# 流失预测集成模型
churn_prediction_ensemble = {
    "gradient_boosting": XGBoost(n_estimators=500, max_depth=8),
    "random_forest": RandomForest(n_estimators=300, min_samples_split=5),
    "neural_network": MLP(hidden_layers=(256, 128, 64), dropout=0.3),
    "survival_analysis": CoxProportionalHazards(),
    "time_series": LSTM(sequence_length=90, units=128)
}

# 集成权重（基于历史表现动态调整）
ensemble_weights = {
    "gradient_boosting": 0.30,
    "random_forest": 0.25,
    "neural_network": 0.20,
    "survival_analysis": 0.15,
    "time_series": 0.10
}

# 预测结果
churn_probability = sum(
    model.predict(customer_features) * weight 
    for model, weight in zip(models, ensemble_weights.values())
)

product_adoption_score = {
    "feature_breadth": calculate_feature_breadth(customer),  # 使用了多少功能
    "feature_depth": calculate_feature_depth(customer),     # 功能使用深度
    "user_penetration": calculate_user_penetration(customer), # 用户渗透率
    "advanced_usage": calculate_advanced_usage(customer),    # 高级功能使用
    "integration_usage": calculate_integration_usage(customer) # 集成使用情况
}

# 综合评分
product_adoption = weighted_average(product_adoption_score, weights)

def optimal_intervention_timing(customer):
    # 分析客户的行为模式
    activity_patterns = analyze_activity_patterns(customer)
    
    # 识别最佳联系时间
    best_time = predict_optimal_contact_time(customer, activity_patterns)
    
    # 评估干预紧迫性
    urgency = calculate_intervention_urgency(customer)
    
    # 考虑客户偏好
    preferences = get_communication_preferences(customer)
    
    # 综合决策
    if urgency == "high":
        return "immediate"
    elif urgency == "medium" and preferences.allows_scheduled:
        return best_time
    else:
        return "next_business_day"

def predict_intervention_effectiveness(customer, intervention_options):
    results = []
    
    for intervention in intervention_options:
        # 基于历史数据预测成功率
        success_probability = predict_success_rate(customer, intervention)
        
        # 预测客户响应时间
        response_time = predict_response_time(customer, intervention)
        
        # 预测健康度改善幅度
        health_improvement = predict_health_improvement(customer, intervention)
        
        # 计算预期 ROI
        expected_roi = calculate_intervention_roi(
            success_probability, 
            health_improvement, 
            intervention.cost
        )
        
        results.append({
            "intervention": intervention,
            "success_probability": success_probability,
            "response_time": response_time,
            "health_improvement": health_improvement,
            "expected_roi": expected_roi
        })
    
    # 返回最佳干预策略
    return max(results, key=lambda x: x["expected_roi"])

def predict_feature_needs(customer):
    # 分析客户当前使用情况
    current_usage = analyze_current_usage(customer)
    
    # 识别使用模式
    usage_patterns = identify_usage_patterns(customer)
    
    # 参考类似客户的成长路径
    similar_customers = find_similar_customers(customer)
    growth_paths = analyze_growth_paths(similar_customers)
    
    # 预测未来需求
    predicted_needs = []
    for feature in all_features:
        if feature not in current_usage:
            need_probability = calculate_need_probability(
                customer, 
                feature, 
                usage_patterns, 
                growth_paths
            )
            if need_probability > 0.7:
                predicted_needs.append({
                    "feature": feature,
                    "probability": need_probability,
                    "timing": predict_adoption_timing(customer, feature),
                    "business_value": estimate_business_value(customer, feature)
                })
    
    return predicted_needs

def identify_upsell_opportunities(customer):
    opportunities = []
    
    # 分析使用限制
    if customer.approaching_limits():
        opportunities.append({
            "type": "capacity_upgrade",
            "reason": f"即将达到 {customer.current_plan} 的限制",
            "recommendation": recommend_next_plan(customer),
            "timing": "immediate"
        })
    
    # 分析高级功能使用
    advanced_features = customer.using_advanced_features()
    if len(advanced_features) > 3:
        opportunities.append({
            "type": "premium_upgrade",
            "reason": "已在使用多个高级功能",
            "recommendation": "Premium Plan",
            "timing": "next_renewal"
        })
    
    # 分析业务增长
    if customer.business_growing():
        opportunities.append({
            "type": "enterprise_upgrade",
            "reason": "业务快速增长",
            "recommendation": "Enterprise Plan",
            "timing": "quarterly_review"
        })
    
    return opportunities

def optimize_resource_allocation(customers):
    # 计算每个客户的终身价值
    ltv_scores = {customer: calculate_ltv(customer) for customer in customers}
    
    # 计算每个客户的风险
    risk_scores = {customer: calculate_risk(customer) for customer in customers}
    
    # 计算每个客户的成长潜力
    growth_scores = {customer: calculate_growth_potential(customer) for customer in customers}
    
    # 综合评分
    priority_scores = {}
    for customer in customers:
        priority_scores[customer] = (
            ltv_scores[customer] * 0.4 +
            risk_scores[customer] * 0.3 +
            growth_scores[customer] * 0.3
        )
    
    # 根据优先级分配资源
    sorted_customers = sorted(priority_scores.items(), key=lambda x: x[1], reverse=True)
    
    allocation = {
        "high_touch": sorted_customers[:10],      # 前 10% 高接触
        "medium_touch": sorted_customers[10:50],   # 10-50% 中等接触
        "low_touch": sorted_customers[50:80],      # 50-80% 低接触
        "tech_touch": sorted_customers[80:]        # 后 20% 技术接触
    }
    
    return allocation

def calculate_intervention_roi(intervention):
    # 计算干预成本
    cost = (
        intervention.time_cost +
        intervention.resource_cost +
        intervention.opportunity_cost
    )
    
    # 计算干预收益
    benefit = (
        intervention.retention_value +
        intervention.upsell_value +
        intervention.referral_value +
        intervention.satisfaction_value
    )
    
    # 计算 ROI
    roi = (benefit - cost) / cost * 100
    
    return {
        "cost": cost,
        "benefit": benefit,
        "roi": roi,
        "payback_period": calculate_payback_period(cost, benefit)
    }