Understanding how your customers feel about your product or service isn’t just nice to have — it’s essential for survival. In fact, customer sentiment analysis is now a tool for businesses looking to truly understand and respond to their customers’ needs, emotions, and expectations.

If you are a product manager or customer success professional working with customer feedback and analytics, you likely already know that customer sentiment analysis can transform product strategy and boost retention rates. 

In this short guide, we’ll explore everything you need to know about analyzing customer sentiment scores and how they can refresh your approach to customer experience.

What Is Customer Sentiment?

Customer sentiment is the emotions, attitudes, and opinions that customers have toward your brand, product, or service. Think of it as the emotional temperature of your customer base:

• Positive: These engaged users are your biggest fans and the most likely to remain loyal to your brand.
• Neutral: They don’t have strong emotions either way. They might use your product or service in a functional way without thinking too much about whether it’s the best option.
• Negative: These customers who have expressed frustrations with your brand and may be a churn risk.

You need to analyze these different profiles to understand what drives them, which brings us to the next question:

What is customer sentiment analysis?

Customer sentiment analysis is the systematic process of analyzing customer communications and feedback to understand the emotions and opinions expressed within them. 

Customer sentiment analysis typically uses sophisticated technologies like Natural Language Processing (NLP) and machine learning to automatically process and categorize customer feedback into positive, negative, or neutral sentiments.

Examples of Customer Sentiment Analysis Methods

Customer sentiment analysis is more than simply categorizing feedback as positive, neutral, or negative. When analyzing your customers’ sentiment, you need to look at several key methods:

• Sentiment Intensity: As you identify your positive and negative feedback, consider how intense the feeling is. Measuring these levels helps distinguish between slightly annoyed customers and those who are genuinely frustrated, allowing you to tailor your response accordingly.
• Context Understanding: Advanced customer sentiment analysis tools consider the context in which feedback is given. A customer complaining about a specific feature might still have an overall positive view of your product — the fact that they are bringing it to your attention rather than simply canceling their subscription is a good sign. When you understand the context of the complaint, you know exactly how to respond.
• Emotional Analysis: Go one step further than just “high” or “low” customer satisfaction — identify specific emotions like joy or disappointment. This emotional insight will guide your interactions, so you can gauge when to be more informal or empathetic with your customers.

Tip: Using the latest analysis tools alongside these methods can offer much more nuanced insights, which are the key to increased customer satisfaction.

How Technology Impacts Customer Sentiment Analysis

We have a world of powerful technology at our fingertips — and we should use it. There are plenty of sentiment analysis tools that use advanced systems to tap into the minds of our customers. Natural language processing is the foundation, enabling systems to understand the context and nuances of how humans communicate. 

For example, when analyzing a customer’s feedback about a new feature, NLP helps understand not just the literal meaning of the words, but also the underlying sentiment. Phrases like “This new feature is really something…” could either be highly complimentary or dripping in sarcasm. That’s where sophisticated sentiment analysis comes into play — it helps us gauge the true meaning of a statement based on context.

The Role of Machine Learning in Sentiment Analysis

Machine learning (ML) is another tool that helps with customer sentiment analysis. NLP works hand in hand with ML algorithms, which learn from patterns and trends in customer feedback.

These algorithms can process vast amounts of text data, identifying trends and nuances that might be missed by human analysts. The technology continues to improve its understanding of context, sarcasm, and industry-specific phrases, making customer sentiment analysis even more accurate.

Let’s say you’re analyzing customer feedback on a video game app. ML algorithms can understand technical jargon and common industry phrases, offering more accurate sentiment analysis than general-purpose tools. This specialized understanding is especially relevant for businesses in technical or niche markets.

How To Measure Customer Sentiment Score

How do you calculate customer sentiment score? There’s no one answer, as measuring customer sentiment can incorporate multiple data collection methods and analysis approaches. 

