AI is viewed by customer service decision makers and agents alike as a boon to the customer and a significant boost to employee experience. AI adoption may still be relatively nascent in the realm of customer service, but it is set to soar in the coming years as more & more teams turn to chatbots, text, voice analytics, and various other use cases. Use of AI by customer service teams is projected to increase by well over 100 per cent over the next 1.5 years or so.
Since customer service is traditionally considered a cost centre by most organizations, the focus on customer improvement efforts has been inextricably linked on reducing costs. That has proved to be a critical mistake, as the resultant has left everyone unhappy. Even as customers grew increasingly sick of pressing two for reservations and four for service, service agents too got increasingly sick of answering the same questions over and over.
Increasingly, virtual agents are being used for service. These are essentially automated systems, trained on service transcripts, that can use AI to recognize and respond to customer requests whether by phone or chat. Unlike much of the history of customer service, the focus here is not on reducing the number of jobs and cutting costs.
The two most important findings from the deployment of virtual agents are that the most significant gains are from improvement in the customer experience and not from cost savings and that successful virtual agents are build on the basis of bots working with humans rather than replacing them altogether.
A lot of customers, recalling their experiences with Interactive Voice Response (IVR) systems are sceptical. While they had to navigate phone menus for gathering information or eventually getting through to a human agent, the experience hardly led to shorter wait times or a better level of service. Online chats with agents can be similarly frustrating because of the slow responses, since the agent is juggling with anywhere between two to six sessions at any point in time.
Virtual agents however can bring about a shift for the better in terms of customer experience. If a virtual agent can interpret the intent behind your chat or phone request, it can get you an answer more quickly and efficiently than a human agent. For most common queries, this delivers a better experience than interacting with an actual human.
Let us drill down further to unearth details about how virtual agents and humans can work together.
Humans and bots have different skills. Human customer service agents can easily recognize when someone is frustrated and can respond with empathy. AI-powered virtual agents, on the other hand, are wizards at assembling data from disparate systems to render a judgment instantly, even if they lack the emotional intelligence to know why such a decision may or may not be the right one under the circumstances.
Bots can save human agents time. Data regarding deploying of virtual agent systems shows that those systems can typically handle 80% of incoming questions without assistance. But what happens when the question is too complex or the customer too upset to deal with a virtual agent?
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Even under such circumstances, virtual agents can save time. Some virtual agent systems are actually designed to collect information, such as the customer’s name or account number and a description of the problem, and suggest resolutions as they hand off the call to a human representative.
Bots can make human agents smarter. Some companies, for example some high-end professional services firms, would rather have human agents handle all service calls. But the same human agents can appear smarter to the customer if a bot is whispering in their ears. Let’s say, for the latest promotion for a product line or for a product which has just gone out-of-stock, the bot will quickly bring it to the attention of the human agent. It’s somewhat akin to having an assistant scurrying around in real-time, to figure out the best things for you to say to the customer you are conversing with.
Bots can improve with human supervision. In cases where virtual agents can’t figure things out, a human supervisor can be of assistance. When it comes to customer service through online chat, a human agent can efficiently manage eight or ten conversations between chatbots and customers — a far higher level of productivity than if the agent were answering the questions herself. And when a virtual agent gets stuck, the more experienced human can step in and solve the problem. Not only that, but the human agent can tag the problem when the virtual agent gets bogged down, which allows the AI system to learn from that scenario and become smarter for the next instance when that question is asked. Thus, both the bots and humans are able to focus on what they do best. For the virtual agents, that is handling routine cases quickly and efficiently. For the human supervisors, that is solving more complex problems, using empathy and training the virtual agents on cases that they didn’t recognize at first.
Finally, let’s take a look at AI, customer experience and data insights aspects.
Customer experience is a competitive driver of growth when successful and the greatest source of risk when failing. Data insights are one of the primary tools for CX enhancement. CX datasets are messy, however, mainly because customer behaviours are chaotic. The rules are undefined and the success criteria are not well defined either. Often, CX can become the nightmare dataset for an AI developer.
The successful application of AI in customer experience requires 3 fundamental capabilities:
Real-time Insights Delivery
Data Unification: Data unification to create a single customer view is a must for any type of behavioural analytics. AI thrives on information — the more the better.
The new generation of data unification tools make this daunting task cheap, fast, and relatively pain-free. Customer journey analytics platforms provide this service for a fraction of the cost of the dedicated data services providers of yore.
Real-time Insights Delivery: For AI to impact the customer experience, insights need to be conveyed in the moment through the customer’s chosen touchpoint. Integrating with these touchpoints is the key to in-the-moment engagement. Most enterprises must rely on myriad on-site, home-grown and legacy touchpoint data sources — product interfaces, payment platforms, point-of-sale systems, customer care, etc.. This reality creates a challenge for delivering real-time insights and still remains very much a custom affair. Customer journey analytics platforms are now filling this gap with a host of APIs options and development kits to deliver comprehensive, real-time touchpoint integration with minimal investment.
Business Context: For a simple, isolated interaction, AI is able to deliver results by simply knowing that an email is an email and a campaign is a campaign. But in holistic, cross-channel journey analytics, the idea that touchpoints of a similar category will be the same across enterprises is an outdated notion.
Customer journeys are as unique to individual businesses as fingerprints. Every company has its own set of touchpoints and a distinct method for employing those engagements in their customer experience. For AI to deliver value, it must be given some context. AI must know the significance of these events in shaping a customer behaviour. That requires an awareness of both the journey that these touchpoints helped to shape and the KPIs which were subsequently impacted by that customer behaviour — whether related to revenue, profitability, customer lifetime value, customer satisfaction or other factors driving high-level business performance.
Armed with that information, AI systems can do more than find the “next best action” or the optimal audience. With proper business context, an AI can find touchpoints and tactics which actually shape the customer behaviours behind the business’ primary measures of performance.
— Raja Mitra