Luxury hotel assessment company turns customer feedback into action using AI
公司规模
1,000+
地区
- America
国家
- United States
产品
- Luminoso Daylight
- Amazon Connect
- Amazon Redshift
- Amazon RDS
技术栈
- Natural Language Processing (NLP)
- Machine Learning
- APIs
- Semantic Networks
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 自然语言处理 (NLP)
- 应用基础设施与中间件 - 数据可视化
适用功能
- 商业运营
服务
- 数据科学服务
- 系统集成
关于客户
LQA (Luxury Quality Assurance) is a company specializing in assessing and framing customer feedback for luxury hotels. The company aims to help its clients deliver better guest service by providing comprehensive insights into customer feedback across various channels. LQA deals with a wide range of data sources, including guest satisfaction surveys, social media reviews, and internal audits. The company faces the challenge of processing large volumes of unstructured, text-based data that come in multiple languages and contain unique vocabulary specific to the hospitality industry. LQA sought to enhance its ability to analyze this data accurately and efficiently, without requiring extensive manual intervention or in-house data science expertise.
挑战
LQA, a luxury hotel assessment company, faced several challenges in analyzing customer feedback. The feedback was largely unstructured and text-based, making it difficult to analyze using standard analytics approaches. The data sources were highly varied, including guest satisfaction surveys, social media reviews, and LQA audits. Additionally, the data volumes were large, with a single luxury hotel receiving thousands of customer communications daily. LQA needed a solution that could scale up or down to meet demand and provide accurate insights despite the unique vocabulary and multiple languages involved in the hospitality industry.
解决方案
To address its challenges, LQA turned to Luminoso Technologies and Amazon Web Services (AWS). Luminoso Daylight, a machine learning and natural language understanding platform, allowed LQA to quickly and accurately analyze customer feedback. The solution works with various AWS services, including Amazon Connect, Amazon Redshift, and Amazon RDS, to meet the requirements of customer feedback analysis projects. LQA began processing and analyzing its clients' unstructured, text-based data in five phases: uploading data into Luminoso Daylight, parsing the text using natural language processing (NLP), augmenting the input with semantic networks, analyzing the data in under 10 minutes, and generating output viewable through Luminoso Dashboards or other data visualization tools. This approach enabled LQA to extract underlying concepts and quantify what people were really talking about using the LQA framework.
运营影响
数量效益
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