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. It is your responsibility 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. In fact, while 80% of companies believe they are providing superior customer service, only 8% of customers agree.
The only way to truly 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.
How Does AI Text Analytics Improve Contact Center Quality?
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 to complete a real-time analysis of everything your customers are saying via text including email, chat, and SMS. You’ll be able to automatically extract insight into the customers' sentiment, emotion, 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:
- Increase contact center efficiency by automatically extracting insight on customer needs from all text interactions.
- Offer better products and services by using authentic customer feedback in your R&D.
- Improve customer satisfaction by addressing underlying issues appropriately and consistently.
- Offer better self-help tools and knowledge databases based on common customer questions and concerns.
- Intuitively manage the contact center through quick and easy identification of problems.
- Improve agent training and coaching using knowledge gaps and areas of improvement.
- Increase quality assurance accuracy by monitoring and analyzing 100% of text interactions.
- Better call center compliance through the use of automatic alerts and warnings when risks are detected.
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?
Most Valuable AI Text Analytics Features to Improve Customer Service
The role of AI text analytics in call center QA cannot be overstated for streamlining your contact center’s processes and improving QA practices. The key is knowing which features are most valuable to you as a QA manager.
100% Coverage of Text-Based Interactions
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 available to manually analyze text conversations, emails, and help tickets. And often, you’re forced to randomly select a few interactions and hope you pull out good quality insight.
With AI text analytics, you can automatically analyze 100% of all text-based interactions through natural language processing. This means that you can extract insight on topic, sentiment, and trends in the customer’s own words without additional manual effort. And you won’t miss key insight due to lack of resources or time.
Root-Cause Analysis of Agent Strengths and Weaknesses
In order 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 so you can 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:
- identify conversations that seem abnormally long to evaluate potential causes of dissatisfaction.
- explore the situational context of customer interactions.
- categorize customer problems into groups or topics by volume.
- tag tickets and conversations based on what the customer means.
Real-Time Agent Guidance
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 seems to be trending toward a lack of empathy or politeness. It can also track brand, product, feature, and issue details, and then provide guidance based on trends.
8 Ways to Improve the Customer Experience with AI Text Analytics
There’s no doubt that AI text analytics helps you go beyond the normal 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 QA, let’s talk about turning those insights into product improvements, service improvements, agent training, and more.
- Increased Contact Center Efficiency: Automatically extract topic, sentiment, and trends from the customer’s help ticket, email, chat description, and text, to route the customer to the agent best suited to solving the issue.
- Better Products and Services: Gain authentic customer feedback on your company’s products and services, so you can fix product errors, meet service needs, and reduce user experience frustration.
- Improved Customer Satisfaction: Offer quicker problem and question resolution and increase customer satisfaction by addressing underlying issues appropriately and consistently.
- Better Self-Help: Identify common topics, recurring issues, frequently asked questions, and trends to create self-help tools and knowledge databases that match your customers’ needs.
- More Intuitive Contact Center Management: Quickly and easily identify problems and areas for improvement—as well as star performers—and then adjust your management style as needed.
- Improved Agent Training and Coaching: Robustly evaluate every customer interaction to identify gaps and areas of improvement for more relevant training and coaching.
- Increased QA Accuracy: Enable 100% text quality assurance and monitoring to reduce mistakes, increase intelligence, and improve decision-making.
- Better Call Center Compliance: Set up automatic alerts and warnings when compliance risks are detected to avoid negative ramifications before they get out of hand.
The Best Contact Center QA Managers Use AI Text Analytics
The goal of AI text analytics is 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 important changes to better fit their needs. And by starting with what your customers are actually telling you and using real 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 with accuracy and clarity. Learn how AI can simplify your job as a contact center QA manager today!