How to Improve Customer Service with Artificial Intelligence

Customers are demanding an increasingly personalized and quality experience. Which is not always easy for a company. Fortunately, technology is also evolving and the different forms of Artificial Intelligence can help, today, to achieve this goal.

Chatbots are, for sure, the option that comes most immediately to mind. But other technologies, such as machine learning (statistical learning), also have a role to play.

Admittedly, not all forms of AI are suitable for all organizations. Still, AI will be essential to the future of customer service.

Here are the top real-world AI use cases to explore for your customer service.

1 – Chatbots

Chatbots, or virtual assistants, are the most popular use of AI in customer service. Making a chatbot makes it possible to better manage certain classic questions, such as the delivery date, the balance of a payment due, the status of an order or any other element from internal systems.

Using a chatbot to manage frequently asked questions (FAQs) also allows the customer service team to help more people.

2 – Agent Assistant (Augmented Agents)

In many modern contact centers, an AI-powered tool automatically interprets the customer’s request, searches for articles or content related to the request, and offers this knowledge on the agent’s screen during the call. .

We then speak of an “augmented” artificial intelligence agent.

This process saves time for both the agent and the customer, and can reduce call time and therefore cost.

3 – Self-service

Customer self-service refers to the ability, on the customer side, to identify and find help, without involving an agent. When given the opportunity, most customers prefer to solve problems themselves, given the right tools and information.

As AI improves, self-service functions are increasingly present and give the customer the possibility to solve their problems at their own pace, and when they want (for example outside normal office hours). ). Self-service is fast becoming a standard in the customer experience.

4 – Robotic Process Automation (RPA)

Robotic Process Automation, or RPA, automates simple tasks that were performed manually by an agent.

Updating files, going back and forth between the CRM and the invoicing tool, tracking inventory or deliveries are examples where RPA speeds up administrative tasks and improves the efficiency of processing the customer’s request.

To find out if RPA can improve customer service, the best way is to ask agents. They will most likely be able to identify the processes that take them the most time or that require the most clicks between systems, or simply suggest simple and repetitive transactions that do not require human intervention. Properly deployed, this type of business process improvement can save customer service millions of dollars each year.

5 – Machine learning

Machine learning can help agents with predictive analytics, to identify the most common questions and answers, and even spot things they might have missed in the communication.

Statistical learning, or machine learning, is essential for processing and analyzing large streams of data, and extracting the actionable information they contain (“insights”).

For customer service, machine learning helps identify common questions and answers, using predictive analytics. This technology can even detect an element that an agent might have missed in the communication.

Additionally, machine learning allows chatbots to adapt to a situation based on past findings and ultimately better help customers with self-service problem solving.

6 – Understanding natural language

Automatic natural language processing (NLP) makes it possible to analyze the “voice of the customer” on different channels (telephone, email, chat, SMS). The semantic technologies branch of AI analyzes the meaning of the text of these interactions in search of certain trends, certain themes.

An agent can then better respond to the customer’s needs. Previously, analyzing all interactions was a time-consuming process that often involved multiple teams and multiple resources.

7 – Sentiment analysis and advanced analytics

Sentiment analysis to identify what a customer is feeling (dissatisfaction, anger, disappointment, etc.) has become commonplace among customer service teams. Some tools can even determine when the customer is upset and notify a supervisor or manager to step in and defuse the situation.

8 – Agent training

As part of agent training, AI can simulate the profile of multiple customers to test dozens of scenarios and put those responsible for answering customers in practical situations. Managers and trainers ensure that agents are ready to respond and help all types of customers who may call on them.

And tomorrow, smart devices

Smart assistants, such as Alexa, Google’s or Siri, offer new perspectives for the customer experience.

Users prefer to communicate with an organization through their preferred platform. However, these “smart home” devices (Echo, Google Home, etc.) are increasingly popular devices. We can therefore imagine – and we must anticipate it – that the customer will want to solve a problem related to a product or a service with a simple question that he will ask his smart speaker, without going through a telephone contact and without sending a email email.

Simplified communications of this type could well make the difference, tomorrow, between a satisfied and loyal customer, and a disappointed customer who will go see your competitors.

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How to Improve Customer Service with Artificial Intelligence


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