<img height="1" width="1" style="display:none;" alt="" src="https://dc.ads.linkedin.com/collect/?pid=102532&amp;fmt=gif">
Written on

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 a lot of text data to manage, analyze, and extract meaning from.

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. There are different types of text analytics that can be used to get the best results. They each serve a different 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 important to your success.

Understanding the Different Types of Text Analytics

As we explained in our blog “Text Analytics for Call Centers,” there are three main types of text analytics that 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, the goal of text analytics is to help you understand your customer issues. When you know how they feel about what you offer and sell, you can drive important changes and improve your product, business processes, and services to better fit your customers’ needs.

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 that 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 true volumes and trends of your customer queries and problems. From there, you can make better and faster decisions about how best to help your customers.

Get Actionable Insights from Contact Center Data with AI Text Analytics. Find  out more.

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 expressed in those interactions with customers, the greater your opportunity you will have to scale your customer support operation. By automatically examining 100% of customer text interactions—instead of just a small 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: During peak periods, text analytics can help you route easy customer support tickets to the “least trained” or temporary agents.
  • Automate your helpdesk: Text analytics can tag help tickets using AI to trigger automations in your helpdesk.
  • Prioritize tickets based on query type: Clear your backlog using text analytics to prioritize tickets based on 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 are performing. 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—as well as star performers—and then create training and coaching where needed. This means you can focus your training efforts on the exact customer queries you currently struggle with the most.

Readily Detect Volume Peaks in Customer Queries

What’s most important to your customers right now? With text analytics, you can discover what customer queries and keywords are driving the most volume in your contact center. From there, you can monitor sensitive issues and keep track of exactly what your customers are saying about any issue.

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 issue 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, let’s say text analytics reveals that “shipping” is a popular support topic month after month alongside “I did not receive my order.” This would be a great 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.

Use the Different Types of Text Analytics to Increase Customer Satisfaction

At the end of the day, 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 automatically analyze every text interaction for key insight. This information leads to better decisions made with accuracy and clarity.

By starting with what your customers are actually telling you and using real 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.Get actionable insights from your contact center data with text analytics. Find  out how.

Tags: Live Chat Quality Assurance, Call Center Quality Assurance, Text Analytics