使用情绪评分来评估客户服务质量

How AI-based Sentiment Models Complement Net Promoter Score

基于人工智能的情感模型如何补充净促销员评分的不足

Net Promoter Score (NPS) is a well-accepted measurement of customer satisfaction in most customer-facing industries. We leverage NPS at Airbnb to help measure how well we serve our community of guests and hosts through our customer service. But NPS has two major drawbacks: 1) NPS is sparse, given only a fraction of users respond to the survey, and 2) NPS is slow. It takes at least a week for results to show up. Airbnb uses A/B testing heavily across our core products and customer service offerings. In the A/B testing world, the longer it takes to see results and interpret experiments, the longer it takes to iterate on the quality of our customer service. This is why we needed a much more sensitive and robust metric.

在大多数面向客户的行业中，净促销员评分（NPS）是一个公认的衡量客户满意度的标准。我们在Airbnb利用NPS来帮助衡量我们通过客户服务为我们的客人和房东社区提供的服务如何。但是NPS有两个主要的缺点。1）NPS是稀疏的，因为只有一小部分用户对调查作出回应，和2）NPS是缓慢的。至少需要一个星期的时间才能显示出结果。Airbnb在我们的核心产品和客户服务产品中大量使用A/B测试。在A/B测试领域，看到结果和解释实验的时间越长，我们迭代客户服务质量的时间就越长。这就是为什么我们需要一个更加敏感和强大的指标。

To address these limitations, Airbnb has developed an AI-based sentiment model to complement NPS. Sentiment models process messages users send to customer support (CS) representatives to extract signals reflecting users’ sentiment. Compared to NPS, the sentiment score has the following advantages:

为了解决这些限制，Airbnb开发了一个基于人工智能的情感模型来补充NPS。情感模型处理用户发送给客户支持（CS）代表的信息，提取反映用户情感的信号。与NPS相比，情感评分有以下优势。

Higher coverage: we are not limited to those who submit a survey, and therefore more users in a given experiment register a value for this metric;
更高的覆盖率：我们并不局限于那些提交调查的人，因此在一个特定的实验中，更多的用户注册了这个指标的值。
Better sensitivity: it takes much less time to reach statistical significance while running an experiment;
更好的敏感性：在运行一个实验时，达到统计学意义所需的时间要少得多。
Causal relationship with long term customer loyalty: we can ‘translate’ user sentiment scores into long term business values.
与长期客户忠诚度的因果关系：我们可以将用户的情绪分数 "转化 "为长期的商业价值。

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