"""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 } }