Customers talk to you every single day. You have hundreds, if not thousands, of conversations about your business, products, services, and support. If you listen, you’ll learn everything you need to know to improve your offerings. They’ll tell you, “I’m not happy with this…” “I don’t understand this…” or “I really wish you offered this…” And their expectations are higher than ever before.
It’s an endless supply of insight that you can tap into to build a better business. After all, when you know what your customers need, want, and enjoy, you can provide it. The key is to collect, analyze, and understand exactly what your customers are saying—in their own words.
That’s the power of AI text analytics for customer feedback.
What Is Text Analytics?
Text analytics takes every text interaction with a customer and automatically extracts critical information regarding sentiment, intention, trends, and concept. It then breaks down this important data into actionable insights for a holistic view of the entire customer experience. You’ll come to understand what your customers are saying, how your agents are handling customer requests, and what you can do to improve.
And as long as it’s text you have to analyze, text analytics can be very effective. You can use it on every channel, from live chat to email, call transcripts, to help tickets, and social media. You can even use text analytics on historical text, aggregating the results and creating a repository of information to build predictive models for successful business operations in the future.
Best yet, text analytics isn’t limited just to customer support and the contact center. Insight can be used by other departments as well—marketing, sales, IT, and product For example, marketing might use the software to track the success of a new promotion code while IT tracks the release of a website update and R&D tracks customer response to a new product.
Types of Text Analytics
But not all text analytics software is the same or offers the same benefits. Text analytics is typically broken down into three main types:
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.
Predictive Analytics: Forecasts future events by interpreting text and making recommendations for keeping up with demand and trends.
Prescriptive Analytics: Teaches you how and why your customers engage with your products to create contingency plans for specific future outcomes.
Depending on your company’s needs, one type of analytics may be better suited for you than another. But no matter which you choose, they all work in a similar way—reading, analyzing and interpreting the text to provide insight into your customers and their interactions with your business.
How Does Text Analytics Work?
So, how exactly does text analytics work? Using real-time analysis, text analytics identifies, extracts, and filters unstructured text. It then transforms that data into a readable format. From there, text mining digs deeper into every conversation to pull out essential keywords, phrases, and context. With that knowledge, you can take actions within your contact center and beyond.
To simplify, let’s break down text analytics into just four steps:
Step 1: Text analytics reads 100% of unstructured (open/free) text available through customer surveys, chats, support tickets, social media, email, voice transcripts, etc.
Step 2: Artificial intelligence, machine learning, and natural language processing then automatically analyze this text to detect keywords, relationships between words, problems, emotions, intentions, preferences, etc.
Step 3: The text analytics software determines sentiment and what the interaction is about—product mentioned, problem reported, question asked, etc.—in order to categorize each conversation appropriately.
For example, the software might gather all conversations that include the phrase “payment problem PayPal,” “promo code 1234,” “shipping costs,” and “customer support help.”
Step 4: These new categories and metrics are applied to the entire database of customer conversations so that, in time, you can trend them, correlate them, prioritize them, detect root causes, and understand friction drivers.
And since all four steps happen automatically, integrating text analytics into your current QA solution and workflow is a relatively straightforward matter. It should integrate seamlessly.
Text Analytics Workflow
Let’s take a look at the normal workflow of a contact center manager trying to answer the question, “How often do people complain about X?”
Without text analytics, she would have to ask agents to manually go through their conversations and track when “X” was brought up by customers. Or she might have to ask for an estimate of its use based on memory. Or she might rely on agents correctly categorizing all help tickets manually, which is cumbersome and unreliable.
With text analytics, answering the question becomes much easier. The contact center manager can simply open the software and look at all comments about “X” by performing a simple search. She can then pull-out snippets around the keyword for a fuller understanding of the context. Then, in just a few clicks, text analytics offers a full analysis of the complaint alongside examples and trends—all in one shareable report.
Why You Need Text Analytics?
- 2.5 quintillion bytes of data are generated daily, and as much as 90% of that data is unstructured.
- 57% of customers would rather contact your company via text-based messages versus voice-based support.
- And 53% of customers believe their feedback doesn’t go to anyone who can actually act on it.
