Across the country, more organizations are leaning into predictive and prescriptive data
Ever wonder what happens after a customer hears this prompt: “On a scale of 0-to-10, how satisfied were you with your customer service experience?” When customers get a text or email prompt, the number they press provides data — but that data may not give decision-makers any context as to what made the experience amazing or horrific.
Was a “0” due to a negative experience with a call center rep, or just general frustration from someone with too many annoying administrative to-dos on their list? Was a “10” a reflexively polite response, or did something superior happen during the customer service experience? An add-in comment box can add some value, but only if it’s smartly managed and feedback is implemented. And even in the best case scenarios, surveys only provide a small sample that is not representative of all experiences, making it impossible to have a holistic perspective on overall customer satisfaction.
Artificial intelligence (AI) learning allows companies to lean into predictive and prescriptive data: what customers will want — even before they know themselves. And these pivots can occur in real time, across all interactions — not just a sample.
‘Expectation transfer’ drives need for hyper-personalized solutions
While post-interaction feedback can be helpful, this data is diagnostic and anchored in the past. Despite new action taken to improve future outcomes, there’s little to be done for customers who had negative past experiences.
Meanwhile, based on a bar set by leaders across industries, consumers anticipate superior customer service across businesses — a phenomenon known as expectation transfer. “Expectation transfer occurs when one key business raises the bar and consumers expect that across the board,” notes Andy Traba, head of product marketing for customer engagement analytics division at NICE. Consumers are looking for personalization, convenience, and an interaction that is seamless and hassle-free. No matter what industry a business operates in, having the ability to change perception of an interaction in real time can become key in improving customer experience and differentiating service.
But services are not one-size-fits-all. AI and machine learning can instantly analyze data, allowing stakeholders to quickly pivot and make unique decisions based on the needs and services of their organization.
“We’ve always had data, but there’s never been an efficient way of analyzing and operationalizing it, and that’s where AI learning comes into play,” says Amanda Belarmino, assistant professor at the William F. Harrah College of Hospitality at the University of Las Vegas, who has studied AI use cases in hotel management.
Unlike ad-hoc data sets and statistical analysis of the past, AI solutions are holistically built to elevate customer service solutions at every touchpoint, providing routing solutions, creating connections with customers throughout their journey and highlighting workforce engagement solutions.
Increased customer satisfaction
AI aids the customer service journey in multiple ways: tracking conversations in real time, providing feedback to agents and using intelligence to monitor language, speech patterns and psychographic profiles to predict future customer needs. But the real power of machine learning is anchored in automation and the ability of AI to continue to learn independently.
Belarmino, who has a Ph.D. in hospitality administration, remembers managing a call center for a reservations system, where her predecessor would monitor how much time agents spent on a call. But Belarmino recognized that time only told one part of a story. “The customers who don’t need to call won’t call,” she says, adding that many travelers will simply book online.
“The customers who do call may have complex needs that demand more time,” Belarmino says. An AI enabled customer experience program can analyze these conversations and pinpoint why customers are calling, what they need and what would streamline and elevate their experience.
AI can also match consumers to agents based on their unique needs. “AI can extend this capability to predict emotion and intent to make the perfect match and discover the best opportunities for downstream automation,” explains Traba about this use case. And this learning can extend to deliver a great experience, even to those customers who never interact directly with a customer service agent.
Research results back up the advantage of AI learning: A 2020 study conducted by Aberdeen Research found that companies using AI capabilities achieve 3.5 times greater increase in customer satisfaction rates.
“Every employee can be a top performer”
“Every organization has their gold-standard employee,” Traba says. AI learning can analyze what traits and behaviors employees bring to customer interactions, raising the bar for all. This is also done in real time, which can improve any case as it’s occurring. “Today AI can learn from top performers and share what makes them so great,” Traba adds. “Every employee can be a top performer.”
But analysis isn’t only used for training and setting customer experience standards. It can be equally key in helping identify employee pain points and prevent potential burnout.
“KPIs for success can be focused on employee well-being alongside customer experience,” notes John Havens, director of emerging technology and strategic development for the Institute of Electrical and Electronics Engineers Standards Association, a professional organization that develops, defines and reviews electronic and computer science standards. This may include analyzing keystrokes or behaviors as a way to monitor well-being.
At the end of the day, AI depends on the data it’s provided, which is why it can be essential for organizations to adopt an AI enabled customer experience solutions that captures and models data not only to elevate customer satisfaction, but to increase the health of the entire organization.
NICE is the worldwide leading provider of cloud and on-premise enterprise software solutions that allows organizations to make smarter decisions based on advanced AI analytics of structured and unstructured interaction data. Enlighten AI for CX self-learning AI solutions are built on 30+ years of experience using the largest syndicated interaction dataset. This solution analyzes every second of every conversation to identify the successful behaviors that drive extraordinary customer experiences. Enlighten AI for CX includes a suite of innovative, pre-built customer experience solutions that operationalize insights, accelerating action and turning customer service into a competitive differentiator. To learn more, visit nice.com.