用分布复制法精准预测未评分客户的满意度。
Predicting Customer Satisfaction by Replicating the Survey Response Distribution
- 通过复现调查响应分布，让预测满意度更贴近真实数据
- 在多个呼叫中心实测中，预测均值与实际调查均值偏差<1%
- 适合需避免评分偏移的客服系统和持续更新模型场景
许多呼叫中心将客户满意度（CSAT）作为关键绩效指标。然而，仅有部分客户在通话后完成满意度调查，导致平均CSAT值存在偏差且不准确，错失辅导、跟进与纠错机会。因此，预测未参与调查客户的满意度具有重要价值。鉴于CSAT是高度监控的指标，预测平均值的偏差必须最小化。本文提出一种方法，使预测的客户满意度（pCSAT）在具备足够数据的生产环境中，能准确复现每个呼叫中心的调查响应分布。该方法可推广至多种多分类问题，提升类别平衡性，并在模型更新时保持分布稳定。
原文摘要 · Abstract (English)
For many call centers, customer satisfaction (CSAT) is a key performance indicator (KPI). However, only a fraction of customers take the CSAT survey after the call, leading to a biased and inaccurate average CSAT value, and missed opportunities for coaching, follow-up, and rectification. Therefore, call centers can benefit from a model predicting customer satisfaction on calls where the customer did not complete the survey. Given that CSAT is a closely monitored KPI, it is critical to minimize any bias in the average predicted CSAT (pCSAT). In this paper, we introduce a method such that predicted CSAT (pCSAT) scores accurately replicate the distribution of survey CSAT responses for every call center with sufficient data in a live production environment. The method can be applied to many multiclass classification problems to improve the class balance and minimize its changes upon model updates.
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