Customer Feedback Analytics from Unsolicited Resources

来自主动提供的资源的客户反馈分析

• 批准号: 568510-2021
• 负责人: Fani, HosseinH
• 金额: $ 2.55万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750305
• 关键词: Customer; Feedback; Analytics; Unsolicited; Resources

Customer Feedback Management (CFM) systems have been extensively developed for improved client satisfaction and experience. Existing systems, however, have relied much on solicited feedback channels, such as surveys and have not considered online social platforms where the customers are not constrained in private customer-industry feedback channels and can leave unsolicited feedback publicly (social feedback). In this proposal, we seek to be among the first to leverage these unsolicited public feedback outlets using social network analysis and natural language processing techniques. Through this project jointly with Mitacs Accelerate support, we aim at 1) enhancing customer satisfaction by automatically understanding the dynamics of the social feedback and how that reflects on specific customer's needs and preferences (customer care); and 2) increasing customer retention, enhancing business opportunities and identifying emergent community trends (business support). Our industrial partner in this proposal, Press'nXPress, is the world's first and only Canadian company that will connect social channels with physical touchpoints. The foremost outcome will be the next-generation social-aware CFM that will be adopted by the growing social feedback, and therefore, enhance Canada's position as one of the world leaders in CFM solutions. Secondly, Canadian industries will leverage the outcomes of this project to improve their customer care, yielding to the retention of current customers, finding new prospective customers, and winning back former customers. From an academic perspective, while providing a solid foundation for the training of highly qualified personnel in equitable and inclusive research and industrial environment, this proposal will bridge social media analytics and customer review analysis research. Unlike the existing graph-based techniques for processing social content, our work will center on the use of language modelling and neural network technologies to make sense of social feedback. We will 1) model services' aspects and their temporal characteristics, 2) identify communities that are centred around related services, and 3) detect emergent aspect-based trends.

客户反馈管理 (CFM) 系统已得到广泛开发，以提高客户满意度和体验。然而，现有系统在很大程度上依赖于征求反馈渠道，例如调查，并且没有考虑在线社交平台，在这些平台上，客户不受私人客户行业反馈渠道的限制，可以公开留下未经请求的反馈（社交反馈）。在本提案中，我们力求成为第一批利用社交网络分析和自然语言处理技术利用这些主动提供的公众反馈渠道的人。通过与 Mitacs Accelerate 支持共同开展的这个项目，我们的目标是 1) 通过自动了解社会反馈的动态及其如何反映特定客户的需求和偏好（客户关怀）来提高客户满意度； 2) 提高客户保留率、增加商业机会并识别新兴社区趋势（业务支持）。我们在该提案中的工业合作伙伴 Press'nXPress 是世界上第一家也是唯一一家将社交渠道与物理接触点连接起来的加拿大公司。最重要的成果将是下一代具有社会意识的 CFM，它将被不断增长的社会反馈所采用，从而增强加拿大作为 CFM 解决方案的世界领导者之一的地位。其次，加拿大各行业将利用该项目的成果来改善客户服务，留住现有客户，寻找新的潜在客户，并赢回以前的客户。从学术角度来看，该提案在为公平、包容的研究和产业环境中高素质人才的培养提供坚实基础的同时，还将社交媒体分析和客户评论分析研究联系起来。与现有的基于图形的处理社交内容的技术不同，我们的工作将集中于使用语言建模和神经网络技术来理解社交反馈。我们将 1) 对服务的方面及其时间特征进行建模，2) 识别以相关服务为中心的社区，3) 检测基于方面的新兴趋势。