In the most basic terms, text analytics acts as a business analyst who automatically simplifies immense amounts of information about customer interactions to provide insight. It helps you create a blueprint for customer experience improvements in tangible ways.
This results in the ability to:
- Better understand how, why, and when your customers contact your company.
- Drill down into customer needs and identify trends.
- Track customer feedback about new and existing products and services.
- Identify areas for improvement based on how agents interact with customers.
- Better improve and scale your customer support by detecting self-service opportunities.
- Alert your contact center, in real-time, to faulty processes that could be generating extra costs.
The truth is that there are always customers waiting to interact with your business, and they’ll use whatever means necessary to contact you. But while your contact center might be able to keep up with conversations, analyzing every conversation is another matter. AI text analytics means the analysis happens automatically. You can just sit back, relax, and reap the benefits.
But it’s important to remember that text analytics is only one piece of the puzzle when it comes to understanding your customers. Customer support is complicated. It’s a multi-faceted wheel that includes everything from a self-service knowledge databases to help tickets, social media, emails, and more.
Text Analytics VS Speech Analytics VS Quality Assurance
The true value of a customer support program comes when all of your technology works together to give you a more complete view of your customers’ wants and needs.
Voice and speech analytics software, while important, only analyzes one piece of the customer experience. It is designed to translate audio to text, analyze words and phrases, and then conduct in-depth research to understand and interpret text to determine performance and accuracy. It’s “multi-channel” in name only so applies only to the voice channel.
Tickets and chats offer a different dataset than voice conversations and require text analytics. Text analytics, as we’ve already explained, analyzes unstructured text to extract insight into what is happening. It offers a similar interpretation ability as speech analytics but for text instead of voice.
Quality assurance (QA) is an umbrella which both speech and text analytics solutions can function very powerfully. The key is to integrate these technologies into the QA process where they make the most impact: speech analytics for monitoring and analyzing calls and text analytics for monitoring and analyzing text-based conversations.
A comprehensive QA program combines contact center scorecards for quality assessment, learning management for applying the right agent training, as well as text and speech analytics into one platform. Ultimately you will need all of these solutions to make better more accurate and informed decisions with clarity, and to implement real and measurable improvements to the customer experience.
If Your Contact Center Uses Spreadsheets, READ THIS
Now that you understand what text analytics is, how it works, and why you need it, let’s talk about actually implementing it into your contact center. What direct pain points can text analytics solve for your contact center agents and managers? A lot more than you might think.
There are eleven common use cases for text analytics in the contact center.
1. Discover What Matters to Your Customers
Why do your customers contact you? Do you know their pain points, what they need, what they like, and what questions they have? To get to the root of your customer support issues, you have to analyze every single interaction and pull-out trends. That’s what text analytics can do.
Using AI, text analytics automatically analyzes everything your customers are saying. You’ll then be presented with a list of keywords, phrases, and sentiments (positive and negative) to broaden your understanding of what your customers are saying. From there, you can draw data-based conclusions about what matters most to your customers.
You can look back on past conversations and track customer interaction history to see if/when a particular issue occurred. This solves a big weakness of manual categorization. With text analytics, it doesn’t matter if an issue was closed. You can still historically analyze the problem and monitor the solution going forward.
2. Detect Revenue Impacting Problems
“If it ain’t broke, don’t fix it.” But what if it is broken? How do you quickly detect when something is a problem that’s costing you money? With text analytics, you can rapidly detect revenue-impacting problems such as promo codes not working, payment gateway issues, website malfunctions, etc.
You don’t have to pick and choose which customer support tickets to read and analyze. Instead, you can analyze hundreds of thousands of support tickets at once to reveal revenue-impacting problems. From there, you can deep dive into the problem tickets to quickly determine exactly what the problem is and how to fix it.
If it’s a common user error, you can report the problem to your marketing team to improve UX and decrease the issue. If it’s a broken payment gateway, you can alert IT and fix the back end. If it’s an agent training issue, you can alert supervisors about the knowledge gaps.
