Customer Sentiment

How Sentiment Analysis Improves Customer Experience [8 Ways]

How Sentiment Analysis Improves Customer Experience [8 Ways]
Customer Service Researcher
LinkedIn icon
How Sentiment Analysis Improves Customer Experience [8 Ways]

To improve customer experience, you first need to understand your customers.

That’s really what sentiment analysis is about.

It shows you what makes your customers happy or unhappy so you can make changes that actually improve their experience, reduce churn, and boost revenue. 

In this article, we’ll discuss eight ways sentiment analysis has helped leading companies like Gousto, Hotjar, and British Airways do just that.

TL;DR

1. Sentiment analysis uses AI models to uncover human feelings in a message or conversation.

2. There are 4 key forms of sentiment analysis, with aspect-based sentiment analysis being the most advanced form.

3. SentiSum is an AI-powered topic and sentiment analysis tool that helps support teams at British Airways, Gousto, Hotjar, and Otrium make fast sense of customer feedback.

4.Sentiment analysis helps identify friction in the customer journey, uncover ways to improve product experience, help support teams prioritize issues, and improve your CSAT and NPS scores.

What is Sentiment Analysis, Anyway?

Sentiment analysis is the process of understanding how customers feel about your product or service.

It’s a form of data science that uses AI, machine learning, and natural language processing (NLP) to uncover human feelings within a message or conversation.

Sentiment analysis tools analyze customer feedback from different channels–support tickets, phone calls, surveys, reviews, and more, in seconds. It then tells you:

  • Key issues customers are contacting you about
  • Sentiment behind each issue (e.g. Positive, Negative, Neutral)
  • Key negative and positive sentiment drivers
  • Increases and decreases in sentiment

There are four main types of sentiment analysis:

  • Polarity-based: Categorizes interactions as positive, negative, or neutral. This is the most basic form of sentiment analysis.
  • Emotion-based: A more advanced approach that categorizes interactions as happy, sad, angry, excited, annoyed, etc.
  • Urgency-based: Identifies the level of urgency in an interaction, useful for support teams looking to prioritize feedback.
  • Intention-based: This takes it a step further by identifying the overall intent behind a conversation, such as “interested buyer” or “churn risk”.

However, the most advanced form is called aspect-based sentiment analysis

It involves breaking down one conversation into several parts, called aspects. Instead of assessing the whole conversation at once, AI models analyze each aspect separately to reveal nuanced insights. 

SentiSum, our sentiment analysis tool, uses aspect-based sentiment analysis to uncover granular insights you can use. More about this in our technology overview.

📖 Related read: Customer Sentiment Analysis | Definition, DIY Template, & More

8 Ways How Sentiment Analysis Improves the Customer Experience (Feat. Real-life Case Studies)

Sentiment analysis has helped companies like Gousto, Hotjar, and British Airways solve major customer experience challenges. 

Here’s a gist of 8 different ways sentiment analysis improves CX, based on our experience working with different companies:

  1. Uncover friction points in the customer journey
  2. Reduce resolution times for high-priority issues
  3. Identify high-risk customers and reduce churn
  4. Map product improvements to what customers want
  5. Make fast sense of customer reviews
  6. Understand and improve CSAT scores
  7. Train agents to provide exceptional customer support 
  8. Build evidence-based cases for change

Let's look at each in detail.

1. Uncover Friction Points in the Customer Journey

Whenever a customer reaches out to you for support, it indicates friction.

These friction points, if they blow up to bad experiences, can cause customers to churn and turn to competitors, leading to revenue loss. 

As a support leader, you want to understand what these key friction areas are and how much of an impact they have on customers, so you can remove them before they turn into big issues. 

This is where sentiment analysis excels the most.

A sentiment analysis tool brings in customer feedback from all your channels (emails, phone calls, chats, surveys, reviews, and more) onto one place. Then, it auto-analyzes and tags each feedback and give insights into:

  • Topic
  • Sentiment 
  • Urgency 

For instance, this could look something like:

  • Topic: Damaged Packaging During Delivery
  • Sentiment: 94% Negative
  • Urgent: Urgent 

This means support leaders know exactly which friction areas they should be prioritizing to fix, in turn improving customer experience in the short and long run.

💡Case Study Spotlight

Gousto is a meal kit delivery service that’s been working with us to unify its voice of customer data across 9 channels.

Gousto plugged in each of their voice of the customer channels (from AWS Connect voice calls to CSAT surveys) onto our sentiment analysis tool. 

