Abstract-based Sentiment Analysis System for the Hospitality Industry
- Technology : Power BI, Artificial Intelligence, Deep Learning, Natural Language Processing
- Industry : Retail, Hospitality
Business Problem
- Hospitality owners may miss out on valuable customer feedback that can provide insights into their performance and areas for improvement
- Difficult to identify and differentiate between customers who actively promote their brand and those who are dissatisfied
- Lack a benchmark to compare their performance against industry peers
- Hospitality owners may struggle to understand how their brand is perceived in the eyes of their customers
- It is difficult to extract meaningful insights from a large volume of unstructured data such as customer feedback, reviews, and social media posts
- Need for accurate sentiment analysis that gives insights into feedback, comments, and customer behavior
- Manual sentiment analysis is impractical and inefficient
Solution
- Achieving precise sentiment classification required the development of a robust natural language processing (NLP) model
- Must distinguish nuanced sentiments, especially in cases where the text contains sarcasm, irony, or mixed emotions
- Developed a model with high accuracy and the ability to generalize across various textual inputs
- Integration of this system into an interactive dashboard for real-time sentiment monitoring and visualization
- Addresses the complexity of data ingestion, processing, and presentation within a user-friendly interface
- Accurately determine whether a text conveys a positive, negative, or neutral sentiment, even within the vast data stream
- Real-time analysis for immediate issue identification and response
- Customizable sentiment scoring to align with specific business needs
Result
- Implemented NPS led to increased customer loyalty, with a significant rise in the number of promoters
- NPS feedback provided actionable insights, leading to improvements in the overall guest experience
- NPS data revealed operational challenges, helping the restaurant to address several issues
- The restaurant experienced a boost in repeat business and increased customer retention
- By benchmarking their NPS scores against industry peers, the restaurant gained a competitive advantage
- Leveraging promoters' willingness to recommend, the restaurant successfully implemented referral marketing strategies
- NPS data empowered the management to make data-driven decisions regarding menu adjustments, staffing levels, and marketing strategies
- With NPS, the restaurant was able to proactively identify and resolve customer issues
Benefits
- The system provides a detailed understanding of customer sentiments
- Enable proactive issue resolution and a significant increase in customer satisfaction
- Sentiment analysis insights have become integral to decision-making processes
- Seamlessly adapts to diverse languages and domains, ensuring a consistent customer experience across regions
- Regular model updates based on user feedback result in increasingly accurate sentiment classification
- The implementation of the sentiment analysis system has elevated customer satisfaction and streamlined operations
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