How Sentiment Analysis Can Improve Customer Experience & Reduce Churn

by Luciana Flora | Jan 29, 2025 | Data & reporting, Process & Automation

Customer reviews shape a brand’s reputation and provide valuable insights into customer experience. Positive feedback builds trust, while negative reviews highlight areas for improvement. Sentiment analysis and AI-driven text classification help businesses efficiently process and act on this data. This article explores how tools like Fivetran and Snowflake enable seamless integration, automated analysis, and actionable insights to enhance customer satisfaction and reduce churn. 

Sentiment Analysis Tool

While the focus should be on obtaining good reviews, it is equally important to analyze both positive and negative comments left by users across different channels.

If good reviews can increase your trustworthiness and visibility, bad ones can offer insights into areas of improvement and customer preferences.

You may wonder what kind of data can be gathered from sentiment analysis and/or text classification tools. Depending on your focus and goals, several use cases can be applied. Both models can support you in identifying:

  • Which reviews are negative and need to be assessed for process improvement.

  • Which customers need to be engaged after their stay to make up for a less-than-ideal experience.

  • Which customers are most likely to return and can benefit from exclusive offers and loyalty programs.

  • Etc.

If you have a sample dataset for reviews, feel free to classify them using our AI text classification tool.

The Need for an Infrastructure

In today’s world of fast-growing data, there is no room for manual data assessment. You need tools that help you integrate processes within your ecosystem and leverage them to the next level.

By automatically connecting data from multiple review channels and analyzing it, you can receive real-time notifications to immediately address negative comments or nurture customers for a quicker return.

Connecting data from different systems to gain a complete understanding of the customer journey will help you improve their experiences.

There isn’t a one-size-fits-all approach, so adjusting the capabilities of these models to your infrastructure needs is the best way to help your business act quickly and effectively.

How?

By using tools like Fivetran and Snowflake, we can seamlessly integrate source channels as well as additional information to process, analyze, and deliver final results back to you.

Get the Data in Automatically

Sources like Google, Yelp, TripAdvisor, and Trustpilot, among others, are already existing connectors that can be quickly set up and integrated with CRMs.

Process the Data

Once the data is cleaned and organized, it is continually fed into a data warehouse where all necessary transformations and processing take place. Once the model is defined and adjusted, you can start collecting new review data and classify customers based on their likelihood of churning.

Analyze and Monitor Your Data

Through notifications, reports, or emails, you can establish a process for acting on the results, transforming the model’s insights into actionable steps.

Customer Churn Prediction Based on Reviews

Pre-Analysis of the Data

As an example, we used a TripAdvisor data sample with basic information regarding customer reviews and accommodation. We performed a cleanup and resized the dataset while maintaining the overall ratings distribution.

We focused on columns regarding general booking and review information to get an overview of customer feedback, including:

  • ‘Title’ of the review

  • ‘Text’ of the review

  • ‘Date_stayed’, ‘Date’, ‘ID’, ‘Via_mobile’

Additionally, we examined specific ratings for service categories using the columns:

  • ‘Service’, ‘Cleanliness’, ‘Value’, ‘Location’, ‘Sleep_quality’, ‘Rooms’, ‘Check_in_front_desk’, ‘Business_service’

On the overall rating throughout the entire timeframe (January 2023 to December 2024), more than 85% of users who left feedback rated their experience between 4 and 5 stars.

When analyzing ratings per category, we observed that cleanliness and sleep quality had the most 5-star ratings, whereas business service (such as internet service) and check-in/front desk received the highest proportion of 2- and 3-star ratings.

Analysis of the Text Classification

To classify and predict whether a user will churn based on their reviews, we used Snowflake’s integrated Cortex functions to both classify and summarize the reviews.

From the results, approximately 10% of customers were classified as “Churn” (possible churn) based on the sentiment of their review.

For the two years under consideration, we noticed that the number of customers leaving reviews increased significantly during the last five months of 2024. Although it is expected that more customers would churn in absolute numbers, the distribution remained low within this category.

 

By integrating review classification with other hotel and stay-related data, we can gain deeper insights into focus areas or specific dates that require attention.

Examining each month individually allows us to understand which services contributed the most to higher churn rates and identify what actions can be taken to improve those services.

Looking specifically at customers who might churn, we found that most gave a 3-star rating, but there were still some customers who provided high ratings and were classified under “Churn.”

We focused on one such case where a customer gave a high rating, yet their review was classified as “Churn.” The model considered highlighted sentences, and although positive comments were included, the strong negative sentiment in certain parts of the review had a significant impact.

Example Review:

“I thoroughly enjoyed my room at Extended Stay. The staff was extremely polite and helpful. What really got me was that every morning, there was fresh coffee alongside bagels and muffins just waiting for guests in the lobby. My only grievance is that they do not have Cat 5 cables/ethernet cables for their guests. Having stayed at Extended Stays before, I was expecting them to. I brought my Xbox along, and thank goodness I brought a wireless adapter, or it would have been useless. Plus, when I first plugged in my laptop, I realized why… they want to charge extra for a high-speed connection! I was thoroughly disappointed.

The other Extended Stays have always provided me with ethernet cords for free, without trying to squeeze extra money out of me. That majorly disappointed me. Sorry, Extended Stay, but you need to change that. I’d rather pay $10 more to stay somewhere that provides an ethernet cord rather than trying to charge me $3 per day for high-speed wireless internet. What a joke—wireless only gets so fast. Other than that, the features and amenities were great, and the staff was friendly and helpful.”

Conclusion

Analyzing both positive and negative reviews can significantly improve processes in the hospitality sector. Leveraging data-driven insights and a fully integrated infrastructure enables quick and efficient responses to both dissatisfied and satisfied clients.

By customizing models to align with your business needs and objectives, you can scale effectively while remaining agile.

Tools like Fivetran and Snowflake, with their connectors to key data sources, make setting up such a structure a straightforward process. Once in place, you can transform insights into action through notifications, reports, or automated emails, ensuring your team can immediately act on the results and implement meaningful changes.

Our team is here to support you in implementing the right tools and strategies to turn customer insights into actionable improvements that drive long-term success – reach out today!

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