3. Track Specific Issues as They Appear and Until They Are Solved
Now, let’s say you have a known problem with printing, and you want to track exactly what issues your customers are experiencing. You can assign “printing” as a topic to track in text analytics. From there, you can track when it increases in frequency, what exactly is being said in context, and when the issue finally disappears (is resolved).
And the issue can be as specific or general as you want. For example, you can track something as general as “payment failed,” or you can be as specific as “promo code 1234.” Text analytics will gather all instances of these keywords, including associated sentiment, so you can keep on top of exactly what is happening and how directly it impacts customer satisfaction. You can even set up tracking for future issues and potential problems, such as upon the release of a product update or for an upcoming sales promotion.
4. Detect Trending Topics, Questions, and Complaints
There are always two approaches to text analytics.
(1) You can configure text analytics to monitor a specific keyword and then track it (as we explained above). For example, you can track people talking about “payment failed” and associate the responses for your queries.
But what if you don’t know what you’re looking for? How can you uncover what’s trending when you don’t know where to start?
(2) Text analytics can tell you what you should be looking for. Without any input from you, it will pull out trending keywords to alert you to popular topics, new issues, and common complaints. This happens without your supervision and is a great way to keep a pulse on your customers.
5. Get Alerts on Regulatory Problems
What about those big issues? We’re talking about compliance issues, website crashes, significant code errors, nails in food, asking a customer for private data, etc. When it comes to the big issues that could shut your company’s doors, it doesn’t matter how often they occur. Even once is too much because their importance is so significant.
If you are manually monitoring customer support, these types of problems could slip through the cracks. You’re relying on your contact center agents to recognize the severity of the situation and to report the issue to the right manager. But with text analytics, AI is trained to automatically search for these kinds of issues and send alerts as they happen. This helps you reduce compliance, regulatory, and legal risk.
6. Prioritize and Allocate Backlogged Tickets
Even the best contact centers can have trouble keeping up with customer demand. If you have a long list of backlogged tickets that have not been looked at by any agents yet, the queue can feel overwhelming. You may not know what to open first or how to prioritize tickets.
This is a big use case for text analytics. The software can classify tickets in the waiting queue, helping you prioritize and route customer inquiries based on assigned criteria. You can prioritize by keyword, topic, sentiment, high frustration, regulatory words, and more. Then, you can assign each ticket to the agent best trained to answer the customer’s needs.
7. Complete Root Cause Analysis of Problems
Let us assume you uncover a problem that’s trending up—a payment problem—what do you do with it? You escalate it to the website team, but then they come back to you and want more information. They want to know exactly what people are saying, so they can figure out where the error is and fix it.
With text analytics, you can delve into the problem, correlate it with context, and determine exactly what the issue is. You can then share this information with IT, and they can implement the adjustments needed.
Root cause analysis is also important for personalized coaching and training. When you are able to break down every text interaction by topic and issue, it becomes clearer how your agents are performing and where there are knowledge gaps. From there, you can focus your efforts on:
- the customer issues that take the most time,
- tickets where sentiment leans negative,
- queries that have the most repeats,
- and issues where customer satisfaction is at risk.
8. Improve Self-Service Capabilities
Customers prefer knowledge bases over all other self-service channels. But unless you review every customer support ticket, email, and live chat, you might miss common questions that would be better served by a Knowledge Base rather than your agents. You can improve self-service capabilities with better FAQs and bots with text analytics.
AI text analytics automatically tags and divides every text-based interaction into common topics. From there, you can delve into high-volume queries and create FAQs based on that insight. This Knowledge Base—both internal and external—can be used to more quickly resolve issues, fill in any information gaps, and increase customer satisfaction.
Not only will this save your agents time, but you’ll also ensure that your customers have the material they need to help themselves.
9. Gain Unsolicited Customer Feedback
Your contact center receives feedback on everything from specific product/service frictions to issues related to bugs. And you already know what features your customers love and can’t live without. With text analytics, your contact center can become the epicenter for unsolicited customer feedback for all your products and services.
This means your contact center will play a critical role in taking design and development to the next level and giving your product teams what they need to be successful.
