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Customers expect better support than ever before—no matter the channel they use to contact you. Whether they call your contact center on the phone or communicate via live chat, your customers want the same exceptional service. And that’s where call center text analytics comes into play.

Text analytics helps your business be customer-centric no matter the channel by gathering insight during every text-based interaction.  It helps your company go beyond great products and good service to provide your customers with what they need and want most---great customer service.  Topics that you want insight about are almost always covered in customer interactions with your call center. 

The key is collecting the right text analytics data for the 57% of customers who would rather contact your company via text based messages versus, through voice-based support. The good news is that this type of data is everywhere. You can find it on social media, in purchasing history, support tickets, over live chat, in email, and more.

In this blog, we take an in-depth look at the power of call center text analytics. So, let’s dive right in.

What is Call Center Text Analytics and How Does it Work?

Today, more than 41% of customers expect live chat on your website. And if a customer contacts you on their mobile, then that number is as high as 50%. The problem is that few customer support teams have a way to monitor, analyze, and understand text-based interactions.

Text analytics software analyzes text to extract insight on sentiment, emotion, problems, trends, language, and key phrases. Through natural language processing and machine learning, the software automatically reviews every text-based channel—live chat, email, transcript, and customer support tickets—to provide you with a holistic view of the customer experience.

Using AI for real-time analysis of every text interaction, the software monitors your conversations to detect and breakdown important information. It can segment and detect trends in customer behavior and opinions to offer real-time conversational guidance. In addition, it aggregates and creates a repository of your text interactions to build predictive models for successful call center operation.

Text analytics works by:

  • Identifying keywords and phrases within text conversations.
  • Extracting and filtering this critical text for analysis.
  • Transforming the extracted text into a readable format that can be interpreted by AI.
  • Mining the text through special algorithms to identify essential insights:
  • Sentiment: This categorizes text conversations as positive, neutral, or negative.
  • Intention: This mines text conversations for specific desires among users and consumers.
  • Trends: This takes large textual data sets and identifies major shifts in consumer behavior.
  • Concept: This classifies and ranks text conversations by predetermined criteria for service and operational improvement ideas.

Why Does Your Call Center Need Text Analytics?

The truth of the matter is that your customers are not always going to fill out your product surveys or tell you exactly what’s going on or causing friction. But if you implement call center text analytics, they don’t have to.

Text analytics software uses artificial intelligence to help you:

  • Better understand how, why, and when your customers use text-based channels to contact your company
  • Drill down into text interactions to better understand conversations and identify trends
  • Track customer feedback about new and existing products and services to ensure issues are resolved
  • Identify areas for improvement when it comes to how agents interact with customers via text
  • 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

How Does Text Analytics Fit Into the Call Center?

With text analytics, your call center can spot trends, understand the percentage of users who have a particular issue, and improve products, services, and customer support based on this feedback. Just like most call center software, the ultimate goal of text analytics is to measure and improve customer satisfaction by tracking performance.

There are Three Types of Text Analytics Approaches in the Call Center:

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

Focused on forecasting future events by interpreting text with the final goal in mind. For example, 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. 

Leveraging customer feedback via text conversations is essential to knowing how and why your customers engage with your products. When you know how they feel about what you offer, you can drive important changes and mold your business processes and services to better fit your customers’ needs.

What Data Does Text Analytics Provide?

Reporting on every customer interaction is vital to running a successful contact center. It’s how you ensure your performance is up to standard. And the good news is that text analytics data can be enriched by traditional support metrics, such as the ones below, to understand how each specific area or category of user queries is behaving in terms of support. 

A few support metrics that can be enriched or augmented by using text analytics are:

1. New Tickets/Interactions

Reveals how many new tickets were created during a specific time period, which provides you an overview of how often your customers are contacting you. Depending on whether or not this data trends up or down, you can make decisions on whether you should invest in more support staff, improve your online knowledge database, or speak to your IT team about recurring issues.

2. Solved Tickets

Reveals how many customer support tickets were completed during a time period. This allows you to determine whether or not your contact center is keeping up with demand.

3. Backlog

Helps determine how many unanswered issues your contact center is dealing with, which is a significant indicator of customer satisfaction.

4. Customer Satisfaction Rating

By reviewing sentiment, text analytics can provide you with data on how happy your customers are

5. First-Time Reply

Tells you how long it took for your agent to make the first public reply—how quickly your agents are resolving issues. Speed is critical to customer satisfaction, especially for text-based interactions.

best-utilize-call-center-QA-tips

How Can Text Analytics Improve Call Center Performance?

Call centers are constantly busy. There’s always another customer to interact with and another customer support ticket to complete. What this means is that your call center agents and managers are too busy to review every text interaction accurately and effectively.

Even if you have a system in place where agents are expected to tag conversations and tickets to identify what happened, it’s not terribly reliable. Often, agents end up relying on the easiest and most common tags—question, concern, IT issue, etc. And, every agent tags customer interactions in their own way. This means you won’t have the information you need to accurately analyze these interactions. 

