Harnessing Business Insights from Unstructured Customer Data

由于消费者广泛采用在线购买和交流渠道，导致在线评论和社交媒体帖子激增。这些文本信息为澳大利亚企业目前为获得客户洞察而进行的调查提供了一个可行的替代方案。然而，这些审查是非结构化的，需要大量的预处理来提取潜在的客户感知。因此，本项目旨在开发一种新的机器学习方法来量化客户在线共享的文本信息中包含的与业务相关的信息。这一替代方法将为一系列澳大利亚公司提供显著的成本节约效益，如零售商、酒店、航空公司和餐馆。

Resulting from customers’ widespread uptake of online channels to buy and communicate has been a surge in online reviews and social media posts. This textual information offers a viable alternative to surveys that Australian businesses currently conduct to obtain customer insights. However, these reviews are unstructured and require substantial pre-processing to extract underlying customer perceptions. Therefore, this project aims to develop a novel machine learning approach to quantify the business-relevant information contained in textual information shared by customers online. This alternative approach will provide significant cost-saving benefits for a range of Australian companies, such as retailers, hotels, airlines and restaurants.