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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 | |
| 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] | |
| 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 | |
| } | |
| } | |