2025年不可错过的客户流失预警系统秘诀,不踩坑的实用技巧

// 客户行为特征计算示例
public class CustomerBehaviorFeatures
{
    public decimal CalculateEngagementScore(DateTime startDate, DateTime endDate)
    {
        // 计算客户在指定时间段内的活跃度得分
        var loginCount = GetLoginCount(startDate, endDate);
        var featureUsage = GetFeatureUsageRate(startDate, endDate);
        var sessionDuration = GetAverageSessionDuration(startDate, endDate);
        
        return (loginCount * 0.4m) + (featureUsage * 0.4m) + (sessionDuration * 0.2m);
    }
    
    public decimal CalculateSatisfactionScore()
    {
        // 基于客户服务交互计算满意度得分
        var complaintCount = GetComplaintCount();
        var praiseCount = GetPraiseCount();
        var serviceRating = GetAverageServiceRating();
        
        return (serviceRating * 0.5m) + 
               ((praiseCount - complaintCount) * 0.3m) + 
               (GetResponseTimeScore() * 0.2m);
    }
}

// 预警模型特征预处理示例
class FeaturePreprocessor {
  constructor() {
    this.scalers = new Map();
  }
  
  // 数值特征标准化
  standardizeFeatures(features) {
    const standardized = {};
    for (const [key, value] of Object.entries(features)) {
      if (typeof value === 'number') {
        const { mean, std } = this.scalers.get(key);
        standardized[key] = (value - mean) / std;
      } else {
        standardized[key] = value;
      }
    }
    return standardized;
  }
  
  // 分类特征编码
  encodeCategoricalFeatures(features, categoricalColumns) {
    const encoded = { ...features };
    categoricalColumns.forEach(column => {
      if (encoded[column]) {
        encoded[`${column}_encoded`] = this.labelEncoder.encode(column, encoded[column]);
        delete encoded[column];
      }
    });
    return encoded;
  }
}