Back in 2011, Gartner made a bold prediction that “by 2020, customers will manage 85% of their relationship with the enterprise without interacting with a human.” The convergence of cloud, social, mobile, and information technologies are driving customer support into a “posthuman” age of self-service and automation.
We’re now well past the halfway mark in that forecast’s journey toward fruition, but it can’t be described as smooth sailing. While automation efforts may be intended to streamline customer service, making things faster and easier, the result is often the opposite.
An Accenture Global Consumer Pulse Survey indicates customers are increasingly frustrated with the level of service they receive from companies. The survey found that 91% of customers have to contact a company multiple times for the same reason, 90% are being put on hold for too long, 89% are forced to repeat their issue to multiple agents or representatives, 73% expect customer service to be easier and more convenient, and 65% expect it to be faster.
Despite a wave of innovation in customer experience tools, data science and analytics, mobile connectivity, and proliferating always-on communication channels and services (social media, asynchronous messaging, chatbots, etc.), there is a growing disconnect between companies and customers — we seem to be having more trouble communicating instead of less. What gives?
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In our quest to leverage technology and make things more convenient for customers, we’ve unintentionally inundated ourselves with too many tools and too much information. What is often referred to as app fatigue or information overload affects businesses just like it affects people, overwhelming customer service departments with multiple channels transmitting myriad streams of valuable data that isn’t cohesively integrated and can’t be easily accessed when needed. It’s why those frustrated customers end up filling out web forms that have no relevance to their questions, repeating their issues to multiple agents over the phone, or sending emails and messages and tweets to poorly monitored accounts. The problem isn’t necessarily too much automation, but too much disconnected information for humans to handle.
There is no going back, so we must find a better way to face the future.
This is where AI and machine learning can help. “Robot” or “virtual” customer service agents cannot replace human beings, which the aforementioned Accenture study noted are always the preferred choice of contact for 83% of customers. But empowering those human agents via technology with the precise information they need at the moment they need it to best quickly help their customers is a functional model toward progress. Using automation to parse and prioritize and instantly surface relevant information from those omnichannel data streams to assist human business representatives is key to realizing that “faster and easier” customer experience dream. In a recent interview with MIT Technology Review, the for CEO for Slack, a popular workplace collaboration tool, described his vision of how AI can reduce information overload within his company’s app: “You could imagine an always-on virtual chief of staff who reads every single message in Slack and then synthesizes all that information based on your preferences, which it has learned about over time. And with implicit and explicit feedback from you, it would recommend a small number of things that seem most important at the time.”
AI in customer service can work the same way.
Utilizing machine learning to deliver insight and map relevance can deliver immediate benefit. You still want to have self-service and automated means for customers to access according to preference, but your human agents will get AI’s assistance in the crucial performance of their roles. Consider your organization’s best customer service professionals. They probably have a lot of experience and have acquired a deep knowledge base. They’re instinctive and they’ve worked out shortcuts in your system you might not have predicted. With AI “observing” such masters at work, they can mimic that expertise and supply it to every agent on demand. The implication is that when new employees start, they can benefit from the knowledge of your longest-serving employees via AI engines always at the ready to recommend effective action.
If you combine automation and AI with the power of the human touch, you enable a fast, smooth, and seamless handoff between all those apps and bots and data streams and the humans who are dealing with them — at the right point in the conversation and the right point in the customer journey. AI integration across all channels, customized to blend with the platform utilized by the organization, promises to not only restore sanity to customer interaction, but to truly enhance the experience for all involved.
We Aren’t There Yet
Much of AI’s potential in customer service applications still remains a dream. The results of the MIT Sloan Management Review 2017 Artificial Intelligence Global Executive Study and Research Project showed that “three-quarters of executives believe AI will enable their companies to move into new businesses. Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage.” However, the survey also noted that only about one in five companies has incorporated any AI in any of their processes, and less than 39% of all companies have an AI strategy in place.
If you’re just getting started on integrating AI into your organization’s customer experience workflow, begin by focusing on specific narrow goals, plan to optimize, consider your audience, and make sure you have metrics in place to measure success. Most importantly, focus on the human element. The goal is not just to pile on to the technologies supported and methods of interaction, but to deliver a more satisfying experience for your customer — who is not a machine.