What makes customers stay loyal to a brand? It’s not just price, product features, or convenience — it’s how you make them feel.
If you run a business in a competitive domain like B2B SaaS, you have probably noticed that customer experience (CX) is now the new battleground where deals are won or lost. According to a finding by PwC, close to 80% of American buyers say that speed, convenience, and friendly customer service are their top priorities for a positive customer experience.
And yet, many businesses, especially in B2B SaaS, don’t really know how their customers feel about their brand.
And that’s where sentiment analysis can help. Sentiment analysis is a game-changing tool that can help you decode customer emotions hidden in feedback, reviews, and conversations.
As businesses move into 2025, knowing how to leverage sentiment analysis can do more than just improve CX. It can help you identify risk areas before they snowball into a churn — or build loyal customers for life.
In this blog, you will learn:
- What sentiment analysis is and why it matters
- How leading brands use it to transform CX and retain customers
- Practical steps to implement sentiment analysis for measurable results
What is sentiment analysis?
To truly improve customer experience, you first need to understand what sentiment analysis is and how it works. So what is sentiment analysis?
As an example, TechSmith, a screen-recording SaaS provider, used sentiment analysis to improve its website and product experience. It analyzed survey feedback from its users to pinpoint their frustrations and optimized their site’s user experience for better customer satisfaction.
Depending on which tool you use, you can use sentiment analysis to go beyond surface-level scanning of your customers’ emotions. For instance, from being able to interpret frustration in a support ticket to identifying delight in a product review, advanced sentiment analysis tools give you the ability to listen at scale.
At this point, it’s also important to understand the different types of sentiment analysis. Basically, there are two types:
- Emotion detection: This analysis identifies specific emotions like happiness, anger, or frustration in customer feedback. For example, analyzing a support ticket may show frustration tied to a recurring issue.
- Fine-grained analysis: Breaks down sentiment into levels, such as very positive, positive, neutral, or negative. This granularity gives you deeper insight into the intensity of customer sentiment.
The role of sentiment analysis in customer experience
Understanding your customers’ emotions is important to creating exceptional brand experiences. Studies show that 91% of unhappy customers don’t voice their concerns — they simply walk away. This silent churn can kill businesses, especially in competitive industries like B2B SaaS.
Sentiment analysis bridges this gap because it helps brands listen to emotions that customers don’t say out loud. It’s like using a stethoscope to detect anomalies in your customer interactions that might otherwise go unnoticed.
Sentiment analysis lets you evaluate your emails, reviews, or social media so that you can gain valuable insights into how customers feel and what you can do to win their business.
Leading brands in the subscription economy like Netflix and Zoom regularly monitor user feedback to identify pain points to improve their product and offer a seamless user experience. As a result, brands that act on these insights are usually the ones that have a loyal customer base and enviable customer lifetime value (LTV).
Practical applications of sentiment analysis
Sentiment analysis offers a wealth of practical applications across various business functions. It goes beyond understanding what customers say. Done well, sentiment analysis can help you improve your revenue projection and increase your retention metrics.
Here are four of the most common applications of sentiment analysis for businesses:
Implementing sentiment analysis in your business
To get the most out of sentiment analysis, you must first align your business goals with your broader CX strategy. The reason? You can get measurable business outcomes when your sentiment analysis efforts are aligned with your business objectives.
Here’s a step-by-step process to implement sentiment analysis in your business for a successful outcome:
Real-world examples of sentiment analysis improving CX
To better understand how sentiment analysis drives improved customer experiences, let's look at a couple of real-world examples from the B2B SaaS space.
1. Zendesk
Zendesk regularly uses sentiment analysis on its customer service tickets to identify frustrated customers and, in some cases, prioritize their cases before other low-priority issues.
Essentially, Zendesk separates interactions with negative sentiment to make sure its agents resolve those tickets swiftly and to the customers’ satisfaction.
This improves the customer experience and reduces churn. More importantly, they avoid the negative PR that an irate customer can spread online through a tweet, Facebook rant, or G2 review.
2. HubSpot
HubSpot does something similar. It scans customer support interactions and sales calls for sentiment analysis to positive or negative sentiments.
Their CX team then segments the customers to offer personalized follow-up solutions that are tailored to each account.
This practice has helped HubSpot increase customer satisfaction and retention rates while boosting revenue from upselling opportunities.
3. Spokeo
Spokeo, a California-based people search website, uses MeetRecord’s sentiment analysis capabilities to track customer interactions during sales calls. This lets them identify positive moments to reinforce strategies that work.
This approach has helped Spokeo view real-time feedback on how their customers feel during sales conversations, tailor its follow-ups, improve its customer satisfaction, and increase conversion rates.
4. Fyle
Fyle, an expense management platform, uses an AI-powered call monitoring and sentiment analysis tool to detect churn indicators.
With this, the company was able to identify at-risk customers earlier and take proactive steps to improve retention. The tool helped Fyle increase churn indicator detection by 3x, improve customer satisfaction by 2x, and reduce churn rates by 50%.
Challenges and considerations of sentiment analysis
There’s no doubt that sentiment analysis offers incredible opportunities to improve customer experience. But it comes with its own set of challenges.
Here are four of the most common challenges that might hinder the process of understanding your customers well:
1. Biased data
Almost all sentiment analysis tools base their technology on the garbage in, garbage out (GIGO) concept, i.e. the quality of output is based on the quality of the input you provide.
If your data is incomplete, one-sided, or poorly sourced, sentiment analysis can provide skewed results. For instance, if you analyze social media conversations alone, you may come up with extreme opinions which don’t usually represent the full spectrum of customers’ opinions from all quarters.
2. Cultural nuances
Phrases, idioms, or even emojis can mean different things across regions. A seemingly neutral phrase in one culture might carry a positive or negative connotation in another. Without accounting for these nuances, your sentiment analysis may miss the mark.
Here are two tangentially related examples of how relying blindly on sentiment analysis can lead to — thankfully not from the SaaS world.
Many years ago, KFC’s “finger-licking good” tagline in China got mistranslated to “eat your fingers off,” giving off cannibalistic vibes.
Similarly, Pepsi’s “Come alive with Pepsi” tagline was hilariously translated as “Pepsi brings ancestors back from the dead.”
3. Sarcasm and context
Algorithms can struggle with sarcasm or irony, often misinterpreting these sentiments. For example, if a customer says "Just great! Another app that doesn’t work," a sentiment analysis tool might pick it up as exciting (“just great!”) without understanding the full context.
4. Data overload
Processing vast amounts of unstructured data from multiple channels (e.g., social media, surveys, and support tickets) can sometimes overwhelm teams. Without the right tools and strategy, businesses risk missing critical insights or misinterpreting data.
To overcome these challenges, focus on standardizing your data collection process, use multiple sources, and keep refining your algorithms. To reiterate, MeetRecord makes this easier by providing smart sentiment analysis solutions that account for context and actionable insights tailored to your business needs.
Make sentiment analysis your secret weapon
Understanding your customers’ emotion can give you an edge over your competitors. But you can’t rely on guesswork to understand how they feel. Instead, you must use reliable tools to measure customer behavior and analyze their sentiments.
The good news is that most businesses still don’t prioritize sentiment analysis — and that’s clearly an advantage for you to get ahead.
Take the guesswork out of understanding your customers with MeetRecord. MeetRecord turns complex data into actionable insights and helps you bridge the gap between you and your customers’ expectations.
Ready to improve your customer experience in 2025? Book a demo with MeetRecord today to build deeper customer connections.
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