We then used real-time tagging and categorisation to provide the Gousto team with accurate and objective understanding of customer sentiment and the topics driving customer contact.

This not only showed Gousto key friction points that their customers were facing, but also empowered different teams across the organization to tackle them and improve customer experience. 

“Through using Sentisum we've significantly reduced the time it takes to unearth customer insights. We now understand these at a much more granular level, which allows us to quickly put actions in place to drive improvements.”

- Joe Quinlivan, Head of Customer Care @ Gousto

2. Reduce Resolution Times for High-priority Issues

Let’s say during your Voice of Customer analysis, you identified 50 issues that drive 99.9% of customer contact. How do you know which issues to tackle first?

Traditionally, support teams would address the most frequent issue first (e.g. Late Delivery) as this can lead to cost savings. But that might not always be the best solution. 

With an additional layer of sentiment analysis, you might discover that the “Late Delivery” issue doesn’t really bother customers.

Instead, you discover that another topic (e.g. “Damaged Goods”) riles customers up–driving negative reviews and bad word of mouth.

These issues to be resolved in a timely manner to prevent further reputation damage and revenue loss. 

After identifying them, your support team can quickly take action and offer compensation plans to customers affected.

💡Case Study Spotlight

Over the COVID-19 pandemic, James Villas experienced a large influx of Zendesk support requests from customers stuck in challenging and uncertain situations. 

Due to high volumes and complexity, the team struggled to prioritize urgent tickets—where a non-timely response would lead to a significant negative impact on the customer's experience.

Johannes, the Head of CRM, used SentiSum to automatically recognise and triage urgent cases within Zendesk.

With SentiSum’s built-in Zendesk integration, James Villas began automatically tagging urgent calls and sending them to a separate inbox for speedy resolution. 

This resulted in a 51% decrease in resolution time for customer issues within the first few weeks.

James Villa's customers now get fast customer service, especially when it's urgent and they need help the most—leading to a higher satisfaction rate and improved customer loyalty.

“With SentiSum doing the heavy lifting, the James Villas support team can focus on their job: making customers happy.”

- Johannes Ganter, Head of CRM and Digital Transformation at James Villas

3. Identify High-Risk Customers and Reduce Churn

Sentiment analysis highlights customers on the brink of churning, along with the specific reasons for their unhappiness.

For instance, our sentiment analysis tool revealed that over 300 customers contacted a meal-kit delivery company about “Poor Ingredient Quality”, with 92% expressing negative sentiments.

Screenshot of how sentiment analysis revealed problem ar

Our ChatGPT-like AI also summarized the impact this issue has on customers.

AI summary of key sentiment and issues

These customers with negative sentiment are at a high risk of switching to competitors.

To prevent this, a quick response is key. You can reach out with solutions, compensation offers, or even a simple expression of empathy–often, understanding and acknowledging a customer's issue can make a significant difference. 

This proactive approach not only helps retain customers at risk of leaving but also enhances the overall customer experience, reinforcing loyalty and satisfaction.

💡Case Study Spotlight

Gourmet liquorice creators, Lakrids by Bülow, used SentiSum to analyze customer feedback in their Dixa support tickets.

Towards the end of 2021, they saw a surge in support tickets about packaging issues, including damages during transportation. 

Based on this feedback, the Lakrids team improved their packaging and transportation processes. They also developed a compensation plan for affected customers.

A year later, customer complaints about packaging decreased by 26%.

“There are two ways that I look at the data: what kind of issues are customers having? How difficult is it to improve on this customer experience? When looking at it through these lenses, it’s easier to prioritize what improvements need to be done and when.”

- Løkke Engraf, Head of Consumer Service and Sales Support at Lakrids

4. Map Product Improvements to What Customers Want

Sentiment analysis isn’t just a tool for the CS team.

It’s also a great way to discover product feedback, enabling data-backed decisions to fill the development roadmap. 

When you’re aware of the issues that affect customers most, you can use that data to prioritize improvements and implement fixes before satisfaction dips lower. Then, you can keep monitoring support data to analyze the impact of your product decisions.

By aligning these offerings with customer sentiments, you can ensure that your product or service meet the needs of your customers.

💡Case Study Spotlight

Hotjar’s Customer Success Team has been using SentiSum to implement customer data into the product development process.

Our topic-based sentiment analysis tells them exactly what issues to prioritize for fixes and improvements for maximum impact.

Customer sentiment insights are now shaping their conversations with product teams, ensuring that updates are customer-driven.

