Analysis of Google Reviews of a Beach Front Resort

Data extracted from Reviews with Written Comments within Past 2 years


Beach Front Resort Hotel

Beach Front Resort Hotel


Why Analyze Hotel Guest Reviews?

Research from Apex Global Learning shows that customer reviews directly impact business revenue:

  • Every increase in star ratings boosts revenue between 5% to 9%
  • There is an 18% difference in revenue between 3 Star and 5 Star companies
  • It takes 10-12 positive reviews to counteract a single negative review

Analyzing customer reviews and operational data is crucial for hotel management to gain valuable insights into guest experiences and identify areas for improvement. By leveraging natural language processing techniques to extract sentiment scores and semantic insights from review text, management can quantify and visualize customer satisfaction levels across various aspects of their operations, such as room quality, service, and location.

These analyses enable management to pinpoint strengths and weaknesses, make data-driven decisions, and prioritize initiatives to enhance guest experiences and overall operational excellence. Ultimately, harnessing the wealth of information contained within customer feedback and operational data empowers hotel management to deliver superior hospitality services and foster guest loyalty, thereby driving long-term success and profitability.

Data Reviewed in this Guest Review Analysis Demonstration

The data analyzed in this report was extracted from Google Reviews provided by guest of a random resort hotel located on the Pacific coast of Mexico. The reviews all had written comments and provided an overall rating between 1 and 5 stars. Most, but not all, also included ratings between 1 and 5 stars of the rooms, service, and location of the hotel.

A small sample of the analysis that can be derived from guest reviews is offered below.

Distribution of Overall Ratings: 1 to 5 Stars

To gain an overview of the distribution of customer ratings, a column chart can be utilized to visualize the count of reviews across each star rating category, from 1 star (lowest) to 5 stars (highest). This chart provides a clear representation of the hotel’s performance, allowing management to quickly identify the proportion of positive (4-5 star) and negative (1-2 star) reviews, as well as the overall rating tendencies.



Mean Rating Across Categories

While the distribution of star ratings offers a broad overview, analyzing the mean ratings across specific operational areas provides more granular insights. A bar chart visualizing the average ratings for overall experience, rooms, service, and location enables hotel management to pinpoint the strengths and weaknesses within each facet of their operations.



Of note for this resort, the mean rating for overall satisfaction is less than the ratings for rooms, service, and location. That may be a sampling issue, as some reviews only included an overall star rating, but there may be other factors besides rooms, service, and location that adversely impact the guests’ overall experience at the resort. Certainly, it warrants further exploration.

Median Sentiment Scores Across Categories

To complement the quantitative star ratings, analyzing the sentiment expressed in textual reviews offers a nuanced perspective on guest experiences. A boxplot showcasing the median sentiment scores across the 1 to 5 star rating categories provides valuable qualitative insights. This visualization enables hotel management to assess the alignment between numerical ratings and the emotional tenor of guest feedback, identifying potential disconnects and opportunities to address underlying issues or reinforce positive aspects of the customer journey.



Sentiment analysis cannot perfectly capture the sentiments of the text, and in this chart, it is worth noting that each category has outlier values. For the “one star” ratings, there are a number of strongly positive outlier sentiments. These might be missclassified sentiments (e.g. “the rooms were not very clean” being classified as “very clean”), or they might reflect certain aspects of the hotel that guests valued highly, even if the overall experience was rated poorly. Deeper examination of the reviews would clarify the apparent discrepancies.

Wordclouds of Frequently Expressed Perceptions

Visualizing the most frequently used words in guest reviews through word clouds can offer a powerful and intuitive representation of the prominent themes and sentiments expressed. By generating separate word clouds for positive and negative reviews, hotel management can quickly identify the key drivers of guest satisfaction and dissatisfaction.

Five Star Word Cloud

One Star Word Cloud
It is apparent from these two word clouds that guests’ perceptions of the resort’s operations can vary significantly. Understanding the context of the disparate assessments may illuminate aspects of the operations that may have uneven quality across time.

Conclusion

This preliminary analysis of Google reviews left by guests provides a valuable overview of the positive and negative aspects of the resort’s operations. The management team would want to examine in greater detail the sentiments and perceptions to better understand and identify those areas in which the resort is performing well and those areas in which performance could be improved. Ultimately, the long-term success of the resort will depend largely on the perceptions and assessments of the guests of the resort, and data analysis of guest reviews offers management the opportunity to understand and address the strengths and weaknesses of the resort.