10. Detect Customer Satisfaction Levels
Are your customers happy with your contact center service? Do they feel satisfied after dealing with you? Are they generally angry, annoyed, frustrated, upset, happy, or anxious after dealing with your agents? You need to know if your customers feel positively or negatively about their interactions with you. And that’s what text analytics offers.
You can analyze all text conversations and highlight emotionally-laden words that show how your customers feel about you—positively and negatively. You can even determine how strongly they feel about your products or services.
This ability to understand your customers’ emotions is what will have the greatest influence on your customer satisfaction levels. You can learn how to adjust your customer support approach to make sure that customers leave their conversations with your agents in a better place than when they started.
11. Complete Quality Assurance Programs
There are also a number of quality use cases for text analytics.
The first one is to use text analytics to pick conversations that are worth auditing. Normally, the way that QA managers select conversations is by using a small sample that is relatively random. What text analytics can do is focus on conversations concerning common problems that require more attention.
For example, let’s say that 22% of customer interactions have to do with the keyword “payment problems.” With text analytics, you won’t have to randomly audit multiple conversations to find one that fits. Instead, you can specifically choose to audit a clearly marked “payment problem” instance to gain better insight.
You can also pick conversations to monitor for quality, based on friction. Let’s say there’s a trending topic, “cannot print.” With text analytics, you can connect this phrase to overly long conversations that take a lot of agent time and energy. What this may mean is that more training is needed, IT needs to get involved, or print issues are multi-faceted and are always brought up in conjunction with something else. Whatever the case, that knowledge is power in the hands of your QA managers.
Lastly, you can use text analytics in conjunction with self-scorecards and learning management systems to complement your overall QA program. You can match up scorecard insight with text analytics trends to see if agent opinions align with customer opinions. And you can craft new training programs and courses based on trending problems as highlighted by text analytics.
How Text Analytics Helps You Meet Overall Business Goals
Text analytics isn’t just valuable for the contact center. When used correctly, text analytics software provides pivotal information for almost every department: quality assurance, sales, product development, marketing, IT, and customer service. Here’s exactly what’s at stake for companies that rely on text analytics to make an impact.
Better Understanding of Customer Activity Drivers Across Departments
As we’ve already hinted at a few times above, understanding customers is not just for call centers. With text analytics, the call center becomes the hub of customer information that can then be shared with every department in your company. And each department—Marketing, Sales, IT, and Product—can keep track of the customer activity most important to them and most necessary for improvement.
- Marketing can track customers talking about a new promo code, a social media advertisement, a TV commercial, an email blast, and more.
- The Product Team can track the product and service updates, new releases, product sales, and various product feedback.
- Sales can track customer satisfaction with sales packages, discounts, shipping costs, and more.
- IT can track a new landing page releases, shopping cart abandonment, website UX, app use, etc.
It’s about giving your call center the ability to review all customer interactions to find out what exactly is driving activity based on trending keywords, phrases, topics, and sentiment. You’ll understand common problems, rising concerns, potential issues, and areas from satisfaction. And then, from there, you can disseminate that information to each department so they can figure out what can be done to improve and what they’re already doing well.
Improve Training and Onboarding for HR and QA
Where do your employees fail and succeed in their duties to your customers? Are there any knowledge gaps that impact your revenue generation? With text analytics, your HR and QA teams can detect training opportunities and root causes with one-click drill-downs.
By auditing conversations—selectively and as a whole—management can quickly and easily find areas of friction, uncover star-performing employees, and reveal knowledge that is lacking. From there, HR can come up with an appropriate onboarding process and continuous training while QA finds the common topics, problems, and complaints that require additional focus.
Bottom line, text analytics provides insight into known and unknown improvement opportunities based on real-time data.
Gain Critical Feedback on Products and Services for Product and Sales Teams
Customers come to contact centers with feedback on features, new product questions, bug fixes, and more. It’s information that’s often missing from customer surveys—which only 10% of customers actually respond to in any case. With text analytics, your product and sales teams gain insight into what your customers actually care about in their own words.
Through AI and language processing, every support ticket, email, and chat message becomes a product and services roadmap to success. Text analytics highlights even the subtlest feedback so you can improve features and debug issues based on your customers’ priority. It’s an easy way to take your R&D to the next level.
