Abstract-based Sentiment Analysis System for the Hospitality Industry

Technology : Artificial Intelligence, Deep Learning, Natural Language Processing, Power BI
Industry : Hospitality, Retail

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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