Since these insights are backed by real numbers, it’s also easy to predict the impact that a product decision will have on the bottom line.

As they move forward, Hotjar plans to further involve the product team with SentiSum.

The goal is to enable the product team to independently access the SentiSum platform, directly engaging with customer support insights to refine their product strategy.

“The way I see SentiSum is, it's not about viewing product feedback, it's about discovering product feedback.”

- Nick Moreton, Director of Support at Hotjar

5. Make Fast Sense of Customer Reviews

Customer reviews are a goldmine of insights that can help you improve customer experience, product, and support teams. 

But traditionally, you'd have to read through each text and manually tag it with a topic. This is incredibly time-consuming and labor-intensive. It’s also simply not feasible if you have thousands of reviews to mine through. 

Manually tagging also creates a lot of bias as it depends highly on the perception and experience of the person doing the tagging. 

Sentiment analysis tools can remove this bias and save you massive time on manual tagging.

It uses Natural Language Processing (NLP) to read through your reviews (much like a human would) and sorts them into clear topics with complete objectivity.

These tools also come with an easy to use dashboard that shows you:

  • The overall customer sentiment and survey completion rate
The overall customer sentiment and survey completion rate - SentiSum
  • Biggest changes in sentiment
Biggest changes in sentiment - SentiSum
  • Key drivers of negative and positive sentiment
Key drivers of negative and positive sentiment - SentiSum

On SentiSum, you can also zoom in on each conversation for details on agent performance, call outcome, and improvement opportunities. 

Each customer support conversation - SentiSum

Analyzing reviews give you a 360-degree view of your customers. You can then lead improvement projects that boost customer satisfaction and reduce the number of negative reviews online.

💡Case Study Spotlight

British Airways Holidays came to us with 100,000s of customer reviews across multiple platforms.

Before SentiSum, the insights team would take a few hundred customer reviews as a sample and spend a few hours manually labeling each one with a topic. This was time-consuming and biased.

SentiSum helped British Airways Holidays analyze over 100,000 reviews in five minutes.

A quick check of the dashboard showed the BA Holidays team how many reviews discuss each topic and which topic contributes the most to positive and negative customer sentiment.

The machine learning engine also flagged specific customer issues for action by the BA Holiday team. 

With SentiSum, the BA Holidays team saved time, gained an objectivity that was previously unattainable, and no longer had to take a small sample size. They now have a detailed understanding across all reviews and surveys and provide high quality experiences for the customers.

“In less than 5 minutes, we are now able to understand the drivers of our advocacy from over 100k reviews.”

- Head of Customer Service, British Airways Holidays

6. Understand and Improve CSAT scores

Low CSAT scores are clear signs of customer unhappiness.

Many support leaders aim to improve these ratings. But they’re not sure what’s causing these low scores in the first place, so it's difficult to take corrective action. 

The answer lies not just in the CSAT surveys, but in the rich insights hidden within customer conversations.

The most effective strategy is to combine CSAT driver analysis with sentiment analysis of customer service interactions. This makes it easy to link specific complaints to their impact on CSAT ratings.

And while CSAT surveys aim to assess agent performance, the responses are often muddled by how other teams are performing, which is not under the control of the support team. 

By segmenting customer service data by department, support teams can pinpoint their specific issues and address them directly. 

You can then send other issues related to product and operation that drove poor CSAT ratings to the respective teams for prompt action. 

Ultimately, this comprehensive approach leads to an improved overall customer experience.

🎙️ Related podcast: Boosting CSAT scores and reducing first response times at Printify

💡Case Study Spotlight

Glammmup, a leading e-Commerce company, was struggling with a CSAT score of 62 when the industry average is 78. 

Their head of customer service, Emily, wanted to rectify this.

While hunting for better approaches, Emily realized they already had all the data they needed. They were sitting on a goldmine of customer data that they were not using: customer service conversations. 

Emily used SentiSum to:

  • Capture data from each customer at every touchpoint
  • Analyze customer sentiment from support conversations and map them to CSAT scores

Through this approach, Emily and the team were able to improve customer service quality and make company-wide changes based on what customers were saying.

After a year, Glammmup saw an increase in CSAT score from 68 (below industry-average) to 82 (above industry-average).

7. Train Agents to Provide Exceptional Customer Support 

Sentiment analysis can reveal gaps in agent training or areas where support staff need additional guidance. This could be in handling complex queries, managing angry customers, or providing more compassionate service. 

