重塑企业客户流失预警系统：从成本黑洞到利润引擎的革命

// 客户流失预测模型
public class ChurnPredictionModel
{
    public decimal PredictChurnProbability(Customer customer)
    {
        // 特征工程：计算关键指标
        var usageDeclineRate = CalculateUsageDeclineRate(customer);
        var complaintFrequency = GetComplaintFrequency(customer);
        var paymentDelayTrend = AnalyzePaymentDelayTrend(customer);
        
        // 机器学习模型预测
        var churnProbability = ApplyMachineLearningModel(
            usageDeclineRate, 
            complaintFrequency, 
            paymentDelayTrend
        );
        
        return churnProbability;
    }
    
    private decimal ApplyMachineLearningModel(
        decimal usageDecline, 
        decimal complaintFreq, 
        decimal paymentDelay)
    {
        // 简化的模型计算逻辑
        // 实际应用中应使用训练好的机器学习模型
        var riskScore = usageDecline * 0.4m + 
                       complaintFreq * 0.3m + 
                       paymentDelay * 0.3m;
        
        return Math.Min(riskScore, 1.0m);
    }
}

// 干预效果跟踪与分析
class InterventionTracker {
  constructor() {
    this.interventionRecords = [];
  }
  
  // 记录干预结果
  recordIntervention(customerId, interventionType, success, feedback) {
    this.interventionRecords.push({
      customerId,
      interventionType,
      success,
      feedback,
      timestamp: new Date()
    });
  }
  
  // 分析干预效果
  analyzeEffectiveness() {
    const effectivenessByType = {};
    
    this.interventionRecords.forEach(record => {
      if (!effectivenessByType[record.interventionType]) {
        effectivenessByType[record.interventionType] = {
          total: 0,
          successful: 0
        };
      }
      
      effectivenessByType[record.interventionType].total++;
      if (record.success) {
        effectivenessByType[record.interventionType].successful++;
      }
    });
    
    return effectivenessByType;
  }
}