Implementasi Electronic Customer Relationship Management pada Aplikasi Layanan Pelanggan Hotel

M. wildan, Safitri Juanita
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引用次数: 1

Abstract

The interest of hotel visitors has decreased since the Covid-19 pandemic. So it is recommended that hotels implement Customer Relationship Management (CRM) which has three stages: getting new customers (Acquire), improving customer relationships (Enhance), and retaining customers (Retain). However, hotels currently do not have practical service applications that help to maintain the relationship between hotel management and consumers, so it requires research that implements E-CRM by designing a prototype of web-based Hotel customer service applications. This study aims to standardize hotel customer service applications that implement E-CRM. It uses qualitative research methods by applying the prototype system development method. The research contribution is to make an E-CRM prototype in a hotel customer service application with several features such as complaints, testimonials, promotions, and point redemption. Based on experiments and testing from a user using UAT and interview, the implementation of E-CRM using a prototype of web-based Hotel customer service applications helps the staff to find customer complaints. The promotion feature helps staff add promotional information that appears on the website's front page. Hotel customers can use the feedback feature in complaints, testimonials and redeem points exchange with prizes. In future research, sentiment analysis on hotel customer reviews using a classification algorithm can be carried out.
电子客户关系管理在酒店客服应用中的实现
自2019冠状病毒病大流行以来,酒店游客的兴趣有所下降。因此,建议酒店实施客户关系管理(CRM),它分为三个阶段:获得新客户(acquisition)、改善客户关系(Enhance)和保留客户(Retain)。然而,酒店目前还没有实际的服务应用程序来帮助维护酒店管理层和消费者之间的关系,因此需要研究通过设计基于web的酒店客户服务应用程序原型来实现E-CRM。本研究旨在标准化实施E-CRM的酒店客户服务应用程序。运用原型系统开发方法,采用定性研究方法。本研究的贡献是在酒店客户服务应用程序中制作一个具有投诉、推荐、促销和积分兑换等功能的E-CRM原型。基于用户使用UAT和访谈的实验和测试,使用基于web的酒店客户服务应用程序原型实现E-CRM可以帮助员工发现客户投诉。促销功能可以帮助员工添加出现在网站首页的促销信息。酒店客户可以使用反馈功能进行投诉、推荐和积分兑换奖品。在未来的研究中,可以使用分类算法对酒店顾客评论进行情感分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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发文量
30
审稿时长
12 weeks
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