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 by gathering insight during every text-based interaction, no matter the channel. It helps your company go beyond great products and good service to provide your customers with what they need and want most--- excellent customer service. Topics you want insight into are almost always covered in customer interactions with your call center.
The key is collecting the correct text analytics data for the 57% of customers who would instead contact your company via text-based messages versus 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, live chat, 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.
Today, more than 41% of customers expect live chat on your website. And if a customer contacts you on their mobile, 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 into 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 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 break down important information. It can segment and see 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:
The truth 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:
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. Like most call center software, the ultimate goal of text analytics is to measure and improve customer satisfaction by tracking performance.
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.
We are 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.
Leverages predictive analytics to create contingency plans for specific future outcomes.
Leveraging customer feedback via text conversations is essential to know how and why your customers engage with your products. When you know how they feel about what you offer, you can drive essential changes and mold your business processes and services to better fit your customers’ needs.
Reporting on every customer interaction is vital to running a thriving 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 behaves in terms of support.
Reveals how many new tickets were created during a specific 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 decide whether you should invest in more support staff, improve your online knowledge database, or speak to your IT team about recurring issues.
Reveals how many customer support tickets were completed during a period. This allows you to determine whether or not your contact center is keeping up with demand.
It helps determine how many unanswered issues your contact center is dealing with, which is a significant indicator of customer satisfaction.
By reviewing sentiment, text analytics can provide you with data on how happy your customers are
It tells you how long it took for your agent to make the first public reply and how quickly your agents resolve issues. Speed is critical to customer satisfaction, especially for text-based interactions.
Call centers are constantly busy. There’s always another customer to interact with and another customer support ticket to complete. This means that your call center agents and managers are too active to review every text interaction accurately and effectively.
Even if you have a system where agents are expected to tag conversations and tickets to identify what happened, it’s unreliable. Often, agents rely on the easiest and most common tags—question, concern, IT issue, etc. And, every agent tags customer interactions in their way. This means you won’t have the information to analyze these interactions accurately.
However, with text analytics software that uses AI to review all text-based interactions automatically, you take work off the plate of your agents and managers. Still, you gain access to much more accurate and helpful information. Text analytics can automatically analyze all text conversations and app various tags, keywords, and categories. And you can set rules for consistent and reliable reporting.
You can delve 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 based on manager involvement or time spent could be automatically placed in a pile of harmful interactions. However, what if the customer had a more complex issue or was angry due to not receiving their order, overcharging, or missing their refund? The agent is not at fault for the negative customer sentiment in these cases.
With text analytics data, you can filter text conversations based on topics, issues, queries, and problems to ensure performance and customer satisfaction match each situation. Then, you can compare how each agent performs compared to the case (specific topic) and not just overall.
There are many ways for call center managers to leverage text analytics. The main one is 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 who need training, agents who deserve rewards, and more.
These areas need your immediate help and can make the most significant difference in your call center’s success.
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 essential.
In their own words, Genuine customer feedback is much more valuable than written responses that are restricted to a list of options. By allowing customers to express themselves freely, you get a better insight.
Managers should be able to drill down into the specifics of every customer interaction in just a few clicks. They can offer highly personalized coaching and training to increase customer satisfaction at every level.
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.
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 to identify keywords and text structure to identify customer opinions and behavior.
It would help if you had a real-time understanding of your text conversations to improve the quality of your service immediately. There should be alerts for situations where politeness isn’t detected, or empathy is missing.
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.
Look for integrations with other major contact center software such as Zendesk, LiveChat, Salesforce, Zoho, and Scorebuddy.
There are many short and long-term benefits to call center text analytics. When you can adequately review and analyze every customer interaction, there’s much-hidden value for improving ROI.
For example, with text analytics, you can:
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.
Contact us today if you’d like to learn more about how Scorebuddy works alongside text analytics for your contact center's complete quality assurance analysis.