How AI is Transforming the Way Customer Service Teams Work in the 2020s
AI was once a concept that belonged in the realm of science fiction. There was even a major Hollywood film with that exact two-letter title. It may have been novel then, but as we move into the 2020s, things are very different indeed.
Artificial intelligence – a term used to describe a group of technologies – is having an immense impact on everyday life. AI is reshaping processes and activities in a wide range of settings. The customer service field is one where transformation via AI could be the most profound.
According to a 2019 report, AI use for customer service is on the rise. 24% of service teams surveyed were already using some form of AI. 56% of decision-makers, meanwhile, claimed that they were actively looking for ways to use the tech.
The impact of AI on customer service is only going to grow as the new decade wears on. There’s no better time than the present, then, to get to grips with AI’s relationship with customer service. We’re going to examine exactly how the tech is transforming the way customer service teams work.
What do we mean by AI?
Before tackling AI and customer service, it’s worth clarifying what AI means. Artificial intelligence is a phrase that gets used a lot but often poorly defined. In its most common usage, AI refers to any technology that acts in a more ‘human way.’
At a basic level, the phrase applies to tech that completes tasks associated with human intelligence. Some fundamental examples include:
- learning
- recognizing patterns
- problem-solving
- planning
There are many different strands to the technology under the umbrella of AI. Two of those most pertinent to customer service are machine learning and natural language processing. Let’s look briefly at what those are before we turn to how AI is transforming customer service.
- Machine learning – In machine learning, a network or system gets fed a large amount of data. It then uses the data to ‘learn’ how to carry out a task. That may be to recognize specific patterns or to understand written text.
- Natural language processing (NLP) – NLP focuses on the interaction between humans and computers. It deals with programming machines to understand language as it is naturally used by humans. That may be in spoken or written form.
How AI is Transforming Customer Service
Now we’ve got a better foundation in AI, let’s turn to its impact on customer service. As we’ve already mentioned, customer service teams are increasingly turning to AI. Firms of all shapes and sizes see the tech as a cheaper and more effective way to boost customer experience.
Source: (https://www.salesforce.com/blog/2019/03/customer-service-trends.html)
AI can be applied to customer service in a variety of ways. We’ll cover and discuss these uses as we look at the sector more broadly. The uptake of AI processes and features will have a notable impact on customer service teams. The following are four main ways that AI is transforming how those teams work:
- self-service & automated support
- automation of customer service processes
- empowering agents to deliver more
- proactive problem-solving.
Self-Service & Automated Support
Customer service is very much a multi-channel discipline. By that, we mean that customers have a range of avenues through which to seek help. Most firms use a unified communications as a service platform to offer a choice of support channels. Customers can reach agents on the phone, via email, through social channels, and more.
AI adds a new channel for firms to provide their customers. Chatbots are pieces of software that can offer customer support with no human input. They use NLP to process and understand questions that customers ask them, and can provide relevant answers or to point customers to where they’ll find the info they need.
Live chat powered by an AI chatbot, then, has the potential to transform customer support. For many simple requests or queries, it can take service agents entirely out of the equation.
Source: (https://www.convinceandconvert.com/digital-marketing/6-critical-chatbot-statistics-for-2018/)
Still, AI does not spell the end of teams of agents. As shown above, consumers don’t view chatbots as a replacement for human service. Instead, they see them as a way to get simple answers or as a conduit to further assistance.
Automation of Customer Service Processes
The larger a company, the more ‘behind the scenes’ processes it needs for customer support. Take, for instance, a firm that gets thousands of email queries and complaints per day. To handle those emails efficiently, the firm needs a process for passing messages to agents.
Often, the process is as simple as categorizing messages by subject line. All emails with a specific word in the subject box will get routed to a particular agent or team. That is far from a perfect system. It can cause messages to bounce around a service team and not get handled as quickly as possible.
AI is starting to transform this area of customer service via automation. Algorithms using machine learning and NLP can understand the meaning of messages. They can analyze the text of an email, recognize what it’s about, and route it accordingly. They can do it all in a fraction of the time it would take a human.
