Cloud-based Restaurant Service Platform and Data Analytics for Customer Satisfaction Survey

• Main Authors: Wu, Zhang-Hao, 吳章豪
• Other Authors: Liu, Duen-Ren
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/4n7q94

碩士 === 國立交通大學 === 管理學院資訊管理學程 === 105 === In present days, restaurants in the market are trying constantly to enhance their services by utilizing new technologies such as cloud, electronic ordering, and online booking. However, there has not a completed and vertically integrated system emerging to the market just yet. Restaurants, usually with insufficient IT experiences, struggle to manage and integrate above functions and control systems. Consequently, this increases the managing difficulties and cost to provide a user friendly service. Moreover, most of the platforms cannot collect data effectively, wasting the value of data, where managers could have analyzed those patterns and then feedback to their restaurants as a decision reference. The purpose of this study aims to provide a complete solution to address the problems above by studying the application services of Tagfans cloud-based platform (eg, ordering, queuing, booking, customer satisfaction survey, etc.). The Tagfans service platform is presented to provide integrated services to restaurants. The satisfaction survey data are collected and analyzed by various data mining methods. The analytical result can be used to to enhance the follow-up data value of each function for further decision making.