Deep learning solution for improved conversational data analysis for distributed customer facing teams

用于改进面向分布式客户团队的对话数据分析的深度学习解决方案

• 批准号: 80547
• 金额: $ 21.88万
• 依托单位: REINFER LTD.
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=80547
• 关键词: Deep; learning; solution; improved; conversational

Regardless of sector or size of operation, significant volumes of conversational data are generated daily through the interaction between a business and its customers as well as through internal communications. Despite the potential value of this data to improve its customer experience, operational activity and competitiveness, the ability to truly analyse the content of complex interactions generated by the large volume of phone calls, emails, on-website chat generated by internal and external customer communication is far from optimal with a current reliance on manual interpretation to perform this activity. With significant variations in content, the ability to automatically extract both the meaning of a communication as well as the intent is a highly complex task which is not possible through current call analytics products or AI based solutions which on rely specific metadata, key phrases or sentiment analysis.Whilst this need has long been recognised, it has been significantly compounded as a result of the COVID pandemic as companies seek new opportunities to drive sales in order to survive with a disruption to the traditional ways of communicating amongst teams/customers and disparate working practices of sales teams particularly in an B2B environment. Despite the easing of easing of lockdown measures, many employees (and departments) continue to operate remotely with more tasks than ever being delivered by digital channels and phone calls as a replacement for physical interactions/in-person sales meetings, and with the majority of sectors facing challenging market conditions, the need to better understand a client's needs and more quickly respond to evolving market opportunities has become even more critical.Through the deployment of the first automated conversational intent recognition system capable of interpreting unstructured communications data from any form of communication and converting this from natural language to structured data, without the need for software expertise or additional coding input, Reinfer overcomes the limitations of both manual practice and emerging AI based solutions in this space. Whilst the ability to automate the interpretation of communication data has now been proven by Reinfer -- the ability to rapidly disseminate this across remote working sales and customer service teams has not. It is this capability that the proposed project seeks to deliver exploring the potential to be integrated into existing CRM systems and communication platforms e.g. slack and expanding functionality across wider communication channels.If successful, the solution has the ability to deliver significant socioeconomic impact across multiple sectors supporting increased sales, operational efficiencies and as a valuable support tool for disparate working teams. The solution can play a critical role in supporting both the recovery of UK business and future growth as well as help prepare for a potential 'new world' where virtual customer engagement increasingly replaces traditional face-to-face meetings.

无论行业或运营规模如何，通过企业与客户之间的交互以及内部通信，每天都会产生大量的会话数据。尽管这些数据在改善客户体验、运营活动和竞争力方面具有潜在价值，但真正分析由内部和外部客户沟通产生的大量电话、电子邮件、网站聊天产生的复杂互动内容的能力远非最佳，目前依赖于人工解释来执行这些活动。随着内容的显著变化，自动提取通信含义和意图的能力是一项高度复杂的任务，这是不可能通过当前的呼叫分析产品或基于AI的解决方案来实现的，这些解决方案依赖于特定的元数据、关键短语或情感分析。虽然这一需求早已被认识到，但由于新冠疫情的爆发，企业寻求新的机会来推动销售，以便在团队/客户之间的传统沟通方式和销售团队的不同工作实践（尤其是在B2B环境中）受到破坏的情况下生存下来，这一需求已经大大加剧。尽管放松了封锁措施，但许多员工（和部门）继续远程操作，通过数字渠道和电话交付的任务比以往任何时候都多，取代了实际互动/面对面的销售会议。随着大多数行业面临充满挑战的市场条件，更好地了解客户需求并更快地对不断变化的市场机会做出反应的必要性变得更加重要。通过部署第一个自动会话意图识别系统，该系统能够从任何形式的通信中解释非结构化通信数据，并将其从自然语言转换为结构化数据，而无需软件专业知识或额外的编码输入，Reinfer克服了人工实践和新兴的基于AI的解决方案在该领域的局限性。虽然Reinfer已经证明了自动解释通信数据的能力，但在远程工作的销售和客户服务团队之间快速传播的能力还没有得到证实。正是这种能力，提议的项目寻求提供探索整合到现有的CRM系统和通信平台的潜力，例如slack和跨更广泛的通信渠道扩展功能。如果成功，该解决方案能够在多个部门提供显著的社会经济影响，支持增加销售，提高运营效率，并作为不同工作团队的有价值的支持工具。该解决方案可以在支持英国业务的复苏和未来增长方面发挥关键作用，并有助于为潜在的“新世界”做好准备，在这个新世界中，虚拟客户互动日益取代传统的面对面会议。