Implementation of Empath X SLA predictive tool for a government agency

• Main Authors: LEE, Hui Shan, SHANKARARAMAN, Venky, OUH, Eng Lieh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Citizen service delivery; Text analytics; Empath; Service level agreement; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7737
• Institution: Singapore Management University

Service Level Agreement (SLA) plays a significant role in the relationship between citizens and the government. It stipulates the quality levels required for the meaningful interaction between the two parties. Most SLA predictive models consider end-to-end duration and frequency of failed service requests as model inputs with little research on the analysis of textual details of the service request. This is an issue for government bodies as the latter do not just want to meet SLA, but also be proactive by knowing the citizens before assisting them. Inclusion of textual data potentially answer to this requirement of knowing the citizen before the officer tries to meet SLA. In this paper, we attempt to enrich SLA predictive process by analysing the textual data contained in the service requests. Based on a dataset of 800k case records from a customer service centre based in Singapore, we use text analytics to derive features from the dataset, which will be included with other commonly used variables in the prediction of SLA. We further explore the use of the Empath library to provide a categorical outcome that is more beneficial for the customer service officers to understand the citizen, than a numerical outcome. Based on our experiments, we observe that a predictive model built via logistic regression performs the best with an accuracy of 75%. This result remains valid when Empath categories are included as an input variable. This paper adds to the body of research work done in citizen service by proposing an SLA predictive model that incorporates lexical features from textual data to facilitate proactive citizen service delivery.