Predicting Customer Satisfaction by Replicating the Survey Response Distribution

TL;DR

This paper presents a method to accurately predict customer satisfaction scores in call centers, replicating the survey response distribution to address bias from incomplete survey data and improve KPI reliability.

Contribution

The paper introduces a novel approach for predicting CSAT scores that closely replicate the actual response distribution, reducing bias and enhancing KPI accuracy in live environments.

Findings

Predicted CSAT scores match survey response distribution.

Method reduces bias in average CSAT estimates.

Applicable to multiclass classification problems.

Abstract

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.