Modeling Customer Experience in a Contact Center through Process Log Mining

Abstract

The use of data mining and modeling methods in service industry is a promising avenue for optimizing current processes in a targeted manner, ultimately reducing costs and improving customer experience. However, the introduction of such tools in already established pipelines often must adapt to the way data is sampled and to its content. In this study, we tackle the challenge of characterizing and predicting customer experience having available only process log data with time-stamp information, without any ground truth feedback from the customers. As a case study, we consider the context of a contact center managed by TeleWare and analyze phone call logs relative to a two months span. We develop an approach to interpret the phone call process events registered in the logs and infer concrete points of improvement in the service management. Our approach is based on latent tree modeling and multi-class Naïve Bayes classification, which jointly allow us to infer a spectrum of customer experiences and test their predictability based on the current data sampling strategy. Moreover, such approach can overcome limitations in customer feedback collection and sharing across organizations, thus having wide applicability and being complementary to tools relying on more heavily constrained data.

A pipeline and comparative study of 12 machine learning models for text classification

“…Over many years, researchers have applied machine learning for text classification problems. The favoured and most commonly deployed method is Naïve Bayes (NB), as it has been reported to be the best performing classifier, in terms of running time and accuracy (Metsis et al, 2006a;Fu et al, 2021). More recently, other algorithms like decision trees, support vector machines (SVM), nearest neighbours, and neural networks have also been reported to be effective in the text classification domain (Trivedi, 2016).…”