However, with text analytics software that uses AI to automatically review all text-based interactions, not only do you take work off the plate of your agents and managers, but you gain access to much more accurate and helpful information. Text analytics can automatically analyze all text conversations and apply a large range of tags, keywords, and categories. And you can set rules for consistent and reliable reporting.

From there, you can delve down into each text conversation in granular detail or from a high-level for rich, flexible, and helpful analysis. Best yet, text analytics in the call center can keep you from making erroneous assumptions.

For example, an overly long or negative LIVE chat conversation reviewed just on the basis of manager involvement or time spent could be automatically placed in a pile of bad interactions. However, what if the customer had a more difficult issue, or was angry due to not receiving their order, being overcharged, or missing their refund? Obviously, in these cases, the agent is not at fault for the negative customer sentiment.

With text analytics data, you can filter text conversations based on topics, issues, queries, and problems to ensure that performance and customer satisfaction matches each situation. Then, you can compare how each agent performs compared to the situation (specific topic) and not just overall.

How Can Call Center Managers Use Text Analytics?

There are many ways for call center managers to leverage text analytics. The main one being to look at individual agent performance and see what can be done to improve overall customer satisfaction to keep your customers happy.

With text analytics, managers can assess the customer’s perception of their interaction with the contact center and identify sentiment trends for each agent and as a whole. From there, call center managers can identify overarching areas for improvement, agents in need of training, agents that deserve rewards, and more.

To successfully use text analytics, call center managers should:

1. Start with the Obvious Problems

These are the areas that need your immediate help and can make the biggest difference in your call center’s success.

2. Organize the Wealth of Information Available

Examine all of the information provided by your text analytics software by keyword and volume. This is a good indicator that the issue/trend is important.

3. Allow your Customers to Write What they Want

Authentic customer feedback, in their own words, is much more valuable than written responses that are restricted to a list of options. By allowing customers to express themselves freely, you get better insight.

4. Provide Coaching Based on Precise Topics and Issues

Managers should be able to drill down into the specifics of every customer interaction in just a few clicks. From there, they can offer highly personalized coaching and training to increase customer satisfaction at every level.

What Features Does Your Call Center Text Analytics Software Need?

Your text analytics software should automate and simplify repetitive tasks. The goal should be to allow your support team to focus on resolving the most complex customer problems.

Here are a few key text analytics features to look for:

1. Customer Sentiment Analysis

The text analytics solution you use should monitor, analyze, and intelligently score every text-based interaction for customer sentiment. This means using text recognition technology that can identify keywords and text structure to identify customer opinions and behavior.

2. Real-Time Analysis

You need real-time understanding of your text conversations so you can improve the quality of your service immediately. There should be alerts for situations where politeness isn’t detected or empathy is missing.

3. Post-Interaction Analysis

After every text conversation, you should be able to perform a detailed analysis of the text for information about the top reasons for contact, product mentions, quality assessment, sentiment evolution, intent, and more.

4. Integration

Look for integrations with other major contact center software such as Zendesk, LiveChat, Salesforce, Zoho, and Scorebuddy.

5. Reports

With one click, you should be able to discover how your text interactions have gone, who your star performing agents are, what issues come up most often, contact drivers, and more. The reports should automatically summarize the key data of your text interactions with graphs, tables, and more detailed breakdowns.

What is Repeat Call Analysis?

The Long-Term ROI of Call Center Text Analytics

There are many short and long-term benefits to call center text analytics. When you can properly review and analyze every customer interaction, there’s a lot of hidden value for improving ROI.

For example, with text analytics, you can:

  • Identify Contact Center Trends: What’s most important to your customers right now? Why are your customers chatting with you? What issues are they experiencing and what are they asking for? Are there emerging issues and escalations? Text analytics provides answers to all of these questions and more, so you can make better decisions about where and how you should be focusing your efforts.
  • Provide More Specific Agent Training: When you can granularly break down text interaction by topic and issue, it becomes fairly obvious how your agents are performing. You can quickly and easily identify problems and areas for improvement, and then create training and coaching for those specific issues. This means you can focus your efforts where they will make the most difference.
  • Save Time with Better Self-Service: Self-service knowledge databases and AI bots are great time savers, if they are effective. Text analytics provides detailed breakdowns of customer support topics, categories, and themes so you can improve these systems. This means you can provide better automatic troubleshooting, saving your contact center agents unnecessary time and energy.
  • Reduce Compliance, Regulatory, and Legal Risk: By capturing and reporting on text-based interaction, there are no blind spots in your quality assurance process. You have the data you need to review every customer interaction to ensure all business processes are followed.

Overall, text analytics is essential for bringing new insight into every text-based customer interaction. It complements your existing contact center processes and provides your team with more knowledge and insight into your agents, business processes, and customer satisfaction. 

If you’d like to learn more about how Scorebuddy works alongside text analytics for a more complete quality assurance analysis of your contact center, contact us today.

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Tags: Customer Experience & Service, Call Quality Monitoring