5 Ways to Leverage Artificial Intelligence in Call Centers

Research from the Massachusetts Insitute of Technology (MIT) recently showed that implementing custom trained language models like ChatGPT helped low performing contact center agents to perform at an average level. In the near term, AI will drive call center wages lower and reduce the number of employees needed. Long term, call centers will be reserved for luxury brands and high value customers. Both of these features are valuable tools for any contact center looking to boost agent performance and improve customer communications. Moreover, real-time translation can help businesses to provide a more personalized and empathetic experience to customers.

Lack of time and human resources is crucial in preventing call centres from reading every key and routing them as needed. Generative AI is a powerful tool, as it can be taught to understand customer needs and desires by analyzing existing ticket data and other client communications. It also can effectively direct complex queries to the right departments, making automated customer service more efficient. Generative AI works by learning patterns in existing datasets and then using them to create new ones.

AI-Powered Agents

We have presented five ways in which artificial intelligence can be, and is being, leveraged in call centers to improve performance and customer experience. While AI cannot entirely replace the human-human interaction, it can be leveraged to surface the right information to agents at the right time and provide them with better intelligence to enable them to work smarter. The role of AI in call centers derives from two of its major capabilities, including machine learning and natural language processing (NLP). Machine learning uses the combination of powerful processing and large amounts of data to predict human preferences. One common example of machine learning is Facebook messenger’s ability to recommend spammy incoming messages for routing to a junk folder. Meanwhile, NLP processes and interprets spoken and written messages, a capability used by AI systems such as Alexa, Cortana and Siri.

For instance, call center AI can interact with customers and use natural language processing (NLP) to understand the sentiment and context of each call. In addition, it can learn and problem solve after being trained by technology, such as human-in-the-loop, which gives a call center’s best agents the ability to share their knowledge with the AI engine. Contact Center AI solutions often offer Voice Analytics features to transcribe and analyze calls for meaningful insights that will improve contact center processes.

AI Call Center Software: The Future of Lead Generation and Sales

This technology goes beyond data analysis and rote cognitive labor by generating brand-new information all on its own. One of the biggest trends in the AI call center landscape is the focus on more intelligent routing strategies. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. Finally, call tracking software data is used to match the inbound call to its database to determine the personality and communication style of the customer along with their call history. By using multiple criteria, the PBR software is able to create as detailed a picture as possible of the caller. Stay updated with the latest news, expert advice and in-depth analysis on customer-first marketing, commerce and digital experience design.

This information offers valuable insights into customer behavior, preferences, and trends. It can also be a valuable resource when it comes to optimizing contact center operations. Contact center AI refers to the application of artificial intelligence technologies, such as machine learning and generative AI, within a call center.

They’d feel like they lack time to learn using the AI, but you can easily ease the resistance by supporting them throughout their training. These analytics are meaningful and reliable because they consider customer tone, personality, and sentiment before producing results. The company’s CTO, Colin Christie, acknowledged to Rest of World that using AI tools could mean displacing Navix Health’s AI platform, for instance, does the work that medical receptionists might normally do.

For more complex call center scenarios or high value customers who are dissatisfied, humans should always be available to give them the personal touch they require. It’s also a good idea to ensure that human employees are continually vetting and updating pretrained AI responses to fit the times, buyer and/or user trends, and other changing customer expectations. Contact centers have traditionally required reps to manually handle repetitive tasks, like accepting customer calls and messages, recording and reviewing transcripts, and following up with customers at regular intervals. The modern contact center rarely sticks to traditional phone calls, often giving users the option to communicate with their reps via email, chatbot threads, and social media messages. When contact centers opt to use generative AI-driven chatbots and analytics tools, they can more easily embed intelligent assistance into all of the channels where customers choose to interact with them.

This can be time-consuming and costly, and if not done properly, it could lead to disruptions in service and customer dissatisfaction. AI in call centres, leading to a more efficient and effective customer service experience. After implementing the service, Convert It Marketing obtained and tracked more leads, improving customer engagement and leading to a 122% increase in captured calls and conversions. Additionally, this technique results in more accurate sentiment analysis and speech recognition, as well as more accurate answer suggestions. Because of this, the software is able to change and adapt over time, ensuring that it keeps up with the shifting demands and preferences of its users. It is a method of analyzing recorded conversations or phone calls with clients to acquire adequate information, their intent, and so on.

• Generative AI is a type of artificial intelligence that is focused on generating new content.
• They achieve this by equipping your agents with real-time assistance, suggestions, and guidance during customer interactions.
• Meanwhile, NLP is a branch of AI that helps machines understand text and speech similar to how a person would.
• While the main objective of most call centers has constantly been improving customer satisfaction, with time, BPOs have also come to realize that it’s equally important to take care of the agents as well.
• Artificial intelligence is making inroads into the call center arena by its ability to help resolve customer questions and problems quickly and seamlessly, which is one of the best ways to delight customers.
• No matter how intuitive an artificial intelligence tool might seem, agents still require training to start using AI tools efficiently.

Read more about How To Use AI For Call Centers here.