Spare time is not a luxury many companies have. Most contact centers are overwhelmed with work and can barely keep up with customer expectations, which are higher than ever before. So finding time to collect, analyze, and understand customer insight data is associated with too high operational costs, complexities, analytics expertise and a lot of time collecting the data.
Thankfully, AI text analytics is here to help make all of those tasks easier without much data analysis experience.
What is AI Text Analytics?
Text analytics is software that analyzes text, extracts insights on customer interactions and presents it in a readable, actionable form.
Here’s how it works:
- Through AI and natural language processing (NLP), text analytics breaks down written conversation into keywords and phrases
- Analyzes and understands the relationships between these keywords
- From there, it extracts and filters the text into a readable format
- It then interprets and categorizes this data to mine it for actionable insight, including conversational themes, trends, pain points and presents it in understandable form
In simple terms, it is like having a business analyst who can simplify immense amounts of information about your customer interactions in the contact center. Then, they can immediately provide you with insight into what is happening in all forms of your text conversations. From that information, you can improve your customer support with easy access to complicated information presented in a consumable, tangible way; creating a blueprint for improvement. You’ll know exactly how you can improve customer experience.
Text Analytics Helps You Take to Take Action
Utilizing text analytics allows you to analyze hundreds of thousands of customer text-based interactions quickly and easily. You don’t have to pick-and-choose which customer support tickets to read and analyze. The software will automatically analyze all of them at once and categorize each interaction to reveal pain points, detect customer sentiment, reveal customer intention, identify trends, and highlight necessary improvements.
How to Use AI Text Analytics in the Contact Center
But what does text analytics look like in action? How can you use it in your contact center to the benefit of your customers, agents, and company? It’s all about understanding how AI text analytics works, so you know what you can analyze and improve upon.
There are five areas where AI plays a significant role in parsing text data.
1. Understanding Customer Pain Points
What are your customer support tickets about? Do you take the time to analyze each ticket to spot trends and get to the root of your support issues? Probably not. That’s way too much time and effort to do manually.
With text analytics, you don’t have to analyze each customer support ticket yourself. The software automatically analyzes every text interaction—in just a few minutes and with the click of a button—to figure out your customers’ true pain points.
You’ll then be presented with a list of keywords and phrases that are used most often. For example, you might discover that “error messages” are a common issue. Then, you can ask the AI to list all terms/phrases associated with “errors” to get a better idea of the specific error issue that’s occurring.
Whether there is a quick fix to the pain point or not, by knowing you are gaining a better understanding of your customers’ most common pain points, and you can better prepare your agents in providing outstanding customer experiences.
2. Detect Customer Sentiment/Emotions
Another benefit of text analytics is its ability to detect customer sentiment/emotions. Are your customers angry when they contact you, but they leave happy—thus having had a great customer experience? Or do your customers leave their contact center interactions, even more frustrated than when they initially contacted you?
Customer sentiment about your service is associated with increased revenues, so you need to know if your customers are feeling positively or negatively about their interactions with you. Text analytics analyzes all conversations for emotionally laden words that show whether your customers are feeling positive or negative toward you, as well as how strongly they feel about your product or service experience.
From there, you can determine if your customers do not like something —as specific as your return policy or product quality—and then take steps to fix the issue and improve their experience. This ability to gain frequent feedback and implement solutions is what will influence your customer satisfaction scores. CSAT scores can be significantly improved with this approach.
3. Reveal Customer Intention
Do your customers seem to be asking questions that you already have the solution to on your FAQ page or in your knowledge database? The problem may be that your Help Center doesn’t directly correlate with your customers’ needs because you do not understand their intention. For example, the customer might be having a “password issue,” but what they’re asking about is “account login” or a “blocked account.”
Text analytics analyzes customer text-based queries including chats, emails, notes for their intention and the exact phrasing that is used to get help. You can then update your FAQ articles, your help center, and inform agents, based on the customer’s intention and then formulate your response to match.
4. Identify Trends
What recurring issues, questions, concerns, and feedback does your contact center deal with daily, weekly, monthly, and yearly? According to CX Moments, the truth is that 80% of tickets are either not categorized or wrongly categorized. Most are not able to be examined manually. Using text analytics can automatically categorize every text-based conversation, using commonly used keywords and phrases, so you can identify major trends.
You can break down your customer support interactions into the issues that make up the largest volume of support tickets and discover exactly what they’re saying. From there, you can watch how customer issues evolve over time, inform each department in your company about customer issues that affect them, and decide what needs immediate attention and what can wait.
5. Highlight Necessary Contact Center Improvements
Finally, your contact center team needs all the help they can get to make better and faster decisions to help your customers. But to figure out how to improve and scale your operations, you need to know what’s happening.
Utilizing AI text analytics, you can detect spikes in contact drives and uncover what is driving that volume—based on customer queries and keywords. You can then set up the text analytics software to constantly monitor those issues and even trigger automated helpdesk responses.
You can also:
- Prioritize tickets about important topics or backlogged issues.
- Route easy tickets to temporary or new agents, saving your best agents for the most difficult customer support issues.
- Uncover trends in customer questions and create a knowledge database or FAQ page that is more helpful.
- Discover customer support questions/issues that your agents struggle to answer and thus require more training.
- Find the issues that take up most of your agent’s time and create a solution.
Learn more about text analytics in the call center: