(projected_mrr)
+            
+            cumulative += projected_mrr
+            projected_revenue["cumulative_revenue"].append(cumulative)
+        
+        return projected_revenue
+    
+    async def add_customer(self, customer_data: Dict):
+        """Add a new customer to the system"""
+        customer = {
+            "id": f"cust_{len(self.customers) + 1}",
+            "name": customer_data.get("name", "Unknown"),
+            "tier": customer_data.get("tier", "starter"),
+            "signup_date": datetime.now(),
+            "status": "active"
+        }
+        self.customers.append(customer)
+        logger.info(f"Added new customer: {customer['name']} ({customer['tier']} tier)")
+    
+    async def process_billing_cycle(self):
+        """Process monthly billing cycle"""
+        logger.info("Processing monthly billing cycle...")
+        
+        # Calculate MRR
+        mrr = await self.calculate_mrr()
+        
+        # Calculate costs
+        total_costs = 0
+        for customer in self.customers:
+            tier = customer.get('tier', 'starter')
+            total_costs += self.subscription_tiers[tier]['cost_per_month']
+        
+        # Update total revenue
+        self.revenue_data['total_revenue'] = mrr + self.revenue_data['performance_bonus_revenue'] + self.revenue_data['one_time_revenue']
+        
+        logger.info(f"Billing cycle complete - MRR: ${mrr}, Costs: ${total_costs}, Net Revenue: ${mrr - total_costs}")
+    
+    async def get_monetization_dashboard(self) -> Dict:
+        """Get comprehensive monetization dashboard data"""
+        mrr = await self.calculate_mrr()
+        cac = await self.calculate_customer_acquisition_cost()
+        
+        dashboard_data = {
+            "revenue_summary": self.revenue_data,
+            "customer_metrics": {
+                "total_customers": len(self.customers),
+                "mrr": mrr,
+                "arpu": mrr / len(self.customers) if self.customers else 0,
+                "cac": cac,
+                "ltv_cac_ratio": (await self.calculate_customer_ltv(self.customers[0]['id']) / cac) if self.customers and cac > 0 else 0
+            },
+            "projection_12_months": await self.get_revenue_projection(12),
+            "subscription_breakdown": {
+                tier: len([c for c in self.customers if c.get('tier') == tier]) 
+                for tier in self.subscription_tiers.keys()
+            }
+        }
+        
+        return dashboard_data
+
+
+# Example usage and testing
+async def test_monetization_engine():
+    """Test the monetization engine"""
+    engine = MonetizationEngine()
+    
+    # Add sample customers
+    await engine.add_customer({"name": "Acme Corp", "tier": "professional"})
+    await engine.add_customer({"name": "Globex Inc", "tier": "enterprise"})
+    await engine.add_customer({"name": "Wayne Enterprises", "tier": "starter"})
+    
+    # Process billing
+    await engine.process_billing_cycle()
+    
+    # Generate performance bonus
+    perf_metrics = {"revenue_increase": 50000}
+    bonus = await engine.generate_performance_bonus("cust_1", perf_metrics)
+    print(f"Performance bonus: ${bonus}")
+    
+    # Calculate white label revenue
+    wl_revenue = await engine.calculate_white_label_licensing_revenue(3)
+    print(f"White label revenue: ${wl_revenue}")
+    
+    # Get dashboard
+    dashboard = await engine.get_monetization_dashboard()
+    print("\nMonetization Dashboard:")
+    print(f"MRR: ${dashboard['customer_metrics']['mrr']}")
+    print(f"Total Customers: {dashboard['customer_metrics']['total_customers']}")
+    print(f"Subscription Breakdown: {dashboard['subscription_breakdown']}")
+    
+    return engine
+
+
+if __name__ == "__main__":
+    # Run test
+    asyncio.run(test_monetization_engine())