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Acoustic > 实例探究 > 提升在线预订体验:RIU 酒店及度假村案例研究
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Enhancing Online Reservation Experience: A Case Study of RIU Hotels & Resorts

技术
  • 网络安全和隐私 - 身份认证管理
  • 网络安全和隐私 - 入侵检测
适用行业
  • 海洋与航运
适用功能
  • 采购
  • 产品研发
用例
  • 行为与情绪追踪
  • 牲畜监测
挑战
RIU Hotels & Resorts 是一家全球连锁酒店,在向客户提供无缝在线预订体验方面面临着挑战。该公司的网站遇到了可能导致客户流失的问题。主要挑战是分析和了解客户行为,以发现直接有利于转化和积极用户体验的新需求。该公司还需要一种能够实时监控现场导航的解决方案,以减少任何异常情况直接影响业务之前的行动时间。酒店业的激烈竞争使得用户体验变得至关重要,因为它直接影响客户选择酒店或度假村时的情绪。该公司旨在确定最佳预订路径,以加强与客户的关系。另一个目标是确保组织的安全,避免可能的欺诈行为对业务和品牌造成负面影响。
关于客户
RIU 酒店及度假村是一家全球连锁酒店,在 20 个国家/地区经营 100 家酒店。 2020年,公司接待宾客230万人次。该公司拥有超过 24,500 名员工,目前是全球第 32 大连锁企业。按收入计算,它是西班牙第三大酒店,按客房数量计算,它是西班牙第四大酒店。该公司致力于为客户提供卓越的体验,其主要目标是改进采购流程的每一步。
解决方案
RIU Hotels & Resorts 选择软件即服务 (SaaS) 解决方案 Tealeaf 来应对其挑战。该解决方案允许公司按模式或特定 KPI 配置事件,以更好地了解客户行为,结合临时指标以更好地衡量障碍,并衡量事件的用户行为,从而进行细分以更好地了解整个平台上的客户行为。该解决方案使该公司能够分析和量化遇到的障碍的实际影响,这有助于在优先考虑提高转化率的举措时有效利用资源。该解决方案还有助于提前检测网站上的异常情况并快速做出反应。异常检测用于自动检测行为变化并查看相关指标,使 RIU 能够调查问题的原因。由于探索数据和生成假设的便利性,该解决方案还支持其他部门更灵活的决策。
运营影响
  • The implementation of Tealeaf has led to significant operational improvements for RIU Hotels & Resorts. The company has been able to detect anomalous situations that amounted to 3% of web traffic. As a result of the analysis, new strategies were designed that enabled the company to reduce the number of obstacles by redirecting traffic towards the optimal funnel. The use of Tealeaf has also helped the company to reduce situation analysis efforts by 30% since the platform design is intuitive when looking for behavior patterns. The solution has also helped to reduce response times by more than 50% since before the company had to analyze logs and other records to understand the current situation. The solution has also helped in preventing attacks on the web by spotting a malicious user simulating human behavior and performing thousands of searches per second. The company has also been able to detect cases of fraud, such as unique users who made multiple fraudulent purchases on the web.
数量效益
  • 30% reduction in need for situation analysis
  • 3% of global turnover reported as anomalous was detected and addressed
  • 50% cut in response times to detect anomalies

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