Track Marketing and Messaging Success
Insight into text data can inform your marketing team about the success of their discounts, specials, and promotions. Across channels, they can track keywords and phrases—such as “promo code 1234” or “summer special”—to see if customers are aware and interacting with what they’re offering.
With text analytics, marketing can easily and quickly calculate the cost and benefits of their messaging. From there, they can make judgment calls about their outreach efforts and make adjustments as needed.
Uncover Website and UX Issues for IT Teams
IT teams can use text analytics to make customer-centric design a priority. They can perform A/B testing combined with customer feedback to see what works well, what needs work, and how best to present information. It’s all about making the best website and UX possible.
Best of all, they can use text analytics to track known issues by keyword to see if the fix provided was successful. It’s valuable insight for everything from payment systems to homepage navigation, customer engagement, and shopping cart abandonment.
Choose the Right Text Analytics Software
At the end of the day, text analytics software is meant to automate and simplify repetitive tasks. It should take work off the plate of your managers and agents by using AI to properly review and analyze every text conversation automatically. The best systems are all about ease-of-use and helpful features.
Here’s what that looks like.
Key Features Needed in Your Text Analytics Software
In general, look for a text analytics solution that prioritizes ease of use, onboarding, accuracy of findings, speed of analytics, and integration with your help desks. This means:
- You shouldn’t need IT teams to use your text analytics solution. Instead, discovery and topic configuration should be available in self-service mode, without requiring a project manager (IT specialist) every time.
- It should be easy to onboard new users as you add them. And you should have access to help and support when needed to make things better or coach new users.
- The text analytics data has to be accurate and fast with insight into whatever your particular need is. You should be able to see how often customers mention a specific issue, and in one click, read snippets to understand what works and what doesn’t.
- It should integrate with your help desk, live chat solution, sales team, etc. Look for integrations with Zendesk, Freshdesk, intercom, Salesforce, LiveChat, Zoho, Slack, Kayako, HubSpot, TeamSupport, etc.
- The solution should be able to absorb information from multiple sources and draw conclusions that can be shared with other teams and departments as needed.
The whole idea of text analytics is to do away with time-consuming manual tagging and categorization of text. Using AI, it should automatically categorize, and trend contact drivers across hundreds of thousands of customers so you can make better and faster decisions. It’s all about gaining insights you can act on.
Improving Customer Experience and NPS Through Quality Assessment
That’s why a few other key features your text analytics software needs, include:
- Real-Time Analysis: A real-time understanding of your text conversations allows you to make immediate improvements and offers alerts when it comes to compliance.
- Post-Interaction Analysis: After every text interaction, you should be able to perform a detailed analysis to better understand reasons for contact, product mentions, quality assessment, sentiment evolution, intent, and more.
- Backlog Analysis: You should be able to clear your backlog by prioritizing old tickets based on query type.
- Out-of-the-box Dashboards: Customizable dashboards should help you keep an eye on the issues that are most relevant to your team at the click of a button.
- Reporting: Reports should automatically summarize key data from your text interactions with graphs, tables, and more detailed breakdowns.
And to make sure all of these features and capabilities work well, look for rich use cases that show the text analytics software in action. There should be multiple customers who are willing to share how the software impacted their business. Ask for a use case for each of your company’s top priorities.
Add Text Analytics to Your Contact Center Today!
The era of relying only on direct surveys and hoping for answers is over. No longer should you waste time looking at random customer support conversations and trying to uncover important feedback. With text analytics, the customer tells you what’s working and what is not working.
Every department in your business can benefit from unsolicited customer feedback. You can use it to focus your priorities directly on revenue-generating issues. This saves you money, decreases wasted time, increases productivity, improves agent engagement, and enhances customer satisfaction.
The key is using text analytics to truly understand your customers. When you know what they are saying about your company’s services, products, and support—in their own words—you have a valid starting point driving positive, important, and realistic change.
At the end of the day, knowledge is power. And text analytics is all about gaining the insight you need to make better business decisions and business improvement.
Learn more about Scorebuddy’s text analytics solution today!