Introduction

Artificial intelligence for customer support (AI) and machine learning for customer support (ML) are being discussed in almost every business, and for a good reason. You can find countless articles penned about its advantages and perils, acceptance predictions, and how it’s been steadily creeping into our daily lives for years if you perform a basic Google search. Many people feared AI and machine learning (the robots would take over!), but they provide workable solutions that have helped brands globally better a range of operations.

You can select between an iPhone and a Galaxy phone, Alexa and Chromecast, and a Mac or a PC in today’s environment. Customer service can mean the difference between a firm losing and keeping a key customer.

Making your consumers wait in line for 20 minutes is not helpful to the company. It encourages them to go to a competitor. More than ever, delivering consumer demands through speedy, efficient, and seamless customer support is the only way of winning in a world where customers can move to a rival in a single glance. AI for customer service is the answer.

Customer support has proven to be among the best AI use cases within the enormous canopy generated by this digitalization. While more than half of service businesses are actively seeking methods to incorporate artificial intelligence into their activities, many are unsure how to do so.

Sixty-five percent of consumers are concerned that brands are overusing AI, which can make things more complicated if not done properly. So it’s critical to have a complete understanding of how to use AI and when it’s acceptable.

Knowing the Difference Between AI and Machine Learning

Artificial intelligence (AI) is an extensive term that reinforces the notion that machines can mimic human issue-solving or thinking skills. Machine learning (ML), a subset of AI, uses training data to enhance algorithmic processes, predictions, and judgments.

How Are AI and ML disrupting The Customer Service Industry?

AI is disrupting the customer service industry, making it more cost-effective, increasing customer engagement, and speeding up the process for agents. Agents are repeatedly asked the same basic inquiries that a machine can readily answer. Artificial intelligence, which combines machine learning algorithms and language processing, is capable of automating these fundamental requests and assisting agents in real-time, directing calls to the most appropriate agents, and assessing customer service personnel.

The advantage of using customer service as an AI use case is that there is an abundance of training data to develop machine learning models. Even if a user’s questions aren’t transparent, a decision tree chatbot or other dialect-based customer service AI solution must be able to understand what they mean.

It can learn to do so by examining vast amounts of available data, such as Yahoo! linguistic data, Twitter assistance data, and other accessible data sets. Businesses can also collect data tailored to the types of inquiries their chatbots answer.

Use cases

Enables Self-service

Allowing consumers to help themselves is the ideal method to assist them. They get the solution they need directly, and the organization saves money on customer support. Both the customer and the organization benefit from self-service. Searchable knowledge base management system and virtual agents are the two principal solutions for enabling self-service.

Searchable data sources are similar to company-specific Google. Many websites feature “help” pages, but only a negligible percentage of them truly assist. The reason for this is that search results are unreliable and frequently unusable.

Pre-Interaction Task Automation

Customer service interactions that are successful begin far before the agent-to-customer engagement. It entails categorizing and analyzing the customer’s problem. Then, matching the consumer to the appropriate agent, utilizing massive amounts of data.

Automated issue detection is the first digitization area. It is a manual process that is currently being mechanized with artificial intelligence. The practice of automatically identifying the sort of issue a customer is experiencing is known as automated issue detection.

The information distribution to agents is the second area of automation. The arduous process of accepting income customer service tickets and sending them to the superior agent for that need is known as ticket routing.

This process takes a long time, is inefficient, and causes delays in reaction times. Machine learning algorithms can categorize tickets into multiple categories, in this example, tags, automatically. Another advantage of sentiment analysis is that it may identify angry or disgruntled consumers and prioritize addressing their issues.

Increasing Agent Productivity in Real-Time

So far, AI has assisted customers in assisting themselves and has prepared agents to be more effective due to less agent burnout before even interacting with clients. Making agents more effective in real-time and delivering insights and recommendations to agents throughout the call is the third significant area.

As you might expect, this is tough to accomplish because it must be done in real-time, with information collected from the call, connected to current data, and predictions made to boost agent efficiency.

Conclusion

With the shift to a digital-first approach, brands are being compelled to upgrade at a breakneck speed. Artificial intelligence helps brands streamline procedures in the face of increased client demands and an onslaught of queries.

Even while speaking with human agents, you can use tools like AI knowledge management tools to enhance the customer support experience by instantly retrieving information and providing solutions, allowing the agent to respond to the customer much more quickly.

AI allows customer service professionals to spend more time on the most complicated issues that human agents can resolve, rather than repeating the same easy replies to the same basic inquiries that a chatbot could perform. The detailed application of AI and ML in customer service will most likely take a decade or more to realize. But, its strength and promise are apparent, and its trajectory is unavoidable.

Source: Plato Data Intelligence: Platodata.ai