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"""Advanced monetization strategies for venture optimization."""
import logging
from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
import json
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import numpy as np
from collections import defaultdict
@dataclass
class MonetizationModel:
"""Monetization model configuration."""
name: str
type: str
pricing_tiers: List[Dict[str, Any]]
features: List[str]
constraints: List[str]
metrics: Dict[str, float]
@dataclass
class RevenueStream:
"""Revenue stream configuration."""
name: str
type: str
volume: float
unit_economics: Dict[str, float]
growth_rate: float
churn_rate: float
class MonetizationOptimizer:
"""
Advanced monetization optimization that:
1. Designs pricing models
2. Optimizes revenue streams
3. Maximizes customer value
4. Reduces churn
5. Increases lifetime value
"""
def __init__(self):
self.models: Dict[str, MonetizationModel] = {}
self.streams: Dict[str, RevenueStream] = {}
async def optimize_monetization(self,
venture_type: str,
context: Dict[str, Any]) -> Dict[str, Any]:
"""Optimize monetization strategy."""
try:
# Design models
models = await self._design_models(venture_type, context)
# Optimize pricing
pricing = await self._optimize_pricing(models, context)
# Revenue optimization
revenue = await self._optimize_revenue(pricing, context)
# Value optimization
value = await self._optimize_value(revenue, context)
# Performance projections
projections = await self._project_performance(value, context)
return {
"success": projections["annual_revenue"] >= 1_000_000,
"models": models,
"pricing": pricing,
"revenue": revenue,
"value": value,
"projections": projections
}
except Exception as e:
logging.error(f"Error in monetization optimization: {str(e)}")
return {"success": False, "error": str(e)}
async def _design_models(self,
venture_type: str,
context: Dict[str, Any]) -> Dict[str, Any]:
"""Design monetization models."""
prompt = f"""
Design monetization models:
Venture: {venture_type}
Context: {json.dumps(context)}
Design models for:
1. Subscription tiers
2. Usage-based pricing
3. Hybrid models
4. Enterprise pricing
5. Marketplace fees
Format as:
[Model1]
Name: ...
Type: ...
Tiers: ...
Features: ...
Constraints: ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_model_design(response["answer"])
async def _optimize_pricing(self,
models: Dict[str, Any],
context: Dict[str, Any]) -> Dict[str, Any]:
"""Optimize pricing strategy."""
prompt = f"""
Optimize pricing strategy:
Models: {json.dumps(models)}
Context: {json.dumps(context)}
Optimize for:
1. Market positioning
2. Value perception
3. Competitive dynamics
4. Customer segments
5. Growth potential
Format as:
[Strategy1]
Model: ...
Positioning: ...
Value_Props: ...
Segments: ...
Growth: ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_pricing_strategy(response["answer"])
async def _optimize_revenue(self,
pricing: Dict[str, Any],
context: Dict[str, Any]) -> Dict[str, Any]:
"""Optimize revenue streams."""
prompt = f"""
Optimize revenue streams:
Pricing: {json.dumps(pricing)}
Context: {json.dumps(context)}
Optimize for:
1. Revenue mix
2. Growth drivers
3. Retention factors
4. Expansion potential
5. Risk mitigation
Format as:
[Stream1]
Type: ...
Drivers: ...
Retention: ...
Expansion: ...
Risks: ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_revenue_optimization(response["answer"])
async def _optimize_value(self,
revenue: Dict[str, Any],
context: Dict[str, Any]) -> Dict[str, Any]:
"""Optimize customer value."""
prompt = f"""
Optimize customer value:
Revenue: {json.dumps(revenue)}
Context: {json.dumps(context)}
Optimize for:
1. Acquisition cost
2. Lifetime value
3. Churn reduction
4. Upsell potential
5. Network effects
Format as:
[Value1]
Metric: ...
Strategy: ...
Potential: ...
Actions: ...
Timeline: ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_value_optimization(response["answer"])
async def _project_performance(self,
value: Dict[str, Any],
context: Dict[str, Any]) -> Dict[str, Any]:
"""Project monetization performance."""
prompt = f"""
Project performance:
Value: {json.dumps(value)}
Context: {json.dumps(context)}
Project:
1. Revenue growth
2. Customer metrics
3. Unit economics
4. Profitability
5. Scale effects
Format as:
[Projections]
Revenue: ...
Metrics: ...
Economics: ...
Profit: ...
Scale: ...
"""
response = await context["groq_api"].predict(prompt)
return self._parse_performance_projections(response["answer"])
def _calculate_revenue_potential(self, model: MonetizationModel) -> float:
"""Calculate revenue potential for model."""
base_potential = sum(
tier.get("price", 0) * tier.get("volume", 0)
for tier in model.pricing_tiers
)
growth_factor = 1.0 + (model.metrics.get("growth_rate", 0) / 100)
retention_factor = 1.0 - (model.metrics.get("churn_rate", 0) / 100)
return base_potential * growth_factor * retention_factor
def _calculate_customer_ltv(self, stream: RevenueStream) -> float:
"""Calculate customer lifetime value."""
monthly_revenue = stream.volume * stream.unit_economics.get("arpu", 0)
churn_rate = stream.churn_rate / 100
discount_rate = 0.1 # 10% annual discount rate
if churn_rate > 0:
ltv = monthly_revenue / churn_rate
else:
ltv = monthly_revenue * 12 # Assume 1 year if no churn
return ltv / (1 + discount_rate)
def get_monetization_metrics(self) -> Dict[str, Any]:
"""Get comprehensive monetization metrics."""
return {
"model_metrics": {
model.name: {
"revenue_potential": self._calculate_revenue_potential(model),
"tier_count": len(model.pricing_tiers),
"feature_count": len(model.features),
"constraint_count": len(model.constraints)
}
for model in self.models.values()
},
"stream_metrics": {
stream.name: {
"monthly_revenue": stream.volume * stream.unit_economics.get("arpu", 0),
"ltv": self._calculate_customer_ltv(stream),
"growth_rate": stream.growth_rate,
"churn_rate": stream.churn_rate
}
for stream in self.streams.values()
},
"aggregate_metrics": {
"total_revenue_potential": sum(
self._calculate_revenue_potential(model)
for model in self.models.values()
),
"average_ltv": np.mean([
self._calculate_customer_ltv(stream)
for stream in self.streams.values()
]) if self.streams else 0,
"weighted_growth_rate": np.average(
[stream.growth_rate for stream in self.streams.values()],
weights=[stream.volume for stream in self.streams.values()]
) if self.streams else 0
}
}
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