In this special guest feature, Krishna Raj Raja, founder and CEO of SupportLogic, discusses how AI can help service agents detect customer signals – picking up on how a customer is feeling and also understanding context, then dynamically flagging or escalating a case before an annoyance turns into a major problem. SupportLogic enables companies to prevent customer escalations and protect revenue by understanding and acting on the voice of the customer in real-time. SupportLogic delivers a continuous service experience (SX) platform that uses AI to extract and analyze customer signals from both structured and unstructured data across multiple service channels and provides recommendations and intelligent collaborative workflows.
In movies and popular culture AI is always portrayed as hyper-intelligent and emotionless. In fact, movies differentiate AI beings from humans by their absence of human emotions (think of Data from Star Trek and HAL from 2001 A Space Odyssey). And, out here in the real world, there’s another misconception that AI is used to replace human beings by automating mundane and repetitive tasks. In reality, however, AI can be used to enhance and enrich human-to-human communication. A couple of my favorite examples are Apple Facetime’s eye contact correction technology and background noise reduction using deep neural networks. These are both good examples of AI that is transparent to the user – it sits in the background and you don’t realize that it is working behind the scenes. But how can AI be used to improve human-to-human communications in a business context?
One powerful way is for AI to do what it’s good at – finding patterns hidden in data – to pick up on customer “signals.” For example, in the typical customer support center, a customer might interact with several different people over web and email. AI can analyze those interactions to detect sentiment signals and urgency – picking up on how a customer is feeling and also understanding historical and situational context. Then, it can analyze other customer cases where this same situation existed and dynamically predict whether a customer situation will escalate as annoyance turns into infuriation. The result is that support engineers are able to be more in-tune with their customers’ intentions and also be both proactive and productive in helping them.
Why Customer Service Needs AI
Customer needs, in general, and how businesses impact their customers’ experiences have changed significantly. The pandemic has accelerated business adoption of digital engagement channels for customer communications and handling service interactions (e.g. questions, issues, complaints, returns and other “fixes”). Poor service experiences by customers often contribute to higher churn rates and negatively impact a company’s financial performance. Also, a company’s relationships with its customers are no longer transactional; today in the subscription economy, building long-term customer loyalty is critical to driving revenue growth. A Microsoft study stated that 90% of customers place a high value on quality of service when choosing and retaining a brand, and 58% “show little hesitation at severing the relationship” should the brand fall short of service expectations.
So, where (and how) does customer service fall down? Agents on the front line that engage with customers are not sharing information with other departments (i.e. operating in silos with “tribal knowledge”) and as customers are routed to those different departments, they are forced to re-tell their stories, leading to frustration. And, a lack of customer knowledge, including their prior experiences, puts agents in a reactive mode that also leads to frustration. Service organizations need to demonstrate knowledge and empathy so that people don’t “just feel like a number.”
How AI Can Improve the Customer Service Experience
With many brands today, service reps are the only live humans with which customers interact, giving them an outsized impact on the overall customer experience. Delivering a superior service experience enables companies to prevent customer loss, reduce escalations and service costs, and deliver a stellar experience that significantly increases customer lifetime value and satisfaction. How does it work?
First, AI can extract customer signals from unstructured data in service interactions. While many systems capture vast amounts of data, the key is to capture signals that can lead to important insights and recommended actions. The data can come from chat conversations, emails, discussion forums, voice-to-text or surveys. At the same time, this type of advanced AI can also understand context, which plays an important role in informing service agents and advising them on what actions to take with a customer or when to escalate a support issue. The combination of signal understanding and context is powerful – it makes service agents smarter, more customer-focused and “human” in their approach. From the customer’s perspective, it’s the difference between interacting with someone who seems to know them vs. interacting with a complete stranger.
In addition, agents quickly get recommendations and can take action in the moment, during the interaction. This real-time response plays a key role in helping an agent to deliver a positive service experience that prevents customer churn and converts issues into opportunities to create happy customers. According to HubSpot Research, 90% of customers rate an immediate response as important or very important when they have a service question.
Real AI Generates Real Results for Real Customers
Today, we’re seeing companies use this kind of advanced AI that harnesses customer signals to drive significant improvements in their operations. Companies report significant improvements such as a 25% reduction in customer churn, a 40% reduction in customer escalations and a 35% reduction in operational expenses. These types of results can have a significant impact a company’s top-line and bottom-line, and the results tend to happen in a matter of months, not years.
In today’s economy, sustained revenue growth depends on building healthy customer relationships, and the customer service function is playing an increasingly important role in protecting and generating revenue for organizations. In many cases, service and support is the first (and potentially last) touch point for customers and creates a lasting impression of a company’s brand image and reputation.
Companies can only grow and protect their revenue if they can understand and act on unbiased customer signals from service interactions that happen in the moment. This is why AI can play such an important role in improving the service experience – it elevates agent performance and company performance to drive growth, improve operations and create a competitive advantage.
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