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    Types of Text Analytics & How they Improve Customer Experience

    Your contact center has a massive amount of unstructured text. From Tweets to Facebook comments, emails, live chats, call transcripts and help tickets, there is much text data to manage, analyze, and extract meaning.

    That is the main reason for applying text analytics. It’s an invaluable tool/technology used for organizing and interpreting unstructured textual information, so you can obtain data-driven, actionable insights that you can use to make decisions and take action.

    Text analytics is key to gaining valuable insights into your customer’s issues, including:

    • unmet needs
    • desired product/service features
    • problems with the product or service
    • intentions
    • attitudes and emotions
    • causes of satisfaction / dissatisfaction
    • brand perception
    • preferences

    But not all text analytics are the same. Different types of text analytics can be used to get the best results. They each serve another function based on how they categorize text, and understanding the value of each type of text analytics and how it can work for your call center is essential to your success.

    Understanding the Different Types of Text Analytics

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    As we explained in our blog “Text Analytics for Call Centers,” three main types of text analytics can be used to resolve the most complex customer problems.


    1. Descriptive Analytics: Gathers data from unstructured text to identify conversational themes and trends for a clearer picture of customer satisfaction, purchasing habits, and support issues over time.

    2. Predictive Analytics: Forecasts future events by interpreting text with the final goal in mind. This type of text analytics might review open customer support tickets to recommend the ideal number of agents needed to keep up with demand.

    3. Prescriptive Analytics: Leverages predictive analytics to create contingency plans for specific future outcomes. Teaches you how and why your customers engage with your products.

    How Text Analytics is Applied to Improve and Scale Your Contact Center

    In all cases, text analytics aims to help you understand your customer issues. When you know how they feel about what you offer and sell, you can drive essential changes and improve your product, business processes, and services to fit your customers’ needs better.

    Now, let us break down how these different types of text analytics can work for you to gain valuable insights that you can act on.

    Immediately Focus on the Issues that Matter to Your Customers

    What topics come up most often, and do your agents struggle with any topics in particular? Are there recurring issues your customers are dealing with that your company needs to fix? Does the FAQ page in your knowledge database match the questions most frequently asked by your customers?

    You can answer all of these questions and more with text analytics. AI will automatically identify and analyze the actual volumes and trends of your customer queries and problems. You can make better and faster decisions about how best to help your customers.

    QA reporting with automation and AI

    Scale your customer support operations by examining 100% of customer interactions

    The more you know about the support issues most commonly expressed by customers; and then have the ability to examine all of the customer issues being said in those interactions with customers, the more excellent your opportunity to scale your customer support operation. By automatically examining 100% of customer text interactions—instead of just a tiny sample—text analytics can help you:

    • Prioritize self-service opportunities: Analyze trends in customer questions to decide what FAQs are most valuable and what bots would be most helpful.
    • Better route customer inquiries: Text analytics can help you route easy customer support tickets to the “least trained” or temporary agents during peak periods.


    • Automate your helpdesk: Text analytics can tag help tickets using AI to trigger automation in your helpdesk.
    • Prioritize tickets based on query type: Clear your backlog using text analytics to prioritize tickets based on the query type.

    Monitor and train customer support agents based on skill and knowledge gaps.

    When you can break down every text interaction by topic and issue, it becomes clearer how your agents perform. Then, using this text analytics data, you can offer highly personalized coaching and training to increase customer satisfaction at every level.

    You can quickly and easily identify problems and areas for improvement and star performers and then create training and coaching where needed. This means you can focus your training efforts on the customer queries you currently struggle with the most.

    Readily Detect Volume Peaks in Customer Queries

    What’s most important to your customers right now? You can discover what customer queries and keywords are driving the most volume in your contact center with text analytics. You can monitor sensitive issues and keep track of precisely what your customers are saying about any topic.

    For example, you might find that “payment” is starting to trend alongside “mentions of defects.” This can quickly alert you to an issue with your online payment portal so you can contact IT and get the problem fixed. With text analytics, you can make better decisions about where and how you should be focusing your efforts.

    Analyze and Escalate Customer Issues that Take the Most Time

    Why are customers contacting your contact center? Do you know? Text analytics can help you find the customer issues that take up most of your agents’ time. Using a granular breakdown of all your tickets by issue type, the software can help you determine what issues are constantly occurring so you can handle them best.

    For example, text analytics reveals that “shipping” is a popular support topic month after month alongside “I did not receive my order.” This would be an excellent opportunity to update how you share shipping information with your customers and keep them up to date, so they don’t have to contact your call center. Or, if the issue is more complicated, such as “refunds,” you can specifically train a team of agents to handle all of these requests and then use text analytics to automatically escalate all related queries at the click of a button.

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    Use the Different Types of Text Analytics to Increase Customer Satisfaction

    Text analytics is all about making your contact center more customer-centric. As explained above, it makes the entire process of understanding your customers’ wants and needs less daunting by using AI to analyze every text interaction for critical insight automatically. This information leads to better decisions made with accuracy and clarity.

    By starting with what your customers are telling you and using honest conversations to build out your contact center, you can better commit to a customer-centric support strategy. It’s all about using the different types of text analytics to analyze, categorize, and report on actions and insight directly from your customers.


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