Customer feedback comes in many forms. Some examples include:

• Direct survey responses
• Social media mentions
• Support ticket conversations
• Other contact center interactions

Each source provides unique insights into different aspects of the customer experience, both positive and negative.

Net Promoter Score (NPS) surveys are a common starting point for sentiment analysis, as they offer a quantifiable metric for customer satisfaction and loyalty. However, the job isn’t done when you’ve collected and analyzed all the responses. To accurately gauge your customer feelings, you should combine these scores with more detailed sentiment analysis of open-ended feedback and other customer interactions, such as phone support conversation.

Follow these three steps for your customer sentiment analysis approach:

1. Gather feedback from multiple channels, including Net Promoter Score surveys, support conversations, and social media mentions. 
2. Use sentiment score analysis tools to process your feedback, identifying neutral, positive, and negative sentiments across different aspects of your product or service.
3. Combine these insights with other customer satisfaction metrics to create a complete picture of customer sentiment.

Turning Sentiment Analysis Into Action

As we’ve mentioned above, it’s not about how much research you’ve done or how many metrics you have, but how you use them. Customer sentiment analysis is only valuable when you turn the raw data and insights you’ve collected into real actions that improve your users’ journey.

When analyzing customer sentiment data, try to identify patterns and trends that tie into your business decisions. Look for correlations between sentiment changes and specific events, feature launches, or customer support interactions. 

For example, let’s say your customers are struggling with a feature update that’s overly complex, resulting in a high volume of negative feedback and low product adoption. In that case, you might choose to extend the period of time they can use the older version, or add new training materials, like in-product guides, to help them transition to the new one.

Challenges in Customer Sentiment Analysis

While it’s a no-brainer to make the most of the sentiment analysis tools on offer, you should be aware of the potential downsides. 

Understanding context continues to be a significant challenge, particularly when dealing with sarcasm or industry-specific phrases. Cultural nuances and language differences can also affect the accuracy of sentiment analysis.

The solution? Bring in the humans. Adopting a hybrid approach to sentiment analysis, where the use of automated tools is combined with human oversight, ensures cultural context and subtle nuances are properly interpreted. This will give you the most accurate data from your sentiment analysis efforts — and likely save you from any embarrassing faux pas from misunderstood phrasing.

The Future of Sentiment Analysis

As AI and ML technologies show no signs of slowing down, the capabilities of sentiment analysis tools will expand. We’re already seeing developments in real-time sentiment analysis, enabling businesses to respond to customer feedback in a flash.

The integration of sentiment metrics with other customer experience metrics will provide even more comprehensive insights into customer behavior and preferences. This is good news for your brand, as it will equip you with the info you need to create a more personalized customer experience. 

The growing trend of customization is one to keep an eye on. A good example is Spotify creating you a tailored music playlist on your birthday, based on your listening preferences. The positive impact of this level of customization cannot be underestimated as it’s guaranteed to make your users feel special, boosting your customer satisfaction score (or CSAT).

Customer Sentiment: The Next Steps for Your Business

So there it is, your crash course in customer sentiment. By now you know that effectively measuring and analyzing your organization’s customer sentiment, you can create better products, provide superior customer experiences, and build stronger relationships. In other words, you’ll make your customers feel really good.

Remember: Customer sentiment analysis is an ongoing process that requires continuous monitoring and refinement. Be sure to constantly seek fresh insights to ensure you’re keeping up with your customers’ changing needs and opinions, and can identify any major pain points before they escalate.

Also, bear in mind that the insights gained from customer sentiment analysis should inform not just customer service responses, but also product development, marketing strategies, and overall business decisions. Have regular conversations and “customer satisfaction check-ins” with these cross-functional teams to ensure you address all the positives and negatives. After all, it takes a village to provide the best customer experience.

If you’re looking to implement or improve your customer sentiment analysis, Userflow can help. Our platform provides powerful tools to identify, collect, analyze, and act on customer sentiment data. Start your free trial today or book a demo to see how we can help you better understand and serve your customers.