Using a tool like SentiSum, you can look at records of customer chats, phone calls, and emails. This helps you see how well their agents are doing and the quality of the calls.

Agent performance analysis and AI summary - SentiSum

With this information, you can train specific agents or the whole team better. You can help them improve how they talk to customers, understand the product more, or deal with tough situations, which can lead to higher customer satisfaction.

💡Case Study Spotlight

Scandinavian Biolabs, a hair-care D2C brand, attributed customer conversations to respective agents using Sentisum. 

They then used these insights to coach support team members on how to:

  • Handle difficult conversations
  • Deal with negative feedback
  • Provide resources for the customer

This ensures that the support team are leveling up communication together, and keeps customer satisfaction at the heart of every interaction.

8. Build Evidence-based Cases for Change

Support leaders often have “hunches” about what’s bothering customers, how they’re feeling, and what improvements they want to see.

However, they don’t have the data to “prove” their hunches. And more often than not, improvement plans are based on gut feeling rather than genuine impact. 

Sentiment analysis quantifies qualitative feedback (e.g. 94% of customers feel negative about X issue), so leaders can fix issues that make the most impact on customer experience. 

These tools give you the data and confidence you need to convince your CEO or other departments that something needs to be fixed—and it needs to be fixed now

This not only benefits your customers, but also prevents revenue leaks in the long run.

💡Case Study Spotlight

The Scandinavian Biolabs team was receiving thousands of tickets per month. They were spending a lot of time manually trying to understand which of these tickets were the top issues amidst all the noise.

With a sentiment analysis tool like Sentisum, they were able to get an objective view of their data and understand recurring pain points for customers.

Using this information, the team at Scandinavian Biolabs reprioritized their product roadmap to implement a new customer platform earlier than they had anticipated.

After this implementation, one of the biggest negative sentiment drivers decreased by 50% thanks to heightened awareness and informed product iterations.

“It’s very difficult to convince the rest of the team that something was important when you don’t have the data to back it up.

Insights from SentiSum started becoming the basis for our strategic and product meetings, giving us a sounding board for understanding our customers and their needs outside of customer surveys.”  

- Anders Reckendorff, CEO and Co-Founder at Scandinavian Biolabs
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Trending articles

Customer Sentiment

How Sentiment Analysis Improves Customer Experience [8 Ways]

March 30, 2024
Ben Goodey
Customer Service Researcher
In this article
Understand your customer’s problems and get actionable insights
See pricing

Is your AI accurate, or am I getting sold snake oil?

The accuracy of every NLP software depends on the context. Some industries and organisations have very complex issues, some are easier to understand.

Our technology surfaces more granular insights and is very accurate compared to (1) customer service agents, (2) built-in keyword tagging tools, (3) other providers who use more generic AI models or ask you to build a taxonomy yourself.

We build you a customised taxonomy and maintain it continuously with the help of our dedicated data scientists. That means the accuracy of your tags are not dependent on the work you put in.

Either way, we recommend you start a free trial. Included in the trial is historical analysis of your data—more than enough for you to prove it works.

To improve customer experience, you first need to understand your customers.

That’s really what sentiment analysis is about.

It shows you what makes your customers happy or unhappy so you can make changes that actually improve their experience, reduce churn, and boost revenue. 

In this article, we’ll discuss eight ways sentiment analysis has helped leading companies like Gousto, Hotjar, and British Airways do just that.

TL;DR

1. Sentiment analysis uses AI models to uncover human feelings in a message or conversation.

2. There are 4 key forms of sentiment analysis, with aspect-based sentiment analysis being the most advanced form.

3. SentiSum is an AI-powered topic and sentiment analysis tool that helps support teams at British Airways, Gousto, Hotjar, and Otrium make fast sense of customer feedback.

4.Sentiment analysis helps identify friction in the customer journey, uncover ways to improve product experience, help support teams prioritize issues, and improve your CSAT and NPS scores.

What is Sentiment Analysis, Anyway?

Sentiment analysis is the process of understanding how customers feel about your product or service.

It’s a form of data science that uses AI, machine learning, and natural language processing (NLP) to uncover human feelings within a message or conversation.

