How IA Assistants Close The Gaps In Customer Service

In the call centers, AI assistants (Artificial Intelligence) are helping to fill in the gaps that commonly affect customer service: communication failures, uninformed support agents and satisfaction of user expectations.

Just as machine learning has been promoted to provide better user experiences, it can also help drive a better user commitment.

Many companies have already implemented chat bots and virtual assistants in their customer service with users, and as these integrations become more accurate, you get more information on how they are helping.

How IA Assistants Close The Gaps In Customer Service

1) Help Agents Understand How Users Convey Their Problems

Finding a solution first means getting the right context, but in many cases, users are not sure what details they should share about their problem. This makes agents have a responsibility to understand how users communicate their problems, ask the right questions, and have an idea of the reactions of their users.

To address this communication gap, computers can use natural language processing (NLP) to turn it into automatic language and find its meaning. In customer service, the value of this can be how an AI assistant analyzes support tickets and learns how users convey their problems. By providing information about common words and phrases that users use, agents can get more information about them and create content that is better suited to the way users perceive their problems.

2) Make Agents More Knowledgeable And Able To Share Them

Agents have the important task of providing a satisfactory experience to users. In a fast moving company, it can be especially difficult to know what the latest product updates are, where or to whom they need to route specific tickets or what kind of self-service options they offer their users.

These gaps in knowledge can benefit from an automated virtual assistant for users that delivers the correct information to the agents at the right time. While working within a support ticket, they can receive automatic suggestions, giving them all the appropriate details to share in the user’s query. Automated virtual assistants not only automate the search for information, but also learn how to identify the right context, ensuring that what is delivered to the agents is correct.

3) Ensure That Agents Are Well Informed To Meet The Expectations Of Users

What is the user’s gap? It is the difference between your expectations and your perceptions. When users interact with customer service, they have expectations of what they want from that interaction: an attentive support agent and a clear solution. The way in which the user ultimately perceives the interaction, when it coincides with their expectations, can be measured with a user satisfaction score. Ultimately, it is about providing a positive customer experience.

Artificial intelligence can work within support interactions to learn when users feel their expectations are being met. Specific details, such as the time of the first response, the duration of a support ticket, the number of reopenings and reassignments, and the words used by the user can be measured as indicators that contribute to customer satisfaction.

AI-powered machine learning that can predict whether a user will be satisfied before their problem is resolved uses these details. It can be difficult for humans to easily detect signs of user satisfaction that are at risk, but it is much easier with machines that focus exclusively on them.

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