电商行业智能客服工单系统：如何适配大促高峰与售后维权场景？

// 工单优先级自动分类算法示例
function classifyTicketPriority(content, customerValue) {
  const urgentKeywords = ['退款', '投诉', '未收到货', '质量問題'];
  const highPriorityKeywords = ['换货', '发票', '优惠券'];
  
  let priority = 'normal';
  const contentLower = content.toLowerCase();
  
  if (urgentKeywords.some(keyword => contentLower.includes(keyword))) {
    priority = 'urgent';
  } else if (highPriorityKeywords.some(keyword => contentLower.includes(keyword))) {
    priority = 'high';
  }
  
  // 高价值客户自动提升优先级
  if (customerValue === 'high' && priority === 'normal') {
    priority = 'high';
  }
  
  return priority;
}

// 售后工单自动处理流程示例
public async Task ProcessAfterSalesTicket(Ticket ticket)
{
    // 根据工单类型自动执行相应流程
    switch (ticket.Type)
    {
        case TicketType.Refund:
            await ProcessRefund(ticket);
            break;
        case TicketType.Exchange:
            await ProcessExchange(ticket);
            break;
        case TicketType.Complaint:
            await ProcessComplaint(ticket);
            break;
        default:
            await ProcessGeneralTicket(ticket);
            break;
    }
    
    // 自动更新工单状态并通知客户
    ticket.Status = TicketStatus.Processing;
    await SendNotification(ticket.CustomerId, 
        "您的售后申请已受理，正在处理中");
    
    return Ok();
}

-- 分析大促期间工单处理效率的SQL查询示例
SELECT 
    DATE_FORMAT(created_at, '%Y-%m-%d %H:00') AS hour_period,
    COUNT(*) AS ticket_count,
    AVG(TIMESTAMPDIFF(MINUTE, created_at, resolved_at)) AS avg_resolution_time,
    category,
    priority
FROM tickets
WHERE created_at >= '2023-11-01' 
    AND created_at <= '2023-11-15'
GROUP BY hour_period, category, priority
ORDER BY hour_period, ticket_count DESC;