Sentiment analysis tools analyze customer feedback from different channels–support tickets, phone calls, surveys, reviews, and more, in seconds. It then tells you:

  • Key issues customers are contacting you about
  • Sentiment behind each issue (e.g. Positive, Negative, Neutral)
  • Key negative and positive sentiment drivers
  • Increases and decreases in sentiment

There are four main types of sentiment analysis:

  • Polarity-based: Categorizes interactions as positive, negative, or neutral. This is the most basic form of sentiment analysis.
  • Emotion-based: A more advanced approach that categorizes interactions as happy, sad, angry, excited, annoyed, etc.
  • Urgency-based: Identifies the level of urgency in an interaction, useful for support teams looking to prioritize feedback.
  • Intention-based: This takes it a step further by identifying the overall intent behind a conversation, such as “interested buyer” or “churn risk”.

However, the most advanced form is called aspect-based sentiment analysis

It involves breaking down one conversation into several parts, called aspects. Instead of assessing the whole conversation at once, AI models analyze each aspect separately to reveal nuanced insights. 

SentiSum, our sentiment analysis tool, uses aspect-based sentiment analysis to uncover granular insights you can use. More about this in our technology overview.

📖 Related read: Customer Sentiment Analysis | Definition, DIY Template, & More

8 Ways How Sentiment Analysis Improves the Customer Experience (Feat. Real-life Case Studies)

Sentiment analysis has helped companies like Gousto, Hotjar, and British Airways solve major customer experience challenges. 

Here’s a gist of 8 different ways sentiment analysis improves CX, based on our experience working with different companies:

  1. Uncover friction points in the customer journey
  2. Reduce resolution times for high-priority issues
  3. Identify high-risk customers and reduce churn
  4. Map product improvements to what customers want
  5. Make fast sense of customer reviews
  6. Understand and improve CSAT scores
  7. Train agents to provide exceptional customer support 
  8. Build evidence-based cases for change

Let's look at each in detail.

1. Uncover Friction Points in the Customer Journey

Whenever a customer reaches out to you for support, it indicates friction.

These friction points, if they blow up to bad experiences, can cause customers to churn and turn to competitors, leading to revenue loss. 

As a support leader, you want to understand what these key friction areas are and how much of an impact they have on customers, so you can remove them before they turn into big issues. 

This is where sentiment analysis excels the most.

A sentiment analysis tool brings in customer feedback from all your channels (emails, phone calls, chats, surveys, reviews, and more) onto one place. Then, it auto-analyzes and tags each feedback and give insights into:

  • Topic
  • Sentiment 
  • Urgency 

For instance, this could look something like:

  • Topic: Damaged Packaging During Delivery
  • Sentiment: 94% Negative
  • Urgent: Urgent 

This means support leaders know exactly which friction areas they should be prioritizing to fix, in turn improving customer experience in the short and long run.

💡Case Study Spotlight

Gousto is a meal kit delivery service that’s been working with us to unify its voice of customer data across 9 channels.

Gousto plugged in each of their voice of the customer channels (from AWS Connect voice calls to CSAT surveys) onto our sentiment analysis tool. 

We then used real-time tagging and categorisation to provide the Gousto team with accurate and objective understanding of customer sentiment and the topics driving customer contact.

This not only showed Gousto key friction points that their customers were facing, but also empowered different teams across the organization to tackle them and improve customer experience. 

“Through using Sentisum we've significantly reduced the time it takes to unearth customer insights. We now understand these at a much more granular level, which allows us to quickly put actions in place to drive improvements.”

- Joe Quinlivan, Head of Customer Care @ Gousto

2. Reduce Resolution Times for High-priority Issues

Let’s say during your Voice of Customer analysis, you identified 50 issues that drive 99.9% of customer contact. How do you know which issues to tackle first?

Traditionally, support teams would address the most frequent issue first (e.g. Late Delivery) as this can lead to cost savings. But that might not always be the best solution. 

With an additional layer of sentiment analysis, you might discover that the “Late Delivery” issue doesn’t really bother customers.

Instead, you discover that another topic (e.g. “Damaged Goods”) riles customers up–driving negative reviews and bad word of mouth.

These issues to be resolved in a timely manner to prevent further reputation damage and revenue loss. 

After identifying them, your support team can quickly take action and offer compensation plans to customers affected.

💡Case Study Spotlight

Over the COVID-19 pandemic, James Villas experienced a large influx of Zendesk support requests from customers stuck in challenging and uncertain situations. 

Due to high volumes and complexity, the team struggled to prioritize urgent tickets—where a non-timely response would lead to a significant negative impact on the customer's experience.

Johannes, the Head of CRM, used SentiSum to automatically recognise and triage urgent cases within Zendesk.

