Enhancing Hotel Management with AI-Driven Analytics

About the Project

Our client, a leading hotel management company in Switzerland, oversees a portfolio of luxury and boutique hotels. Committed to exceptional guest experiences, they sought to harness customer feedback to enhance service quality and operational efficiency. They partnered with QSoft to develop an AI-driven analytics solution capable of extracting actionable insights from customer reviews. QSoft developed a cutting-edge AI-driven analytics platform tailored to the client’s needs. The solution enabled the aggregation, cleaning, and analysis of customer reviews from multiple platforms, transforming raw data into meaningful insights. To improve accessibility, a chatbot was implemented to provide users with intuitive summaries and responses based on their queries.

Technologies

Frontend: Next.js, Chart.js

Backend: Flask

Hosting/Deployment: Amazon Web Services (AWS)

Version Control: GIT

AI/NLP: OpenAI (LLM), Vector Database, Embedding, RAG

Data Visualization: Chart.js

Algorithms: Re-ranking for insight prioritization

Challenges

Data Aggregation: The client needed a system that could efficiently scrape and consolidate reviews from various platforms, such as online travel agencies and review websites. The challenge was to ensure the solution could handle diverse data structures and formats while maintaining accuracy and completeness.
Data Cleaning: Raw data from external sources often contained noise, irrelevant information, or incomplete entries. Developing a robust mechanism to clean and standardize this data for meaningful analysis was a crucial challenge.
Insight Generation: The client required a solution to extract actionable insights from the data, such as recurring themes in customer feedback or trends in service satisfaction. This demanded advanced AI algorithms capable of understanding natural language and identifying key patterns.
User Interaction: To make insights accessible, the client wanted a chatbot that could interpret user queries and provide summaries or detailed responses. The challenge was to ensure the chatbot delivered accurate, context-aware answers in real time.

Our Solutions

Data Aggregation: QSoft implemented web scraping tools integrated with Flask to automate the extraction of reviews from multiple sources. The scraping process was designed to handle diverse formats, and Amazon Web Services (AWS) ensured scalability and reliability during data aggregation.
Data Cleaning: An automated pipeline using NLP techniques was built to clean and preprocess the data. Techniques such as stop-word removal, sentiment normalization, and spell-checking ensured that only relevant, high-quality data was retained for analysis. A Vector Database was utilized to structure the cleaned data for efficient retrieval and querying.
Insight Generation: Advanced AI models, including LLMs (OpenAI) and a re-ranking algorithm, were deployed to analyze customer reviews. These models identified themes such as frequent complaints, service highlights, and emerging trends. The Retrieval-Augmented Generation (RAG) technique was used to enhance the contextual accuracy of insights.
User Interaction: QSoft designed and implemented a chatbot interface using Next.js for the frontend, integrated with OpenAI embeddings for semantic search capabilities. The chatbot allowed users to query the data intuitively, generating summaries, highlighting critical feedback, and responding to specific questions about guest satisfaction or operational performance.

Results

Improved Operational Efficiency: The platform streamlined the review analysis process, reducing the manual effort required to consolidate and interpret customer feedback by over 70%.
Actionable Insights for Better Services: By identifying recurring themes and trends in guest reviews, the platform empowered the client to address specific service issues and improve guest satisfaction proactively.
Enhanced User Experience with Chatbot: The chatbot provided quick, accurate summaries and detailed responses, making insights accessible to team members without technical expertise. This boosted the client’s ability to act on guest feedback in real-time.
Scalable and Reliable Solution: The use of AWS for hosting ensured the platform could scale seamlessly as the client expanded its portfolio of hotels, handling larger datasets without performance degradation.

Enhancing Hotel Management with AI-Driven Analytics

Enhancing Hotel Management with AI-Driven Analytics

About the Project

Technologies

Project duration: 8 months

Team Size : 7

Satisfaction Score: 95%

The Screenshots

Challenges

Our Solutions

Results

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