社交媒体和计算机技术在酒店行业中使用机器学习进行客人满意度和推荐分析

Sagar Bhatt, Rakesh Dani, Deepanshu Girsa, R. Kuksal, K. Joshi, Ashulekha Gupta
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引用次数: 0

摘要

顾客忠诚度不仅对顾客满意度有很大的影响,还有助于提高效率,从而提高酒店的利润和销售额,对顾客产生积极的影响。忠诚是一种长期的承诺,它有助于企业在市场上长期立足,并使企业取得成功。酒店业是所有行业中最快、最大的创造就业机会和收入的行业之一。在酒店行业,员工要么在前台(前厅和餐饮服务),要么在后台(食品生产和客房部)与客人接触,以最专业的方式履行他们的职责。顾客满意是维持顾客忠诚度的重要目标之一。本研究收集了原始数据,属于实证研究。数据是从北加尔瓦尔地区收集的,覆盖的主要城市是曼苏里、德拉敦、里希凯什和哈里瓦尔。本研究的发现是顾客保留/忠诚度与向他人推荐的关系,以及与整体门店绩效的关系。本研究定义了餐饮门店相关的整体行为变量对满意度的影响。该研究通过在线和离线填写问卷的方式进行,人口规模为110人。人口涵盖了在任何一家酒店住过至少一晚的普通旅客。采用相关分析、方差分析和t检验对数据进行分析。该研究反映了数据收集约束与COVID-19政府指南之间的差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uses of Social Media and Computer Technologies for Guest Satisfaction and Recommendation Analysis using Machine Learning In Hotels Industries
Guest loyalty has great effect on their satisfaction as well as it helps to increase the efficiency, and it leads to improved profit and sale of hotel and create positive impact on customers. Loyalty is a long-term commitment which helps to stand the business in market for a long time and make business successful. Hospitality industry is the one of the fastest and largest job generating and revenue generating industry in among all sectors. In hospitality industry, employees come with the contact with guest either in front of the house (Front Office & F& B service) or back of the house (Food Production & Housekeeping) and perform their duties in the best professional way. Customer satisfaction is one of the important objectives to sustain the guest loyalty for repetition. In this research primary data was collected and it belongs to empirical research. Data were collected from the UttrakahndGarhwal region and the major cities covered are Mansoori, Dehradun, Rishikesh and Haridwar. The findings of this study are how customer retention/loyalty relates to the recommendation to the others in addition with the overall outlets performance. The study defines the impact of the overall behavioral variables related to F& B outlets in relation to the satisfaction. The research was conducted with the help of online & offline filled questionnaires with the population size of 110. The population covered the regular travelers who have visited any of the Hotels and stayed at least for one night. The data was analyzed by using correlation, ANOVAs and T-Test. The study has reflected the Gap in constraint of the data collection and the COVID-19 Government guidelines.
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