With SentiSum’s built-in Zendesk integration, James Villas began automatically tagging urgent calls and sending them to a separate inbox for speedy resolution. 

This resulted in a 51% decrease in resolution time for customer issues within the first few weeks.

James Villa's customers now get fast customer service, especially when it's urgent and they need help the most—leading to a higher satisfaction rate and improved customer loyalty.

“With SentiSum doing the heavy lifting, the James Villas support team can focus on their job: making customers happy.”

- Johannes Ganter, Head of CRM and Digital Transformation at James Villas

3. Identify High-Risk Customers and Reduce Churn

Sentiment analysis highlights customers on the brink of churning, along with the specific reasons for their unhappiness.

For instance, our sentiment analysis tool revealed that over 300 customers contacted a meal-kit delivery company about “Poor Ingredient Quality”, with 92% expressing negative sentiments.

Screenshot of how sentiment analysis revealed problem ar

Our ChatGPT-like AI also summarized the impact this issue has on customers.

AI summary of key sentiment and issues

These customers with negative sentiment are at a high risk of switching to competitors.

To prevent this, a quick response is key. You can reach out with solutions, compensation offers, or even a simple expression of empathy–often, understanding and acknowledging a customer's issue can make a significant difference. 

This proactive approach not only helps retain customers at risk of leaving but also enhances the overall customer experience, reinforcing loyalty and satisfaction.

💡Case Study Spotlight

Gourmet liquorice creators, Lakrids by Bülow, used SentiSum to analyze customer feedback in their Dixa support tickets.

Towards the end of 2021, they saw a surge in support tickets about packaging issues, including damages during transportation. 

Based on this feedback, the Lakrids team improved their packaging and transportation processes. They also developed a compensation plan for affected customers.

A year later, customer complaints about packaging decreased by 26%.

“There are two ways that I look at the data: what kind of issues are customers having? How difficult is it to improve on this customer experience? When looking at it through these lenses, it’s easier to prioritize what improvements need to be done and when.”

- Løkke Engraf, Head of Consumer Service and Sales Support at Lakrids

4. Map Product Improvements to What Customers Want

Sentiment analysis isn’t just a tool for the CS team.

It’s also a great way to discover product feedback, enabling data-backed decisions to fill the development roadmap. 

When you’re aware of the issues that affect customers most, you can use that data to prioritize improvements and implement fixes before satisfaction dips lower. Then, you can keep monitoring support data to analyze the impact of your product decisions.

By aligning these offerings with customer sentiments, you can ensure that your product or service meet the needs of your customers.

💡Case Study Spotlight

Hotjar’s Customer Success Team has been using SentiSum to implement customer data into the product development process.

Our topic-based sentiment analysis tells them exactly what issues to prioritize for fixes and improvements for maximum impact.

Customer sentiment insights are now shaping their conversations with product teams, ensuring that updates are customer-driven.

Since these insights are backed by real numbers, it’s also easy to predict the impact that a product decision will have on the bottom line.

As they move forward, Hotjar plans to further involve the product team with SentiSum.

The goal is to enable the product team to independently access the SentiSum platform, directly engaging with customer support insights to refine their product strategy.

“The way I see SentiSum is, it's not about viewing product feedback, it's about discovering product feedback.”

- Nick Moreton, Director of Support at Hotjar

5. Make Fast Sense of Customer Reviews

Customer reviews are a goldmine of insights that can help you improve customer experience, product, and support teams. 

But traditionally, you'd have to read through each text and manually tag it with a topic. This is incredibly time-consuming and labor-intensive. It’s also simply not feasible if you have thousands of reviews to mine through. 

Manually tagging also creates a lot of bias as it depends highly on the perception and experience of the person doing the tagging. 

Sentiment analysis tools can remove this bias and save you massive time on manual tagging.

It uses Natural Language Processing (NLP) to read through your reviews (much like a human would) and sorts them into clear topics with complete objectivity.

These tools also come with an easy to use dashboard that shows you:

  • The overall customer sentiment and survey completion rate
The overall customer sentiment and survey completion rate - SentiSum
  • Biggest changes in sentiment
Biggest changes in sentiment - SentiSum
  • Key drivers of negative and positive sentiment
Key drivers of negative and positive sentiment - SentiSum

On SentiSum, you can also zoom in on each conversation for details on agent performance, call outcome, and improvement opportunities. 

Each customer support conversation - SentiSum

Analyzing reviews give you a 360-degree view of your customers. You can then lead improvement projects that boost customer satisfaction and reduce the number of negative reviews online.

