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.

中文翻译：

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顾客会开心吗？从服务遭遇数据中识别不满意的客户

尽管了解客户满意度是服务企业成功的关键因素，但现有的启发方法仍存在一些缺点，例如人工费力或可用性延迟。但是，分析方法的兴起允许对服务交付过程中捕获的遭遇数据进行自动和即时分析，以识别不满意的客户。根据现实服务用例中1,584个IT事件的遭遇数据，对可以预测不满意客户的有监督的机器学习模型进行了培训和评估。我们表明，从相遇数据中识别出不满意的客户是完全可行的：通过Logistic回归方法，我们可以准确地预测出不满意的客户，这大大改善了“盲目飞行”的现状。此外，我们能够量化关键服务元素对客户满意度的影响。了解相遇数据与客户满意度之间关系的可能性将为评估和扩展现有服务管理理论提供充足的机会。从遇到的数据中识别出不满意的客户，为客户服务管理增加了一种有价值的方法。在服务遇到问题时就已经检测出不满意的客户，使服务提供商可以立即解决服务故障，尽早开始恢复措施，从而减少客户流失。此外，提供商将对客户满意度的相关驱动因素有更深入的了解，从而为将来的新服务开发提供信息。本文提出了一种基于数据的可扩展方法，以自动，及时，且具有成本效益的方式。随着数据可用性的提高，此类基于AI的方法将迅速传播并释放潜力，以获取有关服务管理的重要见解。