Think of the situations when you contacted customer support for whatever reason. Did you have a good experience and quick response? Are you sure you talked to a human agent?

The exciting part of the usage of artificial intelligence is that it can be implemented in different stages of the customer experience journey. Setting the bots aside, AI-based tools help to proceed a customer request quicker, improve the accuracy of response, and help to establish a personalized approach. And in most cases, you, as a customer, don’t even notice this!

Let’s see how effective the customer support becomes and how its transformation increases the overall customer satisfaction.

Generative AI in Action: Automating Responses to Customers

AI tools in customer support take more elaborated forms and functions. Setting up a bot in a CRM system is not the only option anymore. Various tools based on AI algorithms help customer support agents to prepopulate ready responses, quickly analyze the issues, and provide statistics on customer satisfaction. And the technology has become so elaborate that it helps to build a seamless experience with customers.

While businesses step on the path of digital transformation, the customers sometimes don’t even notice the change. The AI for customer support transforms the whole landscape of providing the services. As a result, it brings multiple advantages for both sides, the customer and the business.

Taking a virtual assistant as an example, let’s see how the AI integration puts the customer service to the next level.

Case in Study: Virtual Assistant

Chatbot is not the only form a virtual assistant takes. Indeed, you can build a bot and integrate it, say, in the chat channel to lead general inquiry conversations with customers in place of the agents. But virtual assistants can also be integrated in CRM systems which agents operate on a daily basis. For example, with an assistant in the email channel, an agent receives a generated response ready to be validated and sent to the customer. It saves tons of time and provides a personalized experience to the customer.

Which algorithms come into play?

Intent Recognition

With natural language processing (NLP), the AI model understands the customer’s requests. The algorithms split the request into smaller pieces, or tokens, to understand the meaning of the words, grammar, context. The intent recognition allows a model to understand whether the customer asks a question, writes feedback, or has some inquiry. Based on the analysis, it generates the response.

Response Generation

The response is not only based on a customer’s inquiry, it considers multiple data fed to the model during training. The models are usually taught on a company’s complex data and on historical data of interactions with the customer. Say, it’s not enough for the model to understand a customer’s message “I didn’t receive my order”. It should proceed with the sentiment analysis and dig into previous interactions with the customer, including order placement. The training step is critical for a model to generate an appropriate response.

CRM Integration

The model integrates into a customer support space as soon as it is built. For example, integrated into Zendesk through APIs, the virtual assistant can analyze the historic data and generate the final response. The agent will only need to validate whether this response is accurate, and then send it to the customer. If customer support uses multiple tools to answer customer questions, it’s important to integrate the bot with all of them. For example, reading the expected delivery date in the tool of the transporter will allow the bot to generate a clear and accurate response to the customer.

Continuous Learning

There are two approaches to constantly train the AI model: through supervised learning or through reinforcement learning. The agents should validate the AI response, which at the same time teaches the model for further similar interactions. The AI models are thus refined through a trial and error process. The more trained the model, the more accurate response it will generate. As a result, the more seamless the interaction with the customer will be.

Reaping the Fruits

Well-trained models give so specific responses that they look like they were written by humans. We can already observe this in our daily interactions. According to the National University, only a third of consumers understand they interact with AI, while in reality 77% of them do.

Most AI usages in customer support include responding to customer’s messages (45%, according to Forbes). The benefits are the following:

• Reduction of time of response
• Reduction of manual intervention
• Personalized answers
• Sentiment analysis
• Improved customer experience
• Increased customer satisfaction

As additional benefits, companies implementing AI in customer service can provide 24/7 operations and reduce costs on manual processing tasks by the agents.

Empowering Customers, Enabling Agents

As the new AI solutions continue to expand, customer support transforms its concepts. The activities built around the customer not only allow to improve the service, but also to optimize the working process of customer service agents. With machine learning algorithms and sentiment analysis, the AI tools in customer support revolutionize the interaction businesses have with their cu