AI for Customer service 

AI for Customer Service
AI for Customer Service

Success in the digital transformation era starts with providing the best customer experience. CIOs should use AI to redefine how businesses interact with customers.

Business entities are focused on improving customer experience as that becomes one of the key differentiators for businesses moving forward. What’s critical for CIOs to understand, though, is that the focus shouldn’t be on slightly improving customer service; it should be on completely rethinking it, and that requires the use of artificial intelligence (AI).

Customer service needs a rethink in the digital era

Customer service today is largely reactive. An individual calls into a business’s contact center and needs to provide a wide range of information, such as name, address, loyalty number, contact information, and a description of the problem. If the agent can’t help, the caller is passed on to the next person and the process starts all over again. There are many problems with this, most notably it wastes the customer’s time and frustrates them.

Improving customer service isn’t about making marginal improvements such as reducing hold times. Rather, it’s about completely rethinking how customer service should work if enabled by AI. AI is able to connect the dots between a user’s activity and information and make the agent seem almost like Kreskin, as they can predict why the person is calling and suggest how to solve their problem even before asked.

How AI can help customer service

To illustrate, I’ll provide a couple of examples of customer service with AI.

Agent guidance without AI

Sarah Mitchell, a frequent traveler of Nationwide Airlines, is travelling from Denver to New York’s JFK airport, but her flight is cancelled because of weather-related issues. The flight isn’t until tomorrow, and she is given advanced notice from the airline.

Sarah logs onto the website and starts to search for new flights. As it turns out, all flights to JFK are either full or cancelled, so she expands her search to include LaGuardia and Newark in New Jersey. After spending 30 minutes, Sarah gets frustrated and calls the airline where she is greeted by a polite agent. The agent asks her name and frequent flier number and how he may help her. She explains the situation and the agent goes through many of the steps Sarah already went through. After a lengthy delay, Sarah starts to get agitated and is concerned the flights are filling up fast and she may miss out. Finally, after about 30 minutes, her flight is rebooked into a connecting flight to Long Island. It’s not direct, like the other, but she is happy to get home, although irritated the transaction took so long.

Agent guidance with AI

Assume Sarah does all the same tasks up until the time she calls. When she calls the airline, the AI has gathered all the information and understands that her flight was canceled and that she had expanded her search to include other regional airports. The agent also knows that Sarah is a top-level flier and wants to ensure that she is re-accommodated as quickly as possible.

When the agent answers, he greets Sarah by name and says, “Good evening, Miss Mitchell, thank you for your loyalty to our Nationwide, I see you were trying to change your canceled flight. Can I help you with that?”

Sarah is delighted the agent knew the information and says, “Yes, I am trying to get to the New York area, but my flight has been canceled.” The AI has already figured out she is trying to fly to the closest airport to New York City and has found the connecting flight to Long Island. The agent says, “I can put you on flight first thing in the morning to Long Island. Will that work for you?” Sarah happily accepts it.

In the above example, based on her actions, the AI was able to understand who was calling, her status with the airline, and what she was trying to accomplish. And it provided an immediate response for the agent without requiring him to look up or ask for any additional information. This has the added benefit of getting the customer off the phone quickly, cutting down the hold time for other passengers that were affected.

Intelligent Routing without AI

David Thomas is a long-time subscriber to an internet-based music service and notices his bill is larger than he anticipated. He goes online to check his account and notices that he was charged for an album he purchased but then subsequently canceled.

In the search bar on the website, David enters “Erroneous billing” and is given a number of options to choose from. He can’t find what he is looking for and does some other searches. Finally, he gives up and calls the help number, where is greeted by an agent that asks how she may help him. He tells her the problem, and she states that she is sorry but she is in the online help department and can only answer questions about how the service works, and she transfers him to account support.

David then needs to explain the problem to this agent, who informs David that account support doesn’t handle refunds and transfers him yet again. After going through the explanation a third time, the refund is finally issued.

Intelligent routing with AI

When David calls the online music company, the AI has already analyzed the web search information and predicts that David is calling to ask for a refund on a charge that he feels is erroneous, and it routes the call directly to the refunds department. The AI pulls information from the portal, CRM system, and other data sources to analyze the customer history.

The agent is quickly informed that David has been a customer in good standing for years and has never asked for a refund before. The agent greets David with, “Hello, Mr. Thomas. I see you feel you were erroneously billed. Can I help you with that?” David explains that he purchased the album but then canceled it quickly. The agent informs him that accidents happen and the company would be happy to refund the money.

In this case, the AI was able to gather information based on David’s activity and predict the purpose of the call. The results of the analysis were used to route the call to the appropriate person. The AI then executed business rules to determine whether the refund is approved and informed the agent.

Businesses are gathering massive amounts of information about the habits and activities of their customers. The challenge is that people can’t analyze the large volumes of data as fast as machines. AI systems can be used to examine data and make inferences that can help businesses service customers faster and more accurately, making every customer service person more effective by putting the right information in front of them.

Customer service matters more than ever, and CIOs should look to AI as a game-changing technology.

Source: Why customer service needs artificial intelligence

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