What can be learned from satisfaction assessments?

N. Cohen, Simran Lamba, P. Reddy
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Abstract

Companies survey their customers to measure their satisfaction levels with the company and its services. The received responses are crucial as they allow companies to assess their respective performances and find ways to make needed improvements. This study focuses on the non-systematic bias that arises when customers assign numerical values in ordinal surveys. Using real customer satisfaction survey data of a large retail bank, we show that the common practice of segmenting ordinal survey responses into uneven segments limit the value that can be extracted from the data. We then show that it is possible to assess the magnitude of the irreducible error under simple assumptions, even in real surveys, and place the achievable modeling goal in perspective. We finish the study by suggesting that a thoughtful survey design, which uses either a careful binning strategy or proper calibration, can reduce the compounding non-systematic error even in elaborated ordinal surveys. A possible application of the calibration method we propose is efficiently conducting targeted surveys using active learning.
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我们可以从满意度评估中学到什么?
公司通过调查顾客来衡量他们对公司及其服务的满意程度。收到的反馈是至关重要的,因为它们使公司能够评估各自的表现,并找到做出必要改进的方法。本研究的重点是非系统偏差,产生当客户分配数值在顺序调查。利用一家大型零售银行的真实客户满意度调查数据,我们表明,将有序调查反馈分割为不均匀细分的常见做法限制了可以从数据中提取的价值。然后,我们表明,即使在真实的调查中,也可以在简单的假设下评估不可约误差的大小,并将可实现的建模目标放在正确的角度。我们通过建议一个深思熟虑的调查设计,使用仔细的分组策略或适当的校准,可以减少复合非系统误差,即使在精心设计的顺序调查中也是如此。我们提出的校准方法的一个可能应用是利用主动学习有效地进行有针对性的调查。
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