Abstract Understanding and keeping the customer happy is a central tenet of requirements engineering. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. If insufficient attention is given to support issues, however, their escalation to management becomes time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. Our work provides a step toward simplifying the job of support analysts and managers, particularly in predicting the risk of escalating support tickets. In a field study at our large industrial partner, IBM, we used a design science research methodology to characterize the support process and data available to IBM analysts in managing escalations. In a design science methodology, we used feature engineering to translate our understanding of support analysts’ expert knowledge of their customers into features of a support ticket model. We then implemented these features into a machine learning model to predict support ticket escalations. We trained and evaluated our machine learning model on over 2.5 million support tickets and 10,000 escalations, obtaining a recall of 87.36% and an 88.23% reduction in the workload for support analysts looking to identify support tickets at risk of escalation. Further on-site evaluations, through a prototype tool we developed to implement our machine learning techniques in practice, showed more efficient weekly support ticket management meetings. Finally, in addition to these research evaluation activities, we compared the performance of our support ticket model with that of a model developed with no feature engineering; the support ticket model features outperformed the non-engineered model. The artifacts created in this research are designed to serve as a starting place for organizations interested in predicting support ticket escalations, and for future researchers to build on to advance research in escalation prediction.

中文翻译：

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使用特征工程的客户支持工单升级预测

摘要 理解并让客户满意是需求工程的核心原则。收集、分析和协商需求的策略与产品部署后管理客户输入的努力相辅相成。对于后者，支持票是允许客户提交问题、错误报告和功能请求的关键。但是，如果对支持问题没有给予足够的重视，则将其升级到管理层会变得既耗时又昂贵，尤其是对于管理数百个客户和数千张支持票的大型组织。我们的工作为简化支持分析师和经理的工作迈出了一步，特别是在预测升级支持票的风险方面。在我们的大型工业合作伙伴 IBM 的实地研究中，我们使用设计科学研究方法来描述 IBM 分析师在管理升级时可用的支持流程和数据。在设计科学方法中，我们使用特征工程将我们对支持分析师对其客户的专业知识的理解转化为支持票模型的特征。然后，我们将这些功能实施到机器学习模型中，以预测支持工单升级。我们针对超过 250 万张支持票和 10,000 次升级对我们的机器学习模型进行了训练和评估，获得了 87.36% 的召回率和 88.23% 的工作量减少，以帮助支持分析师识别有升级风险的支持票。进一步的现场评估，通过我们开发的原型工具在实践中实施我们的机器学习技术，展示了更高效的每周支持工单管理会议。最后，除了这些研究评估活动之外，我们还将我们的支持工单模型的性能与没有特征工程开发的模型的性能进行了比较；支持票模型功能优于非工程模型。本研究中创建的工件旨在作为对预测支持工单升级感兴趣的组织的起点，并为未来的研究人员进一步推进升级预测研究提供基础。