Start experimenting with AI to improve your customer service today

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Forward-thinking companies are already experimenting with artificial intelligence (AI) to understand how they can integrate this emerging technology into their products and processes to improve  their service to customers. 

AI and machine learning technologies are helping companies to get customers answers faster, automate routine tasks, identify at-risk customers, keep content up-to-date and free up service agent’s time, so they can focus more of their energy on forging stronger relationships with customers. 

So how do you get started? 

Get your data house in order

Customers expect a seamless journey across a company’s channels—one in which self-service options are available and they never have to leave the mobile app or website. 

Before you can start reaping the benefits of AI, put your data house in order and review your overall channel strategy. 

How can you connect channels, so customer data isn’t lost when customers switch channels? Make sure there’s one record of the customer and no divisions when it comes to issues that cut across channels.

Understand where AI is helpful and where a human touch is needed

Chatbots are one of the most popular ways for companies to begin experimenting with AI and automate customer service interactions. What exactly are bots? They include both scripted and self-learning AI tools programmed to interact with customers in a way that enables an automated conversation. According to Van Baker, research vice president at Gartner, “By 2020, over 50% of medium to large enterprises will have deployed product chatbots.”

However, there’s a time and place to offer support to your customers with a bot versus a human. A common misconception is that bots are intended to solve 100% of problems and completely replace human agents. This is not the case. At this time, bots are most useful for keeping customer support accessible after business hours and for solving the repetitive interactions that humans don’t need to spend time on—which frees up human agents to handle tricky requests and high priority enquiries, where they’re needed most.

Low value, repetitive questions are typically solved with a scripted reply or email macro, and the solution is the same for every customer. According to Harvard Business Review: “84% of customers would prefer a straightforward solution to their problem” anyway.

Incorporating bots into real-time communication like live chat can help businesses to improve first response time and agent efficiency. Customers don’t have to wait in a long queue to get a simple answer on how to initiate a return, and likewise, an agent doesn’t have to spend time explaining how to start a return 20 times a day.

(Image: © Pixabay)

Experiment with self service and scale

Investing in self-service allows businesses to harness the power of machine learning to improve their customer experience. Forrester found that 76% of customers prefer self-service to alternatives like email or phone support and more than half of customers will abandon their online purchases if they can’t find fast and easy answers to their questions.

Companies are therefore increasingly rerouting tickets through self-service, with machine and deep learning tools like Answer Bot. Deep learning is a sophisticated form of AI inspired by the human brain that recognises speech, data, and specific patterns. Like humans, it uses that information to make connections and decide on the best action to take. But unlike humans, it can process trillions of pieces of information to see patterns we might miss.

When a customer sends an email to a business that has one of these tools, an algorithm is used to understand the question in the request, then suggest the most relevant content in a reply to the customer – almost instantly. This greatly reduces the time to a customer resolving their issue compared to a human agent. The customer reviews the articles and if an answer is found, they can mark their question as answered. If a customer still needs help, their question is answered as normal by an agent.

Machine learning tools like these tap into all the company’s customer interactions and apply what they learn to customers and content. And because they learn as they go, every suggestion the tool gives is better than the last. Feedback is automatically collected to improve future suggestions and any gaps or outdated content in a company’s available bank of knowledge are flagged, so they can be updated.

In this way, the role of the agent moves from answering many, basic questions to fewer, more complex questions that require a more personal touch. This can help businesses to scale and boosts agent engagement and satisfaction as well. That said, teams should also be prepared for their operational metrics like handle time, to increase as after they deploy automation, as more of the simple requests are automated and only the more complex issues make it to customer service agents.

Use AI to anticipate your customers' needs

AI-powered tools can help you to anticipate customer needs and improve your customer service in real-time. By analysing patterns in historical customer interactions, businesses can predict how likely a customer with an issue is to have a negative experience. So, rather than waiting for a customer to make contact after something goes wrong, companies can get a better understanding of how a conversation is going in the moment, then make changes then and there to ensure a more positive outcome. Companies can also use these tools to route tickets – ones that have a low predicted satisfaction rating, for example, can be sent to a specialist team.

Artificial intelligence is an exciting prospect for many business and industry leaders speculate that the most practical applications of business-related AI will be for customer service. To distinguish your organisation from others, start leveraging AI today and drastically improve your customer experience.

Jason Maynard is VP and general manager of guide at Zendesk