Quantitatively Measuring Customer Experience (CX) in the Rail Industry

定量衡量铁路行业的客户体验 (CX)

• 批准号: 2607018
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2607018
• 关键词: Quantitatively; Measuring; Customer; Experience; CX

Although demand for rail travel has more than doubled over the last 20 years (Munro, 2021), rail travel in the UK is currently facing the challenge of recovering from COVID-19 (Harrington, 2022). One part of the work being done in the rail industry is focusing on encouraging people to travel by rail again. Consequently, there is a need for the rail industry to become more passenger-centric (Camacho, 2016) and to more intricately understand passenger experience (PX). Current methods of collecting CX data are conducted annually, therefore cannot offer up-to-date analysis (Focus, 2020). This PhD aims to create a tool for capturing and assessing customer feedback in the rail industry to help understand passenger experience (PX) in greater depth. The tool should also be designed to provide recommendations to industry for improving PX.This PhD aims to create a new methodology for identifying and assessing factors of PX. The findings of this work will not be specific to any scenario but will be adaptable to other scenarios in which customer experience (CX) is to be assessed in a more customer-centric way. This methodology will be different to other, more quantitative data-driven, methods because it will turn subjective, qualitative data, into quantitative solutions. This research will initially employ qualitative methods to identify factors affecting PX, then attempt to analyse this data using quantitative methods. Furthermore, machine learning algorithms could be employed to categorise data so that the tool could run automatically as more data is received.Research QuestionsHow can a tool be developed to identify and weight up-to-date factors in PX?How can machine learning methods be used to identify key issues raised by passengers?How could the tool be used by industry to provide effective solutions to PX problems?

尽管铁路旅行的需求在过去20年增加了一倍多（Munro，2021年），但英国的铁路旅行目前正面临从COVID-19中恢复的挑战（哈灵顿，2022年）。铁路行业正在做的工作之一是鼓励人们再次乘坐铁路旅行。因此，铁路行业需要更加以乘客为中心（Camacho，2016），并更复杂地了解乘客体验（PX）。目前收集CX数据的方法是每年进行一次，因此无法提供最新的分析（Focus，2020）。该博士旨在创建一个用于捕获和评估铁路行业客户反馈的工具，以帮助更深入地了解乘客体验（PX）。该工具还应旨在为工业界提供改善PX的建议。该博士旨在创建一种新的方法来识别和评估PX的因素。这项工作的结果将不特定于任何场景，但将适用于以更以客户为中心的方式评估客户体验（CX）的其他场景。这种方法与其他更加定量数据驱动的方法不同，因为它将主观、定性数据转化为定量解决方案。本研究将首先采用定性方法来确定影响PX的因素，然后尝试使用定量方法分析这些数据。此外，机器学习算法可以用来分类数据，使该工具可以自动运行，因为更多的数据是received. ResearchsHow可以开发一个工具来识别和加权PX的最新因素？如何使用机器学习方法来识别乘客提出的关键问题？工业界如何利用这一工具为PX问题提供有效的解决办法？