Mining voice of customers and employees in insurance companies from online reviews: a text analytics approach

Abstract

Purpose

Although insurance companies contribute to about $600 billion of the US gross domestic product and employ 2.7 million people, the overall perception of clients and workers employed in this domain is poor. To the best of our knowledge, this study is the first to propose recommendations to improve the service quality of insurance companies using the voice of customers and employees that are available online.

Design/methodology/approach

The proposed methodology consists of four stages: extracting text reviews posted by employees and customers, reviewing feedback using bigrams and trigrams, determining topics and identifying quality-associated managerial recommendations using the Strengths, Weaknesses, Opportunities and Threats (SWOT) and root cause analysis techniques.

Findings

Our results indicate that primary causes for client dissatisfaction are improper client assistance services, inefficient claims processing, issues related to payments/fees and unresponsive ancillary services. Offering different types of payment methods, immediate car replacement policy and features such as smartphone applications are commended very well. The major reasons for workforce frustrations include long and overly complex training, lack of social events and incentive programs. Coworkers, workplace facilities, clean work environment and other amenities are shown to have a positive impact on employees' satisfaction level.

Originality/value

To the best of our knowledge, this study is the first to propose recommendations to improve the service quality of insurance companies using the voice of customers and employees that are available online. The proposed quality-related insights can assist insurance companies in improving the outlook of their workers as well as customers.

Citation

Rajendran, S., Srinivas, S. and Pagel, E. (2023), "Mining voice of customers and employees in insurance companies from online reviews: a text analytics approach", Benchmarking: An International Journal, Vol. 30 No. 1, pp. 1-22. https://doi.org/10.1108/BIJ-12-2020-0650