Contact centers are the frontlines of customer interaction. They handle millions of conversations on a daily basis. And they have a huge impact on customer satisfaction, brand loyalty, and the financial success of an organization. That’s where contact center sentiment analysis comes in. Applying AI analytics to your contact center data can tell you how your customers really feel and help you improve the overall customer experience.

In this guide, we’ll explore why sentiment analysis matters for contact centers and what types of data you might want to use. We’ll also go through a detailed step-by-step guide to performing sentiment analysis on your own data using AI tools. And finally, we’ll cover what you should be doing with your insights afterwards to get the most out of sentiment analysis!

The Importance of Sentiment Analysis in Contact Centers

Contact center sentiment analysis is a powerful tool for unlocking your customers’ true feelings. It can help you make sense of your customer interactions at scale and understand what you need to work on. Once you know what’s making your customers frustrated or delighted, you can figure out how to keep your customers happy.

Key Benefits of Sentiment Analysis:

1. Improve the Customer Experience

Applying sentiment analysis to your contact center gives you direct insights into what you need to improve about the customer experience. For example, you might uncover customers are frustrated by long wait times or being put on hold. You can then use this information to refine your customer service procedures, or perhaps take on more staff to handle customer interactions.

2. Reduce Response Times

Fast responses matter now more than ever, Hubspot recently found that 90% of consumers consider an immediate response to their customer service inquiry as “important” or “very important”. Sentiment analysis can help contact centers pick up on unhappy customers before things escalate. AI-powered tools can automatically alert customer-facing teams to emails or chat interactions with negative sentiment.

For example, you might set up an alert for a significant spike in negative sentiment or negative sentiment associated with a specific issue. Flagging these issues in real time helps reduce response times and resolve issues more effectively.

3. Monitor Agent Performance

Sentiment analysis can also give you valuable feedback on your customer service agents. For example, you could use sentiment data to identify top-performing agents or pick up on areas for improvement. Call center sentiment analysis can also help you develop tailored training programs that better meet your customers’ preferences.

4. Anticipate Future Customer Needs

Sentiment analysis doesn’t just have to be reactive. Advanced AI tools are making it easier than ever to predict and anticipate future customer needs from historic sentiment data. For example, you might notice that customer frustrations increase at busy times of the year. You can then plan in advance to employ more staff or take action to reduce response times.

Key Components of Contact Center Sentiment Analysis

Sentiment analysis used to be a laborious process that involved combing through huge amounts of text by hand and identifying the sentiment. These days you can do the same job in minutes using powerful AI sentiment analysis tools that handle vast amounts of data almost instantly.

Artificial Intelligence (AI) and Large Language Models (LLMs)

One approach is to use Natural Language Processing (NLP) and Machine Learning. The main idea is that a sentiment analysis model learns from examples of text pre-tagged with sentiment. The training data can be created from reviews with ratings or open-ended survey questions linked to satisfaction scores. But it’s still a tedious process.

Most recently, Large Language Models (LLMs) like ChatGPT have been successfully applied to this task. Instead of providing training data, you just need to write a prompt. The accuracy of LLMs when it comes to sentiment analysis tends to be very high. AI and text analytics solutions specializing in feedback analysis, like Thematic, come in with pre-defined prompts that are optimized for accuracy, speed, and consistency.

Sentiment Analysis vs. Thematic Analysis

Thematic analysis identifies and interprets patterns, themes, and meanings within qualitative data. A theme captures what text is about regardless of which words and phrases express it.

For example, one person could say “the food was yummy”, another could say “the dishes were delicious”. In both cases, it’s the same theme. We could call it “tasty food”.

Sentiment analysis focuses on the feelings and emotions in the text. In this example both sentences have positive sentiment.

Data Sources for Contact Center Sentiment Analysis

Your data is like the raw ingredients in a recipe. The type of data you use for sentiment analysis will dictate the insights you get. For example, voice transcripts may contain richer details on customer issues. Social media interactions are usually shorter, more emotional, and more direct. Customers communicate differently over the phone, in person, or via email.

Let’s take a closer look at sentiment analysis data sources that are most commonly used by contact centers:

1. Voice Calls

Voice calls are the bread and butter for most contact centers. These direct, personal interactions can tell you a lot about customer sentiment. You can get deeper insights than other channels by analyzing voice elements like tone, pitch, and pacing. For example, you might pick up on emotions like frustration or dissatisfaction from how your customers speak. It’s also easier for customers to explain their issues in detail over the phone than typing out an email.

How to apply sentiment analysis to voice calls

There are a few things you need to consider when analyzing voice calls. Firstly you’ll need to transcribe your call data into text using speech-to-text technology. Once you have this data you can upload it to Thematic or another AI sentiment analysis tool. These tools focus on analyzing the content of the conversation by identifying the sentiment associated with particular topics.

