How AI assistants close the gaps in customer service

AI assistants are capable of quite a bit more than their living room responsibilities, although we very much appreciate that they can order us pizza and play Spotify. In the workplace, they’re helping to fill the gaps that commonly plague customer service: miscommunications, uninformed support agents, and meeting customers’ expectations. As much as machine learning has been touted for providing better customer experiences, it can help drive better customer engagement as well.

Lots of companies have already emboldened their customer service with the likes of chatbots and virtual assistants, and as these integrations become more refined, we’re getting a better sense of how they’re helping.

Closing the communication gap: helping agents understand how customers convey their issues

Finding a solution first means getting the right context, but in many cases, customers aren’t sure which details they should share about their problem. This puts the responsibility on support agents to understand how customers communicate their issues; they have to ask the right questions, notice patterns and trends, and get a feel for their customers’ reactions. This can be daunting even with a small customer base, since agents are often too focused on closing tickets instead of learning the nuances of their customer conversations.

To address this communication gap and make sure no detail falls through the cracks, computers can utilize natural language processing (NLP) to convert natural language into machine language in order to find its meaning. In customer service, the value of this can be for an AI assistant that analyzes support tickets and learns how customers convey their issues. By providing insights to the common words and phrases that customers use, agents can learn more about their customers and create support content that’s better tailored to how customers perceive their problems.

Filling knowledge gaps: making support agents more knowledgeable and enabled to share their knowhow

Support agents have the substantial task of providing expertise for their ever-changing products, leaving them with little time to catch up on the knowledge provided to them internally. In a fast-moving company, it can especially be tricky to know what the latest product updates are, where or who they need to route specific tickets to, or what kind of self-service options they offer to their customers.

These gaps in knowledge can benefit from an automated virtual customer assistant (VCA) that’s able to deliver the right information to agents at the right time. As they work within a support ticket, they can receive automatic article suggestions on the topic of the support ticket, giving them all the appropriate details to share on the customer’s inquiry. If a support organization utilizes an internal help guide, the VCA can also be embedded within the help guide to assist agents in quickly finding internal documentation as well. These VCAs don’t just automate the search for information – they learn how to identify the proper context for the information as well, ensuring that what is given to agents is accurate.
 

The customer gap: ensuring agents are well-equipped for meeting their customers expectations

You may not have heard of the customer gap before – it’s the difference between customer expectations and customer perceptions. When customers interact with customer service, they bring along expectations of what they want out of the interaction: an attentive support agent, a clearly communicated solution, maybe a friendly demeanor as well. Sometimes it’s what the customer gets, and sometimes it isn’t. How the customer ultimately perceives the interaction, and if it matches their expectation, can be measured with a customer satisfaction (CSAT) score. Ultimately, closing the customer gap is a matter of providing a positive customer experience.

Artificial intelligence can work within the more nuanced spaces of support interactions to learn when customers feel like their expectations are being met. Specific details like first-reply time, how long a support ticket is open, the amount of re-opens and reassignments, and the words used by the customer can all be measured as indicators that contribute to customer satisfaction. These details are utilized by AI-powered machine learning that can predict if a customer will be left satisfied, and can do so before their issue is resolved. It can be tough for humans to readily catch the signals of CSAT being at risk, but it’s far easier with machines that focus solely on them.