Predicting Customer Satisfaction by Replicating the Survey Response Distribution
This addresses the need for unbiased CSAT predictions in call centers to improve coaching and performance monitoring, though it appears incremental as it adapts existing methods to a specific domain.
The paper tackles the problem of biased customer satisfaction (CSAT) predictions in call centers due to low survey response rates by introducing a method that replicates the survey response distribution, enabling accurate average predicted CSAT scores for calls without surveys.
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.