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Call centers are collecting more data from consumers than ever before, and, in return, people expect a better customer experience. When you fail to deliver a seamless customer journey, people are far less likely to do business with you. A recent study found that 74% of consumers are likely to buy based on positive customer experiences alone.  

That point brings us to the critical role that artificial intelligence (AI) is expected to play in call centers in 2022 and beyond. There is a driving motivation to adopt AI in call centers to improve customer service and improve overall operational efficiency. 

Using AI-enabled text analytics has become a big part of improving customer experience. AI’s ability to analyze the unstructured and structured data gathered from customer interactions across various sources makes AI text analytics such a valuable power source for QA managers. AI text analytics can capture all interactions and analyze them to gain better and more actionable customer insights, such as through email, chat, SMS, or other communication mediums. 

This real-time analysis provides essential information related to customer requirements and their experiences. It helps agents optimize their call center operation and improve the satisfaction level of their customers. 

With AI text analytics, you can perform thorough information extraction, theme classification, sentiment, emotion and intention analysis. You can undoubtedly gain a deeper understanding of your call center operation and efficiency. 

  

Monitoring 100% of Interactions 

AI also helps greatly expand the volume of customer interactions that can be analyzed. Up until recently, one of the biggest struggles of contact center quality monitoring was the low number of interactions with customers and prospects that could be monitored. QA managers have a limited amount of time to analyze all the text conversations, emails manually, and help tickets and are typically forced to select a few interactions to glean quality insight randomly.  

You can automatically analyze 100% of all text-based interactions through natural language processing with AI text analytics. This means that QA Managers can now extract insight on topics, sentiments, and trends in the customer’s own words without additional manual effort.  

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Text analytics uses AI and machine learning to go in-depth into every customer interaction and track details that will help focus agents’ efforts and enhance their training. By analyzing 100% of customer interactions,   you can: 

  • Identify conversations that seem abnormally long and 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. 

Addressing Backlog 

Keeping pace with increasing call volumes and customer demand is becoming a herculean task. As such, backlog has become an issue in many call centers. Every unresolved ticket equals an unhappy customer, and tickets can pile up quickly. That’s why your contact center needs processes and tools in place to reduce backlogs and better handle ticket volume.  

To better manage and handle the backlog, you need to categorize the content of all your customer support tickets and then understand the root cause of each problem on those tickets. Lack of time and human resources is crucial in preventing call centres from reading every key and routing them as needed. 

Once again, this is where AI text analytics can play a crucial role. This task is done automatically by reading and analyzing all the tickets in your backlog to provide vital in-depth insights and analysis. The ability to automatically dig down into the causes of your backlog and take the necessary steps to resolve tickets as quickly as possible is invaluable for successful call center operation.  

The AI Effect on Call Center QA 

Using a combination of advanced voice engines, natural language processing (NLP), and highly advanced pattern recognition, AI technology has expanded the call center quality assurance landscape.  

Typical call center QA involves analyzing questions like why a customer perceived their call center interaction a certain way and what specific aspects drove that opinion? What elements of the agent’s communication played a role in forming their opinion? 

If you have five different QA managers listen to the same call, you may get five other answers/opinions about what may have gone right or wrong. This is an inherent flaw within human communications as a combination of our preconceived ideas, experience, and culture play a subtle role in our opinion formation.   

TODAY, new AI technologies allow call centers to combine the best of human intelligence with the best of artificial intelligence to deepen a company’s knowledge of what drives customer outcomes. AI is allowing call centers to seize upon the opportunity to finally overhaul call center QA to deliver what it was initially intended to provide: higher quality customer interactions. 

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More Personalized Future 

A wide range of scenarios, drivers, and call center interaction outcomes are being identified using AI. Now companies and organizations can bolster their understanding of everything from marketing effectiveness to the impact of call center service quality and the real drivers of customer perceptions. 

With call volumes and customer queries doubling day by day, AI’s role in assisting and navigating customer service agents towards higher percentages of customer resolutions is gaining significant traction. The continued convergence of AI and call centers can transform the customer service game, allowing for hyper-personalization of customer experiences based on the technology’s ability for much deeper and more accurate data analysis. 

Supporting Agents, Not Replacing 

While the power of AI will have a profound impact on call centers well into the future, the human element will always be a vital part of the customer service experience. The question of whether AI will replace call center agents is premature. AI agents will create more of a hybrid model for call centers as the tech gains greater acceptance in the space. While some customer inquiries will become automated with the rise of AI-powered call center services, the most complex problems will still need to be solved by live agents.  

There will always be complicated issues that AI won’t be able to handle as effectively as a live agent. Using AI in call centers aims to improve the customer experience and relieve human agents of time and energy spent on simple requests. AI can help customer support reps be more productive and have more engaging and personally satisfying conversations. 

It is all about using AI text analytics to gather more actionable customer data, enabling you to make better decisions to improve call center operations. Today, learn how AI can simplify your job as a contact center QA manager! 

If you want to get to the customer interactions, be sure to download our eBook: Unlock the Power of Text Analytics.  

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