The Beginner’s Guide to Customer Sentiment Analysis

Customer sentiment analysis — sometimes called opinion mining — uses software to conduct audio and text analysis to assess how customers feel about an interaction. Thanks to natural language processing (NLP), some tools have become so advanced they can actually detect human emotion and help you better understand how customers feel about your brand, products, and customer support experiences.

Sentiment analysis may involve reviewing support interactions, survey responses, customer reviews, and even content on forums or third-party review sites. You can track positive, neutral, and negative customer sentiments based on customer opinions.

Strong sentiment analysis software and processes can help you understand how well your company is meeting customer expectations and their overall sentiment and perception of your brand. You can use this information to improve their experience and improve critical business metrics like customer loyalty, satisfaction, and retention.

Why Customer Sentiment Analysis Matters

There are several different sentiment analysis use cases, each of which can provide crucial insights that can contribute to business and revenue growth when leveraged correctly.

Understanding customer emotions

Customer sentiment analysis helps you understand how customers feel about your brand, products, or services, providing insight into customer satisfaction and any areas for improvement. As a result, this can be a critical part of your customer analytics initiatives.

Improving customer experience

By analyzing sentiment, companies can identify pain points and improve the overall customer experience, leading to increased loyalty and retention. This should be part of how you measure your customer experience. As one of the most crucial parts of your sentiment analysis work, this allows you to determine how to better optimize touch points throughout the entire customer journey.

If your customers are complaining about slow support ticket responses, for example, you can look at your support team’s bandwidth. You may find a large number of basic inquiries are slowing your agents down, and you can relieve some pressure on your team by offering live support with AI-powered chatbots that act as the first line of defense.

Resolving issues proactively

Monitoring sentiment in real time allows businesses to flag negative feedback early. By doing so, you can act quickly to resolve potential issues before they escalate. You may be able to salvage strained customer relationships and prevent other customers from running into the same issues, reducing churn.

Driving business decisions

When used correctly, sentiment analysis can provide incredibly actionable insights that you can use to inform product development, marketing strategies, and customer service improvements. For example, if you consistently hear that your customers are struggling and at risk of churn due to a confusing onboarding experience, you may choose to implement customer success programs or include free guided onboarding in your process.

Key Challenges in Customer Sentiment Analysis

While customer sentiment analysis can be helpful, there are also multiple challenges that business leaders need to account for. Let’s review the most common difficulties and how to overcome them.

Understanding context

One of the toughest challenges is deciphering contextual nuances like sarcasm, slang, or regional language differences that can skew sentiment analysis results. Human language is complex, and so is emotion detection.

As a result, it’s important to use top-of-the-line customer analysis software that utilizes machine learning and AI to help you better understand context. Even with these tools, however, experienced data analysts or business leaders must review the information to ensure that you’re getting the full picture.

Handling large volumes of data

Analyzing sentiment across various channels — including social media, emails, reviews, and recorded calls — can be overwhelming, especially if you don’t automate the process. It ultimately requires robust tools to manage and interpret unstructured data.

This is one valuable use of AI in customer service. AI has incredible capabilities when it comes to analyzing large data sets, and it is often able to detect patterns that you may otherwise miss.

Classifying sentiment accurately

Correctly classifying feedback into positive, negative, or neutral sentiment categories can be tricky. In many cases, customers may express mixed emotions or share ambiguous statements, which can lead to misinterpretation.

For example, if a customer is frustrated because their order arrived damaged, but they express that they had a great experience with the customer support team, that can be confusing for AI. Ultimately, it may be a salvaged customer experience that ends on a positive note, but sentiment analysis software may classify it as “neutral.” Again, manual review and high-level software can both help with this.

Tracking in real time

Monitoring sentiment in real time requires advanced tools and processes to provide immediate responses and avoid delayed action. Slow responses can ultimately lead to customer dissatisfaction, especially if your team is unable to spot potential customer challenges quickly.

Look for software that offers real-time analytics functionality, and ensure your team has the right processes in place to identify and prioritize any potential threats to the customer experience as soon as they emerge.

Customer Sentiment Strategies to Try Now

There are different types of customer sentiment technology, including different data collection methods, customer sentiment analysis methods, and analytic models. Let’s review some of the best strategies businesses can implement now.

Keyword-based analysis

By using a keyword-driven approach, businesses can track specific words or phrases that indicate customer sentiment. This may include phrases like “excellent,” “disappointed,” or “frustrating.”

Some businesses also track product or service-related keywords to identify how customers feel about different components of their branded experience. This may include tracking mentions of product names or features.

Customer surveys

Surveys and feedback forms directly ask customers about their experience, providing structured data. Surveys may be most helpful when they offer “select a choice” questions and allow customers to provide open-ended feedback. This may include asking questions to track key performance metrics like a customer satisfaction (CSAT) score or a net promoter score (NPS), which tells you how likely customers would be to refer a friend to your company.

Social listening

Monitoring social media platforms for mentions of your brand or products allows you to gauge public sentiment and respond to customer issues or praise in real time. Social listening software can track mentions of your brand, brand terms, or brand products and services. Some businesses also use social listening to track what customers are saying about their competitors for improved market awareness.

NLP

NLP tools break down and analyze text data, using algorithms to understand customer emotions and classify sentiment in real-time conversations, reviews, and social media posts. NLP in customer service is often used to analyze text reviews, survey responses, and even audio or text files of recorded calls.