2. Chat Transcripts

Many customers prefer digital communications over phone calls. Chat transcripts from chatbots and in-app messaging take up an ever-growing share of customer interactions. Unlike voice calls they don’t need to be transcribed before they can be analyzed. Customers typically outline their issues in the chat, so they can give useful insights into customer satisfaction and sentiment.

Chat transcripts need to be cleaned up and preprocessed before they can be analyzed. This is usually done automatically by AI sentiment analysis tools.

3. Email Correspondence

Just as important as phone calls these days are customer emails. It’s worth noting that emails tend to be longer than other types of text data like chat transcripts or social media chats. So you can get deeper insights into customer sentiment.

Sentiment analysis tools can pick up on things like urgency or frustration through the tone of the subject line or the content of the email. For example, the use of a lot of exclamation points probably indicates anger or frustration.

4. Social Media Interactions

Social media is another easy way to tap into customer sentiment. More and more customers hop onto social media to vent their frustrations with a company. This includes sending DMs or commenting publicly on their page or feed. These interactions can be more emotionally charged than other interactions, and customers often use emojis, hashtags, and GIFs which can all convey sentiment.

5. Surveys and Feedback Forms

Contact centers can also gather direct feedback by sending out surveys and feedback forms. For example, many businesses send out a follow-up survey after customer service interactions. These can include open-ended responses and ratings such as NPS (Net Promoter Scores). These can be a good way to gauge customer sentiment related to the customer service experience as well as wider issues.

6. CRM Data and Ticket Logs

Data from CRM (Customer Relationship Management) systems can be useful to combine with sentiment insights. This data can help you map sentiment across the customer lifecycle and better understand the customer experience. For example, you might link customer sentiment to financial data to explore which issues typically make customers leave your business.

Steps to Implement Sentiment Analysis in Your Contact Center

Here’s a step-by-step guide covering all the practical steps you need to get started with sentiment analysis in your contact center:

1. Assessing Your Current Capabilities

Begin by looking at your existing infrastructure. Think about what you’ll need during the different phases of sentiment analysis and identify any gaps.

• Data collection: Consider how your data is stored. That might include voice calls, chat transcripts, and emails. Do you have everything stored on one platform? Having all of your data in one place makes it easier to analyze using an AI analysis tool.
• Data processing: Some data like video calls may need to be processed before it can be uploaded to an analytics tool. Think about which tool you will use to transcribe your call data accurately.
• Analytics capabilities: Consider which tools you will use for sentiment analysis. Maybe you have an existing tool that can do the job. Or perhaps you need to look for a more advanced tool for more sophisticated sentiment analysis.
• Taking Action: Now is also a good time to think about what you will do with your insights. Do you have processes in place to share your insights and take action? Think about which teams or individuals will be responsible for moving things forward.

2. Choosing the Right Tools and Technologies

Choosing tools that align with your business goals and technical objectives is critical for getting reliable insights that actually make a difference. Here’s some key criteria to help you choose the best sentiment analysis tools for your organization.

AI Analytics Capabilities

Some AI sentiment analysis tools are highly sophisticated and can handle huge quantities of complex data. Others are simpler and better suited to smaller businesses who are just starting out with sentiment analysis.

When choosing the appropriate tool, consider how you will use it. Larger contact centers that require very detailed analytics and a high degree of accuracy should choose a more advanced solution. Smaller businesses may be able to start with built-in sentiment analysis tools that they already have access to on platforms like Zoho or Qualtrics.

Scalability

Choose a tool that can grow with your business. That way you won’t have to transfer over all your data and learn to use a new platform in the future because your needs changed. Check whether the tool you choose matches your current data volumes and if it offers price brackets for different-sized businesses.

Real-Time Analysis

Consider if you need to analyze your data in real time. Real-time analysis can be a useful tool for contact centers to monitor spikes in customer sentiment. For example, a significant drop in overall sentiment scores could indicate a major issue that needs urgent attention. These alerts can be sent directly to the appropriate team so they can take action right away.

Customization

Your business might need specific insights or custom categories beyond out-of-the-box functionality. Customization ensures the tool can adapt to your unique needs. Check if the tool allows you to create custom categories, tweak algorithms, or apply industry-specific models.

Multi-Language Support

If your business operates globally, you’ll probably need a tool that can analyze customer feedback and correctly identify sentiment across different languages and dialects. Assess which tools offer robust language support, especially if you need to analyze feedback from non-English-speaking customers.

Pricing

Pricing is often a decisive factor when it comes to investing in new tools. The best solution is one that fits your budget without sacrificing essential features. Evaluate which features your contact center needs right now and which are just nice to have.

Try Out Thematic on Your Data

Still not sure which sentiment analysis tool is right for you? The best way to find out is to try them out for yourself. Why not start right here with a free guided demo of Thematic using your own data.

3. Integrating with Existing Systems

Once you’ve got the right tool in your arsenal it’s time to connect it with your existing platforms. Consider where your data is stored and how you will combine it all in one place. For example, if you have many voice call recordings you’ll probably need to convert them to text before you can upload them to your AI analysis tool.

