Case Studies > Luxury hotel assessment company turns customer feedback into action using AI

Luxury hotel assessment company turns customer feedback into action using AI

Company Size
1,000+
Region
  • America
Country
  • United States
Product
  • Luminoso Daylight
  • Amazon Connect
  • Amazon Redshift
  • Amazon RDS
Tech Stack
  • Natural Language Processing (NLP)
  • Machine Learning
  • APIs
  • Semantic Networks
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Cost Savings
  • Customer Satisfaction
  • Productivity Improvements
Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Natural Language Processing (NLP)
  • Application Infrastructure & Middleware - Data Visualization
Applicable Functions
  • Business Operation
Services
  • Data Science Services
  • System Integration
About The Customer
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.
The Challenge
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.
The Solution
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.
Operational Impact
  • LQA can now view the data output through various lenses, including root drivers of customer satisfaction and NPS scores, churn and retention analysis, brand monitoring, and issue detection.
  • The company can run regression analyses on how people talk about luxury hotels in both guest surveys and social media, providing scientific and quantifiable score drivers.
  • Clients can take action on these insights to elevate the guest experience and generate economic impact for the hotel and the brand.
  • LQA's hospitality customers have become more profitable and more effective in their decision-making.
  • LQA and its clients can now process and understand customer feedback without data scientists, hand-picked keyword lists, or months of manual training, analyzing feedback in minutes instead of months.
Quantitative Benefit
  • Analyze customer feedback in minutes.
  • Evaluate feedback in 14 different languages.
  • Cut costs through automation of customer research.
  • Reduce product time to market.
  • Increase net profits through improved NPS.

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