Helpful Sentiment Analysis Tools for Your Business

Customer sentiment analysis tools can help you detect patterns, identify potential concerns, and respond to customer needs proactively. You may be able to find dedicated sentiment analysis tools, but some are also included in call center analytics software. Let’s review five of the best tools currently on the market.

Solution	Core Features	Ideal For
Nextiva Contact Center	AI-driven sentiment analysis, real-time agent feedback, conversation analytics, CSAT/NPS tracking	Businesses focused on improving real-time customer interactions and sentiment tracking across omnichannel support
HubSpot Service Hub	Sentiment tracking for emails, tickets, live chats. Integrates with HubSpot tools	Small businesses seeking a cost-effective, integrated solution for customer satisfaction insights
IBM Watson Natural Language Understanding	Text analytics for structured/unstructured/metadata, customizable for unique business language	Large-scale operations needing advanced sentiment analysis across diverse datasets
Sprout Social	Social media listening, real-time sentiment insights, visualization of keywords/phrases, detailed reports	Social media-centric support teams aiming to enhance engagement and analyze brand sentiment online
Lexalytics	Multilingual text analytics, customizable NLP rules, high-speed processing	Global brands requiring fast, scalable sentiment tracking across multiple languages and regions

1. Nextiva Contact Center

Nextiva’s contact center solution integrates AI-driven sentiment analysis, tracking customer emotions during calls and providing real-time feedback to agents for improved interactions.

Nextiva also offers comprehensive reporting on sentiment trends to help businesses make data-driven decisions. You can track key metrics, like scores for CSAT, NPS, and customer sentiment, for clear, quantifiable data. Our software uses both AI and machine learning to analyze complex data sets, detect human emotion, and provide improved insights over time through our conversation analytics features.

2. HubSpot Service Hub

HubSpot’s Service Hub offers customer service software, and it may be a great tool for small businesses. The platform has automated sentiment tracking for emails, tickets, and live chat conversations. This can provide incredible insights into customer satisfaction and engagement levels, and the platform integrates seamlessly with HubSpot’s other suite of tools (which include marketing and sales hubs).

3. IBM Watson Natural Language Understanding

IBM Watson Natural Language Understanding can analyze text data to determine customer sentiment across a large volume of different interactions. It’s capable of collecting insights from structured data, unstructured data, and even metadata, allowing you to customize the platforms’ analytics to account for your business’s unique language.

While the software is incredibly advanced and provides an opportunity to leverage AI in customer experience tech, it’s best suited for large-scale and complex operations.

4. Sprout Social

Sprout Social is focused heavily on social media sentiment and provides real-time insights into how customers feel about your brand and offers tools to engage with your customers directly. This social media listening software is incredibly powerful, offering visualization into the most frequently used words or phrases, tracking user sentiment across multiple social platforms, and providing detailed analysis reports.

However, the company only offers social listening software, so businesses looking for overall sentiment analysis that accounts for survey responses, call analytics, or even third-party content may want to look at other tools.

5. Lexalytics

Lexalytics is a sophisticated NLP platform that delivers multilingual sentiment analysis, making it great for global brands that want to track customer sentiment across multiple languages and regions. The platform focuses exclusively on text analytics, and it’s fast — it can process up to 200 online posts per minute, for example, based on a pipeline model of customizable NPL rules. It is best suited for international brands that need to reach customers in multiple languages.

How to Put Sentiment Analysis into Action

Ready to gain more insight into your customer journey and leverage sentiment analysis to the fullest? Let’s look at key strategies that can help you do so.

Automate feedback collection

If you don’t automate feedback collection, there’s a good chance that it will be forgotten. Send out post-interaction surveys and leverage automated tools to analyze customer feedback for sentiment trends. Ideally, look for a platform that facilitates automated survey distribution and sentiment analysis, enabling quicker responses to potential issues.

Monitor social media and online reviews

Implement social listening tools to keep tabs on what customers are saying about your brand publicly. This is often the fastest way to spot everything from minor concerns to major potential crises, allowing you to respond proactively to negative sentiment and resolve issues quickly. You can also take advantage of positive praise to strengthen relationships with your existing customers.

Integrate sentiment analysis into your customer support system

Use tools like Nextiva’s AI-powered sentiment analysis to track customer emotions during live interactions, allowing agents to adjust their approach in real time. Our voice analytics can help you better understand how customers feel and identify potential training opportunities to improve agent performance.

Align sentiment insights with customer service training

Use sentiment data to train your agents on how to handle both positive and negative emotions from customers, leading to more empathetic and personalized support from your customer service team. You can use this information to set up role-playing scenarios for team training, improve call scripts, and even revise support policies if needed.

Start a voice of customer program

Voice of customer programs focus on gaining insight from customer data and feedback.

Senior business leaders in different departments should participate in a committee to regularly review feedback from multiple platforms (including customer surveys, social media listening, and even agent feedback). Discuss the data together, and prioritize company-wide improvements. This can help you better address potentially unmet customer needs, enhancing your brand’s reputation.

Understand and Improve Customer Sentiment With Nextiva

Sentiment analysis has so many valuable use cases in businesses today. It can improve operational efficiency, optimize the customer journey, shed light on blind spots, and automate repetitive tasks.

Nextiva’s AI-powered customer journey platform can help businesses improve their CX by understanding customers on a deeper level. You can also better identify potential concerns early, allowing you to be more proactive about CX issues across all channels before they become a bigger problem.

Discover how Nextiva can offer more insight into your customer journey today.