Here’s a few things to consider:

Plugins and APIs: Look for pre-built plugins and APIs that work with your current CRM and any other platforms where you store your data. This will make it a lot simpler to get connected.

Unified Dashboards: Aim to consolidate all your sentiment results and other relevant data in one place. This way you’ll be able to make connections between the different data points and pull out overarching themes and deeper insights.

Pilot Program: Larger organizations with a lot of complex data might want to start slowly with a phased rollout. You could test out applying sentiment analysis to one department or customer interaction channel before you decide whether to go ahead across the business.

Data Security: Don’t forget to take the relevant data protection regulations into consideration when you’re uploading or processing customer data. Set up secure data pipelines and make sure access is limited if you are dealing with sensitive customer data.

4. Training Staff and Ensuring Adoption

Your sentiment analysis technology tools are only as good as the people using them. For the best results make sure your teams know how to use them. Don’t forget to update your teams on any relevant updates or new features.

Customer Service Agents

Agents may benefit from training to understand sentiment insights and how to apply them to customer service interactions.

Supervisors and Managers

Supervisors and call center managers can use sentiment insights to evaluate agent performance and assess how their team can improve customer interactions. They may benefit from more in-depth and tailored trainings to get the most out of your sentiment analysis insights and tools.

Other Stakeholders

Sharing success stories can be a powerful way to prove the value of sentiment analysis and get buy-in from key stakeholders. For example, you might begin with a small pilot program and share the results across your organization before a full-scale rollout.

Measuring the Impact of Contact Center Sentiment Analysis

Implementing sentiment analysis is just the beginning. Now your analytics systems are up and running it’s crucial to assess the impact of all your hard work.

Let’s explore some different metrics and approaches to measure and leverage your sentiment analysis results.

Key Performance Indicators (KPIs) to Monitor

Metrics help you track the effectiveness of your contact center sentiment analysis program. And they also make it easier to communicate that information with key stakeholders. Here are our recommended KPIs for monitoring your success:

Customer Satisfaction Score (CSAT)

Customer Satisfaction Score (CSAT) is a pivotal metric in customer experience analytics that indicates customers' overall satisfaction. CSAT is measured by asking customers to rate their satisfaction on a scale. Higher scores indicate greater satisfaction. By cross-referencing your sentiment data with CSAT ratings you can assess whether your sentiment analysis insights and the action you’ve taken has actually made your customers happier.

Net Promoter Score (NPS)

Net Promoter Score (NPS) is a score created by Bain & Company that evaluates customer loyalty and brand appreciation. NPS data is typically collected by asking your customers to rank your product and brand from 0-10 in response to the question, "How likely are you to recommend us to a friend or colleague?". It’s another metric you can use to see if your sentiment analysis insights have had a measurable impact.

First Call Resolution (FCR)

First Call Resolution (FCR) measures the percentage of customer issues that are fixed in the first interaction. You should see FCR rates increase as you begin using sentiment analysis to successfully identify and address important customer issues.

Average Handle Time (AHT)

Average Handle Time is a standard contact center metric for tracking the length of customer service interactions. Sentiment analysis may allow you to identify ways to satisfy customers faster and thus reduce call or chat lengths.

Escalation Rate

Escalation Rate measures how frequently customer interactions are escalated to supervisors. A lower escalation rate could indicate that customer service agents are using sentiment insights to successfully handle tough situations by themselves.

Customer Effort Score (CES)

Customer Effort Score expresses how hard or easy it is for customers to interact with an organization. This metric was created by CEB, which is now part of consultancy Gartner. You can measure CES by asking survey questions like how easy it was to resolve an issue on a scale of 1-7.

A lower CES scores mean an easier experience, and usually more positive customer sentiment. Sentiment analysis can help identify friction points and enable teams to streamline customer interactions.

Continuous Improvement Through Feedback

Once you’ve got your insights and you’ve measured their impact, there’s still another step in the process. Sentiment analysis should be part of a feedback loop that allows you to continually improve your customer interactions. Feedback loops allow businesses to understand their customers' needs better and make informed decisions to enhance overall customer experience.

Don’t forget to follow up with your customers to show that their feedback was considered and appreciated. This is integral to 'closing the loop' with customers. It's not enough to collect feedback - customers provide their comments because they expect you to act! Report back on improvements to boost customer satisfaction and loyalty.

Final Thoughts on Contact Center Sentiment Analysis

Sentiment analysis is a powerful tool for contact centers who want to stay at the top of their game. It gives you deep insights into what your customers are really feeling, and helps you pinpoint exactly what you need to do to make them happy. Understanding customer sentiment enables you to better identify and fix frustrations in real-time before they escalate into major issues. And you can even predict problems that are likely to emerge in the future and take proactive action to avoid them.

Thanks to big developments in AI in recent years, sentiment analysis is now more accessible than ever before. With easy-to-use AI-powered solutions like Thematic, you can start getting reliable and actionable insights into your customer conversations in minutes. For more information about how Thematic works you can request a personalized guided trial right here.