How Can AI Help Airlines Listen Better

Airlines have traditionally used metrics like NPS and SCAT to assess customer service, but these often miss the complexities of customer feedback. With Large Language Models (LLMs), AI can analyze vast amounts of customer interactions from various channels, uncovering specific concerns and trends that generic categories overlook. AI enables more nuanced categorization and real-time monitoring at service touchpoints, helping airlines respond proactively. However, survey responses tend to be polarized, risking skewed AI-driven insights. To address this, airlines should supplement AI analytics with targeted outreach to gather more balanced and representative customer feedback for accurate decision-making.

航空公司传统上使用净推荐值（NPS）和客户满意度（CSAT）等指标来评估客户服务，但这些指标往往难以捕捉客户反馈的复杂性。借助大语言模型（LLM），人工智能可以分析来自各个渠道的海量客户互动数据，从而发现常规分类容易忽略的具体痛点和趋势。AI 能够实现更细致的分类以及服务接触点的实时监控，帮助航空公司做出预警性响应。然而，问卷调研的回复往往存在两极分化，这可能导致 AI 驱动的洞察产生偏差。为了解决这一问题，航空公司应当在 AI 分析的基础上，辅以针对性的主动外联，以收集更平衡、更具代表性的客户反馈，从而支持精准决策。