As a contact center quality assurance manager, you’re constantly working to improve efficiency, increase productivity, facilitate growth, increase customer satisfaction, and build brand loyalty. Your responsibility is to ensure that everything within your domain exceeds expectations with a strategic call center quality assurance framework. It is a herculean set of goals that require constant monitoring, evaluation, and improvement.
Many companies fail to meet the requirements. While 80% of companies believe they provide superior customer service, only 8% of customers agree.
The only way to indeed be successful is to measure—everything! That’s why there’s AI text analytics. If knowledge is power, AI text analytics is one of the most valuable power sources in any QA manager’s arsenal.
Did you know that 80% of today’s enterprise data is unstructured? That means there is a massive amount of text data that you need to manage, analyze, and extract meaning from for call center quality control.
So, if your goal is to improve the customer experience, there are few better ways to do this than by applying AI text analytics. Text analytics allows your operations toanalyse everything your customers are saying via text, including email, chat, and SMS. You’ll be able to automatically extract insight into the customers' sentiments, emotions, problems, trends, language, and behavior. And this will lead you to essential information about every customer interaction so you can make more informed decisions and take appropriate actions.
Using AI text analytics enables you to:
As a contact center QA manager, utilizing AI text analytics means that you’ll be able to improve contact center quality with every agent interaction. But how exactly does it help quality control?
The role of AI text analytics in call center QA cannot be overstated for streamlining your contact center’s processes and improving QA practices. Knowing which features are most valuable to you as a QA manager is the key.
One of the biggest struggles of contact center quality monitoring is low coverage of the volume of interactions with customers and prospects. As a QA manager, you only have so much time to analyze text conversations, emails, and help tickets manually. And often, you’re forced to select a few interactions randomly and hope you pull out good quality insight.
You can automatically analyze 100% of all text-based interactions through natural language processing with AI text analytics. This means that you can extract insight on topics, sentiments, and trends in the customer’s own words without additional manual effort. And you won’t miss critical understanding due to a lack of resources or time.
To focus your agents’ attention on what matters most to your business, you need to know the root cause of why your customers contact you. This means you need high-quality data about every customer interaction to understand what’s happening.
Text analytics uses AI and machine learning to go in-depth into every customer interaction and track details that will help you focus your agents’ efforts and enhance their training. You’ll be able to:
As a QA manager, it’s impossible to monitor every chat. And when you can, agent guidance is always provided after the fact. This leaves significant insight on the table and limits your coaching and training ability.
But with AI text analytics, you can provide real-time guidance based on language patterns and intent. The tool can set off detection alerts and warnings when a text-based conversation tends to lack empathy or politeness. It can also track brand, product, feature, and issue details and provide guidance based on trends.
There’s no doubt that AI text analytics helps you go beyond the non-standard QA form (which still provides a ton of value) and gain feedback in your customers’ own words. So, now that you know what features of AI text analytics matter for Q let’s talk about turning those insights into product improvements, service improvements, agent training, and more.
AI text analytics aims to help you understand your customers better than ever before. When you know how they feel about your customer service, products, agents, and the entire experience, you can drive import significant to fit their needs better. And by starting with what your customers are telling you and using honest conversations to build out your contact center QA, you can better commit to a customer-centric support strategy.
It’s all about using AI text analytics to make better decisions accurately and clearly. LeaToday, learn how AI can simplify your job as a contact center QA manager today!