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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()) |