💡Case Study Spotlight

British Airways Holidays came to us with 100,000s of customer reviews across multiple platforms.

Before SentiSum, the insights team would take a few hundred customer reviews as a sample and spend a few hours manually labeling each one with a topic. This was time-consuming and biased.

SentiSum helped British Airways Holidays analyze over 100,000 reviews in five minutes.

A quick check of the dashboard showed the BA Holidays team how many reviews discuss each topic and which topic contributes the most to positive and negative customer sentiment.

The machine learning engine also flagged specific customer issues for action by the BA Holiday team. 

With SentiSum, the BA Holidays team saved time, gained an objectivity that was previously unattainable, and no longer had to take a small sample size. They now have a detailed understanding across all reviews and surveys and provide high quality experiences for the customers.

“In less than 5 minutes, we are now able to understand the drivers of our advocacy from over 100k reviews.”

- Head of Customer Service, British Airways Holidays

6. Understand and Improve CSAT scores

Low CSAT scores are clear signs of customer unhappiness.

Many support leaders aim to improve these ratings. But they’re not sure what’s causing these low scores in the first place, so it's difficult to take corrective action. 

The answer lies not just in the CSAT surveys, but in the rich insights hidden within customer conversations.

The most effective strategy is to combine CSAT driver analysis with sentiment analysis of customer service interactions. This makes it easy to link specific complaints to their impact on CSAT ratings.

And while CSAT surveys aim to assess agent performance, the responses are often muddled by how other teams are performing, which is not under the control of the support team. 

By segmenting customer service data by department, support teams can pinpoint their specific issues and address them directly. 

You can then send other issues related to product and operation that drove poor CSAT ratings to the respective teams for prompt action. 

Ultimately, this comprehensive approach leads to an improved overall customer experience.

🎙️ Related podcast: Boosting CSAT scores and reducing first response times at Printify

💡Case Study Spotlight

Glammmup, a leading e-Commerce company, was struggling with a CSAT score of 62 when the industry average is 78. 

Their head of customer service, Emily, wanted to rectify this.

While hunting for better approaches, Emily realized they already had all the data they needed. They were sitting on a goldmine of customer data that they were not using: customer service conversations. 

Emily used SentiSum to:

  • Capture data from each customer at every touchpoint
  • Analyze customer sentiment from support conversations and map them to CSAT scores

Through this approach, Emily and the team were able to improve customer service quality and make company-wide changes based on what customers were saying.

After a year, Glammmup saw an increase in CSAT score from 68 (below industry-average) to 82 (above industry-average).

7. Train Agents to Provide Exceptional Customer Support 

Sentiment analysis can reveal gaps in agent training or areas where support staff need additional guidance. This could be in handling complex queries, managing angry customers, or providing more compassionate service. 

Using a tool like SentiSum, you can look at records of customer chats, phone calls, and emails. This helps you see how well their agents are doing and the quality of the calls.

Agent performance analysis and AI summary - SentiSum

With this information, you can train specific agents or the whole team better. You can help them improve how they talk to customers, understand the product more, or deal with tough situations, which can lead to higher customer satisfaction.

💡Case Study Spotlight

Scandinavian Biolabs, a hair-care D2C brand, attributed customer conversations to respective agents using Sentisum. 

They then used these insights to coach support team members on how to:

  • Handle difficult conversations
  • Deal with negative feedback
  • Provide resources for the customer

This ensures that the support team are leveling up communication together, and keeps customer satisfaction at the heart of every interaction.

8. Build Evidence-based Cases for Change

Support leaders often have “hunches” about what’s bothering customers, how they’re feeling, and what improvements they want to see.

However, they don’t have the data to “prove” their hunches. And more often than not, improvement plans are based on gut feeling rather than genuine impact. 

Sentiment analysis quantifies qualitative feedback (e.g. 94% of customers feel negative about X issue), so leaders can fix issues that make the most impact on customer experience. 

These tools give you the data and confidence you need to convince your CEO or other departments that something needs to be fixed—and it needs to be fixed now

This not only benefits your customers, but also prevents revenue leaks in the long run.

💡Case Study Spotlight

The Scandinavian Biolabs team was receiving thousands of tickets per month. They were spending a lot of time manually trying to understand which of these tickets were the top issues amidst all the noise.

With a sentiment analysis tool like Sentisum, they were able to get an objective view of their data and understand recurring pain points for customers.

