Developing a Fuzzy Predictive Aid System in Contact Centres for an Efficient Customer Recognition Process

Abstract

Contact centres (CCs) are one of the main touch points by which customers contact an organization. A recent report mentions that about seventy percent of all business interactions are handled in CCs. One of the issues in CCs is the high rate of employee attrition, referred to as Customer Service Representative (CSR) turnover. It is mentioned in the literature that CSRs do not stay in CCs for long, and consequently CCs lose their experienced staff. One of the reasons for this high turnover rate is due to CSR's dissatisfaction with their work environment. There can be many different factors that can lead to such dissatisfaction with the successful identification of the virtual customer being one of them. In this study, we propose a Fuzzy predictive aid system (FPAS) that assists the CSRs in the customer recognition process. The output of FPAS expresses the level of difficulty in customer recognition in two forms, namely: a scalar value and a linguistic value. These values represent to the CSR the level of difficulty in customer recognition before any interaction with a given customer and furthermore depending on that identify the sequence of questions to be asked from the customer for his recognition. Our proposed approach uses Nonlinear integer programming and fuzzy inference based techniques to calculate the level of difficulty that is associated with the recognition of customers.