In our last post we looked at how AI / ML is being used in customer service to boost efficiency and reduce customer effort by transforming the queue experience and reducing wait times. With AI-powered self-service systems handling most routine inquiries, customers no longer need to queue for answers or navigate complex IVR menus. Not only does this improve the customer’s experience but it also enables operators to handle even greater volumes of inquiries.
But using AI / ML to enhance customer service extends far beyond queuing.
So let’s take a look at some of the ways these technologies are fundamentally transforming operators’ approach to customer service.
Changing the mode
Traditional approaches to customer service emphasize efficient resolution of customer problems when they’re reported. AI being used to resolve simple inquiries and problems via intelligent self-service, enables human agents to focus on more complex problems, higher-value interactions, or issues where empathy and problem-solving are required. It is also employed to shorten queue times, reduce the requirement to repeat information, ensure customers are efficiently transferred to an agent that can handle their problem, and to boost efficient problem-solving.
But AI has the potential to flip traditional customer service models on their head and support a shift towards proactive customer care with predictive, real-time resolution of issues.
This new mode of customer service uses data, automation, and machine learning to solve a high proportion of problems autonomously, using predictive analytics to identify issues by analyzing real-time network data, customer behavior, service requirements, historical interactions and so on. This shift to proactive customer service enables operators to pre-empt or resolve problems before customers reach out for help – significantly reducing or entirely removing customer effort.
Hyperpersonalizing customer service through AI
Back in 2016, Salesforce announced that by 2020 51% of customers expected companies to anticipate their needs and make relevant suggestion before they made contact. More recently, Statista, found three-quarters of surveyed global customers now expected a more personalized experience.
While traditional customer service models emphasized consistency and efficiency, the result was usually a generic, scripted response.
In contrast, AI helps deliver the highly personalized experiences customers now expect by tailoring responses based on a customer’s usage, history, location, previous interactions, preferences and so on.
For example, if a network problem is detected, it can accurately pinpoint anyone that’s affected and assure them that the issue is already being resolved by network engineers. An estimated time of resolution can be provided, with customers proactively notified as the fix progresses, anything changes or the problem is resolved - with updates tailored according to customer preferences.
This not only reduces customer effort by proactively providing information before it’s requested, but also builds trust and loyalty.
Delivering total experience excellence
With AI working proactively and managing most routine inquiries and tasks, considerable stress is removed from human customer support agents. But AI has the potential to go a step further by boosting the performance of agents and automating many routine tasks. Intelligent co-pilots, for example, provide real-time insights into customers’ problems – making helpful suggestions as to next-best-actions and automatically summarizing the call. All of this frees up agent time and reduces the cognitive and emotional load on agents, allowing them to focus on being empathetic, delivering a personalized touch and engaging customers.
Moving forward, AI will be able to interpret and respond to human emotion. Emotion AI – also known as affective computing and artificial emotional intelligence – is a subset of AI that measures, understands, simulates, and reacts to human emotions.
This is already being introduced into some call centers. In voice calls, voice analytics detect voice inflections and recognize whether these correlate with stress, anger or happiness; in video calls AI can pick up micro-expressions that reveal a customer’s emotional state. This insight can be shared with agents, along with best-practice advice as to how to handle the customer’s emotional state – ensuring that all agents are operating at the same level as the most effective agents.
The AI co-pilot might suggest effective EQ phrases, for example, that are proven to reduce tension, or an intelligent chatbot might employ these phrases automatically – further humanizing the experience.
This type of technology supports hyper-personalization by detecting a caller’s accent and matching it or ensuring that an intelligent chatbot responds as the gender of customer service agent the caller prefers.
Similar technology can also be used to detect when an agent is becoming stressed, which provides the opportunity to support them before they burn out – thereby potentially reducing agent churn.
AI-driven automation and human empathy is a powerful combination that delivers the personalized, seamless and effortless experience customers now expect. In our next post find out how Subtonomy is using AI / ML in its platform and applications to deliver a better service experience today.