Using this information, the team at Scandinavian Biolabs reprioritized their product roadmap to implement a new customer platform earlier than they had anticipated.

After this implementation, one of the biggest negative sentiment drivers decreased by 50% thanks to heightened awareness and informed product iterations.

“It’s very difficult to convince the rest of the team that something was important when you don’t have the data to back it up.

Insights from SentiSum started becoming the basis for our strategic and product meetings, giving us a sounding board for understanding our customers and their needs outside of customer surveys.”  

- Anders Reckendorff, CEO and Co-Founder at Scandinavian Biolabs

Frequently asked questions

Is your AI accurate, or am I getting sold snake oil?

The accuracy of every NLP software depends on the context. Some industries and organisations have very complex issues, some are easier to understand.

Our technology surfaces more granular insights and is very accurate compared to (1) customer service agents, (2) built-in keyword tagging tools, (3) other providers who use more generic AI models or ask you to build a taxonomy yourself.

We build you a customised taxonomy and maintain it continuously with the help of our dedicated data scientists. That means the accuracy of your tags are not dependent on the work you put in.

Either way, we recommend you start a free trial. Included in the trial is historical analysis of your data—more than enough for you to prove it works.

Do you integrate with my systems? How long is that going to take?

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What size company do you usually work with? Is this valuable for me?

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What is your term of the contract?

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How do you keep my data private?

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How Sentiment Analysis Improves Customer Experience - FAQs

Why is sentiment analysis important in customer service?

Sentiment analysis is important in customer service because it helps businesses understand how customers feel about their products, services, and overall brand. By automatically detecting emotions in customer interactions, companies can quickly identify and address issues that are causing frustration. 

This enables more proactive and personalized customer service, which improves customer satisfaction, reduces churn, and strengthens brand loyalty.

How can customer survey data help in understanding customer satisfaction using sentiment analysis?

Customer survey data provides valuable insights into customer satisfaction through sentiment analysis. By analyzing the emotions, opinions, and attitudes expressed in open-ended survey responses, businesses can gauge overall customer sentiment and identify specific areas of satisfaction or dissatisfaction.

Sentiment analysis tools automatically categorize feedback as positive, negative, or neutral. This allows companies to quickly spot trends, benchmark customer perception, and prioritize improvements to enhance the customer experience.

Review the 11 best survey analysis tools here.

How does sentiment analysis work?

Sentiment analysis involves using natural language processing (NLP) to determine the emotional tone behind words. It categorizes text into sentiments like positive, negative, or neutral. This process relies on machine learning algorithms that are trained on large datasets to recognize language patterns, context, and nuances. The outcome is a better understanding of customer opinions, trends, and overall sentiment towards products, services, or topics, which can be used for improving customer experience, product development, and marketing strategies.

How does SentiSum ensure accurate sentiment analysis?

SentiSum ensures accurate sentiment analysis through advanced natural language processing technologies and machine learning algorithms. It employs a custom taxonomy tailored to specific business needs, enabling granular tagging of customer feedback. Continuous learning and adaptation improve accuracy over time, allowing for precise sentiment detection and categorization tailored to each organization's unique requirements. For a comprehensive understanding of their methods, consider visiting their website directly.

What are the best sentiment analysis tools?

There are many sentiment analysis tools to choose from depending on your use case and feature requirements. Here are the top 3:

  • SentiSum: SentiSum is an AI-powered customer sentiment analysis platform that works across all support and feedback channels. It provides accurate and granular insights that are easy to use for support teams.
  • MonkeyLearn: MonkeyLearn is a user-friendly text analysis platform that offers sentiment analysis capabilities. It allows you to easily build custom sentiment analysis models without coding. 
  • Brandwatch: Brandwatch is a consumer intelligence platform that includes robust sentiment analysis features. It can process data from various sources like social media, news sites, blogs, and forums.

Find more sentiment analysis tools in our detailed roundup.

Is your AI accurate, or am I getting sold snake oil?

The accuracy of every NLP software depends on the context. Some industries and organisations have very complex issues, some are easier to understand.

Our technology surfaces more granular insights and is very accurate compared to (1) customer service agents, (2) built-in keyword tagging tools, (3) other providers who use more generic AI models or ask you to build a taxonomy yourself.

We build you a customised taxonomy and maintain it continuously with the help of our dedicated data scientists. That means the accuracy of your tags are not dependent on the work you put in.

Either way, we recommend you start a free trial. Included in the trial is historical analysis of your data—more than enough for you to prove it works.