Insufficient Evidence in a Customer Service Article

Subject: Management
Pages: 1
Words: 302
Reading time:
2 min
Study level: Bachelor

The journal article investigated how to identify the number of unsatisfied clients during service delivery. It develops and applies analytical methods for automatic and instant analysis of the data recorded. Through machine learning models, unhappy customers were evaluated from sample data of 1584 IT cases that were actual service incidents (Baier et al., 2021). The findings showed that it is feasible to identify unsatisfied clients from engagement data. The journal analyzed dissatisfied clients through the logistic regression approach and deeply discussed the critical service elements of consumer satisfaction.

The authors have no errors in reasoning or insufficient evidence in this study. The implications of understanding customer dissatisfaction are vital to service management theories. The proposition is defined as detecting customers’ experiences during the interaction with the service. This study provides the current service with methods and approaches to recognize and address service failures within the firm. The organization can reduce customer attrition and strengthen the relationship through technology. The research article had a built-in bias because it focused on recent incidents that the customers could easily recall. The report majorly relied on the data of events in the immediate memory of the customer, which may lead to a wrong interpretation of when the service involves long engagement.

In conclusion, the findings of this journal are essential and can easily be compared with modern life experiences. There is the increased availability of data generated from AI approaches and software that most service providers use. It is easy to extend the data-based approaches to predict customer satisfaction and open potential to gain crucial insights into developing customer service. YouTube and Spotify use a data approach to indicate the type of music and show what different customers enjoy. This helps the two platforms identify what service customers are happy with and their level of dissatisfaction.


Baier, L., Kühl, N., Schüritz, R., & Satzger, G. (2021). Will the customers be happy? Identifying unsatisfied customers from service encounter data. Journal of Service Management, 32(2), 265-288. Web.