What’s more, as such an algorithm processes more messages, it will get ‘smarter.’ The nature of machine learning dictates that the more data it analyzes, the better the responses. With the spread of the Internet of Things and connected devices that create more and more data, that’s a notable advantage of machine learning.
Automating these kinds of processes makes a customer service team more efficient. Your agents won’t have to re-route queries that came to the wrong place. Instead, they can spend more time responding to customer questions and issues.
Empowering Agents to Deliver More
With all the talk so far about automation, it’s important to say that AI isn’t going to replace human service agents. That’s not the kind of transformation we’re talking about. A 2019 survey found some mixed consumer attitudes to AI customer support.
The study revealed that 86% of consumers prefer exchanging messages with an agent. That’s as opposed to a chatbot. There’s no customer appetite for fully automated customer service. Instead, AI is something that can empower human support staff to deliver more to customers. It can help further boost UC platforms, which already help improve customer service.
Source: (https://www.zdnet.com/article/ai-is-revolutionizing-customer-service/)
The kinds of automation mentioned above take the pressure off a customer service team. There are other ways, too, that AI can let agents deliver improved support for customers. Take, for instance, how AI can empower agents in a contact center.
AI solutions such as internal chatbots can listen to agent-customer interactions in real-time are now getting developed. These solutions understand what’s discussed and help the agent to deliver a solution. They might, for example, automatically retrieve relevant information from the firm’s systems.
Sentiment analysis is another AI phenomenon that could redefine the contact center. It’s where AI software helps agents discern the mood or attitude of a caller. The agent will then better understand how best to proceed with their call.
For customer service teams, AI is more likely to give them useful support tools than to replace them. Whether handling IM, email, or phone inquiries, agents can use AI to augment the support they give.
Proactive Problem-Solving
People traditionally think of customer service as reactive. It makes sense, considering the time that customer service staff spend responding to queries or complaints. With the help of AI, however, firms are starting to make customer service more proactive.
AI-driven data analysis is one example. Analytics engines can pore over vast sets of data from many sources. They can analyze customer interactions with a website, marketing materials, and more. Thanks to machine learning, the engines are also able to identify any striking patterns.
That can lead you to new insights. For instance, you may find a common thread linking site visitors who leave without buying. That thread may be a specific problem with your site or a particular objection. You’re then able to rectify the issue and ensure you don’t lose any other prospects for the same reason.
Source: (https://www.ibm.com/services/ibmix/case-studies/1-800-flowers.html)
Chatbots or virtual assistants are having an impact in this area, too. Many companies use bots to help create personalized recommendations for shoppers. Those bots guide customers to the right products in the same way that a customer service agent might.
AI & Customer Service: The Start of a Revolution?
AI is one of today’s most exciting branches of tech development. Processes and tools associated with AI are revolutionizing a wide array of sectors. In the customer service field, the transformative effects of AI are already evident.
Companies of all shapes and sizes use AI in a variety of guises. From chatbots to smart analytics engines, AI tools are a big part of customer support for many firms. The uptake of such AI applications is only going to rise. More and more new uses for the tech in customer service will get developed in the coming years.
Thanks to AI, how customer service teams work will never be the same. Potential fears of AI putting agents out of work, though, are unfounded. The bots and tools that the tech spawns will not replace the human touch at the moment.
The primary way that AI transforms the work of customer service teams is more subtle. It puts agents in a better position to deliver a higher standard of service. AI can resolve mundane, simple tasks. It can automate processes to make them more efficient. It can even provide tools to make offering customers the best possible solutions far more straightforward.
Sources:
1- https://jaxenter.com/machine-learning-business-165812.html
2 – https://www.cgsinc.com/en/resources/2019-CGS-Customer-Service-Chatbots-Channels-Survey
3 – https://www.salesforce.com/blog/2019/03/customer-service-trends.html
4 – https://monkeylearn.com/sentiment-analysis/
