Enhanced customer support through effective identification of PMRs at high risk of escalations

在软件工程中，客户的输入对于收集、分析和确定产品需求的优先级至关重要，并且在部署后通过记录问题报告来确保持续的客户满意度。这些报告可能以产品增强建议（对下一个版本的特性有用）的形式出现，或者以软件供应商需要更紧急关注的软件缺陷报告的形式出现。然而，对于大规模的软件产品来说，有效地响应客户问题报告是一个复杂的过程，因为它涉及到评估报告的问题，考虑折衷，并对可能的解决方案和处理问题所需的资源做出重要的判断。软件供应商面临的重要问题是，如果没有有效的解决方案，这些报告中的一些会“升级”为危急情况，严重影响客户的业务以及软件供应商的声誉和满意度的损失。这个项目解决了我们的工业合作伙伴IBM的这个非常具体的问题：客户报告的问题并不总是得到足够的关注，从而导致损害客户关系的升级。一旦客户报告了问题，客户支持、管理和开发人员就缺乏适当的技术和工具来分析评估和解决问题所需的信息，从而导致查找相关信息和解决问题以有效响应客户的重大延迟。通过与IBM Victoria实验室的合作，该项目开发了实现机器学习和IBM Watson认知能力的技术和工具，为任何提出的支持问题提供高水平的服务。更具体地说，它提供了更有效的pmr（客户报告的问题）识别，这些问题有升级为关键客户情况的高风险，以及管理与pmr相关的信息和通信。它使他们能够更优化地预测和规划资源，并在正确的时间集中精力“降级”局势。我们的研究结果将减少在升级上花费的时间和成本，并有助于维护与IBM客户的有效关系

In software engineering, customers' input is critical to gathering, analyzing and prioritizing product requirements, as well as in ensuring continuous customers' satisfaction through recording problem reports after deployment. These reports might come in the form of suggestions for product enhancements (useful for features in the next release) or software defect reports that need more urgent attention from the software vendor. For large-scale software products effectively responding to customer problem reporting is, however, a complex process, as it involves assessing the reported problem, considering tradeoffs and making non-trivial judgments about possible solutions and resources required to tackle the problem. The significant problem faced by software vendors is that, without effective resolution, some of these reports 'escalate' into critical situations, seriously impacting the customers' businesses as well the software vendor's loss of reputation and satisfaction. This project addresses this very specific problem at our industrial partner IBM: customer reported problems not always receiving adequate attention, leading to escalations that hurt customer relationships. Once a customer reports a problem, the customer support, management and developers lack the appropriate techniques and tools to analyze the information needed to assess and resolve it, resulting in significant delays in finding the relevant information and addressing the problem for effective response to the customer. In collaboration with the IBM Victoria lab, this project develops techniques and tools that implement machine learning and IBM Watson's cognitive capabilities to provide high levels of service to any support issues raised. More specifically, it provides for more effective identification of PMRs (customer reported problems) that have a high risk of escalating into critical customer situations, as well as managing the information and communication related to PMRs. It provides them with the ability to forecast and plan resources more optimally, and to focus at the right time to 'de-escalate' situations. Our research results will reduce the time and cost spent on escalations, and help maintain productive relationship with the IBM customers