Best of ICMI in 2021 - #5: How AI Can Enhance Customer Care

Best of ICMI in 2021 - #5 

If you are unsure about the impact of AI in the contact center landscape, just take into consideration that by as early as next year, AI-enabled conversational agents are expected to handle nearly 20 percent of all customer service requests. The market for AI-enabled contact center technology is expected to reach $2.8 billion by 2024. Oracle estimates that nearly 80 percent of businesses have already implemented or are in the process of implementing AI technology in their contact center operations in the immediate future.

There have been bumps in the road. Interactive Voice Reactive (IVR) strategies for contact centers to meet customer queries and requests to this point have largely been ineffective and, at times, a downright frustrating experience for consumers. Whether it’s a mismatch between the exact query and the pre-set options available or dealing with human agents who simply did not have the right information available to help out customers – the list of inefficiencies of traditional contact center operations is long. In the business imperative to lower costs, what was left out of the contact center operations was the need to emphasize customer experience. That may have been acceptable when the technology was new, but shoddy customer experience is a luxury no business can now afford.

Fortunately, this is precisely the area AI excels in. With vast stores of information arranged logically and intuitively through clever algorithms and a combination of Natural Language Processing (NLP) and machine learning (ML), AI-enabled contact centers are fast learning the nuances of handling tricky human experiences and getting highly adept at first call resolutions.

How AI Can Enhance Customer Care

There are really two distinct types of AI currently in use in contact centers. The first kind focuses on processing large amounts of data and coming up with actionable insights before or during customer interactions to empower agents. Essentially, this focuses on providing the right information at the right time to contact center agents.

The second type is more popularly known as conversational AI, and focuses on analyzing the content of conversations in customer interactions. Through emotion and behaviour analysis, the technology can come up with real-time sentiment analysis. Instead of an irate customer escalating an issue, the system can step in beforehand and alert the team responsible to intervene earlier, thereby automating the process.

AI is effective because it can parse through huge volumes of data and behavioural inputs to come with actionable insights in real-time. These data-informed insights can enrich customer interactions and lead to less stress for both agents and callers. The systems are now intuitive enough even to follow conversations and offer response options to agents while they talk to customers – without the customer being aware. This allows for highly data-driven (informed) exchanges with the human agent free to focus on his/ her empathy and customer handling skills without having to stumble around trying to find pertinent information on the customer during the call.

Agents are empowered with actionable insights and become effective brand ambassadors for the company. By focusing on their jobs as experts at human interaction and empathy (and not just hurtling around massive databases to find relevant customer information), the agent’s job becomes of much higher strategic importance to the business.

The Road Ahead

Going beyond frictionless customer service and virtual agents, AI for contact centers holds the transformational capacity for businesses to really understand their customers in a way they never could before. While AI comes with highly actionable business insights specific to contexts, we need to further develop data science/machine learning skills that can predict customer interactions with greater levels of accuracy. Exceptional customer service means being able to serve a need before that need arises. These data insights need to be further crafted into compelling user experiences for a variety of interfaces in different computing environments that can be delivered seamlessly irrespective of where the customer is physically located.