While the understanding of customer satisfaction is a key success factor for service enterprises, existing elicitation approaches suffer from several drawbacks such as high manual effort or delayed availability. However, the rise of analytical methods allows for the automatic and instant analysis of encounter data captured during service delivery in order to identify unsatisfied customers.
Based on encounter data of 1,584 IT incidents in a real-world service use case, supervised machine learning models to predict unsatisfied customers are trained and evaluated.
We show that the identification of unsatisfied customers from encounter data is well feasible: via a logistic regression approach, we predict dissatisfied customers already with decent accuracy—a substantial improvement to the current situation of “flying blind”. In addition, we are able to quantify the impacts of key service elements on customer satisfaction.
The possibility to understand the relationship between encounter data and customer satisfaction will offer ample opportunities to evaluate and expand existing service management theories.
Identifying dissatisfied customers from encounter data adds a valuable methodology to customer service management. Detecting unsatisfied customers already during the service encounter enables service providers to immediately address service failures, start recovery actions early and, thus, reduce customer attrition. In addition, providers will gain a deeper understanding of the relevant drivers of customer satisfaction informing future new service development.
This article proposes an extendable data-based approach to predict customer satisfaction in an automated, timely and cost-effective way. With increasing data availability, such AI-based approaches will spread quickly and unlock potential to gain important insights for service management.
Baier, L., Kühl, N., Schüritz, R. and Satzger, G. (2020), "Will the customers be happy? Identifying unsatisfied customers from service encounter data", Journal of Service Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JOSM-06-2019-0173Download as .RIS
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