Home Can AI Bring Customer Service Into the 21st Century? Thankful Thinks So

Can AI Bring Customer Service Into the 21st Century? Thankful Thinks So

Why is it that customer service never seems to get any innovation attention? For decades, providing great customer service has been a constant challenge and expense, yet relatively few technologies exist to ease the difficulty or cost. Sales and marketing see new software arrive every year, yet customer support has scarcely changed in the last half-century, save for better issue-tracking tools.

Technology has brought an expectation of immediacy from the consumer — an expectation impossible to fulfill in customer service. According to LivePerson, 34 percent of consumers wouldn’t rate a customer experience as excellent if it took the company more than a minute to respond. The average customer service response times? Almost three minutes for chat and 17 hours for email. SuperOffice reports that one in five companies fail to regularly respond to chat requests entirely. 

Over a decade ago, chatbots promised to solve these problems with 24/7 support. But, as we can see from the dearth of chatbot deployments in the industry, that promise hasn’t been fulfilled. Theories as to why vary, but most agree that chatbot interactions are unintelligent, frustrating, and obviously not human; at the end of the day, they didn’t resolve the customer’s need.

Increasingly, the strategy to make service better is to not provide any service at all. Instead, companies try to “enable” customers to find their own answers and solutions by deflecting their requests to an FAQ page or a form. While this saves time for support agents, it rarely provides a satisfactory end-customer experience. If the customer is fixing his own problems, the company gets none of the loyalty (or retention stats) that result from a customer feeling “looked after.” There’s an enormous difference between resolving a problem and deflecting it.

What Customers Want

Customer loyalty is the fuel that drives successful businesses. Keeping loyal customers is far more valuable than finding new ones — increasing customer retention by as little as 5 percent can lead to a 95 percent profit spike. And from the opposite perspective, NewVoice Media’s latest Serial Switchers report found that, in 2018, bad customer service cost businesses more than $75 billion. In other words, companies that figure out the customer service equation and generate loyalty could collectively add billions to their bottom line.

So what’s keeping customer service teams from claiming that lost revenue? What Forbes contributor and customer experience expert Stan Phelps calls “the customer expectation gap.” Phelps defines this gap across three dimensions, grounding each in data from IBM Institute for Business Value report:

  • Speed. More than eight in 20 consumers want faster response times, according to the report.
  • Consistency. Sixty-eight percent of those surveyed said they want customer service teams to harmonize their experiences across all channels of communication.
  • Personalization. Of those surveyed, 76 percent expect customer service teams to understand and address their individual needs.

What can customer service teams do to close the gap? Support agents can only help so many customers per hour, and pushing them to work faster cuts consistency and personalization. Instead, companies like Thankful are taking on the challenge with fresh AI technology and solutions, enabling companies to provide service that’s quick, personable, and consistent. In essence, Thankful hopes to fulfill the long-forgotten promise of customer service: giving customers what they want.

The Tech Customer Service Needs

Thankful’s mission to bring artificial intelligence to bear on shoddy customer service began when CEO Ted Mico met co-founder and CTO Evan Tann while he was developing an AI-powered wine recommendation tool for customers.

“I’d had a procession of bad customer experiences earlier that week,” Mico explains, “so I jokingly asked Evan, ‘Why are we working on fixing wine recommendations when customer service is so broken?’ Thankful was founded that week with the mission of making help human.”

Realizing many of Mico’s service issues could’ve been handled without human intervention, Mico and Tann went to work on an AI platform. Tann released the first version of the software on GitHub the same day as Microsoft’s BotBuilder. It blew up quicker than the big-budget build, and Tann’s radical approach soon made it to the front page of Hacker News.

Despite the initial acclaim, Tann’s team had to radically rewrite the codebase over several years before the platform could attain the 99 percent out-of-the-box accuracy rate the brand used as a benchmark before it could launch to businesses.

“Most [early] bots couldn’t provide correct responses after a couple of tries, which frustrated early-adopting consumers, businesses, and influencers in the space,” Madhu Mathihalli writes in industry magazine TotalRetail.

“We’re not a bot,” Mico stresses. “In fact, more than 90 percent of incoming queries we’re dealing with are email, not chat. Thankful is the brain that governs service via any text-based channel.”

“The key to great service is understanding what the customer wants and being able to deliver what the customer needs,” Mico adds. “At Thankful, we talk a lot about the five pillars that make up great customer service — speed, knowledge, accuracy, empathy, and thoroughness. Any of these pillars is hard for technology to emulate — getting all five to work together took almost three years of programming.”

Faster Is First

Once Mico and Tann had an accurate model, they set their sights on the most glaring of the three customer service gaps: speed.

“Consumers’ expectation for immediate service was created by tech, and it can only be solved by tech,” Mico argues. “We wanted technology to deliver on the promise of solving problems for the customers, delivering a human-like experience that makes them feel as though they’re being properly looked after.”  

“We currently average 40-50 percent resolution rates for our e-commerce clients,” he says. Without an agent in the loop, Thankful still strives to provide a high level of service. This allows a company’s human agents to focus and dedicate more time to the remaining issues, which are often more complex.  

Consistency Is Critical

The second piece of the customer service puzzle, consistency, is the one that Mico and Tann think has been most absent from midmarket online retailers. Gladly’s 2018 Customer Service Expectations Survey revealed that 76 percent of customers receive conflicting answers when they ask different support agents the same question.

Mico says that the replicable nature of e-commerce customers’ challenges is partially what led him and Tann to focus on the space. “It’s mostly repetitive issues like shipping, exchanges, returns, and product information: perfect for machine learning,” he says, “but now Thankful is also capable of much more complex e-comm-related actions.”

The Proof Is in Personalization

Consistency, of course, can be a double-edged sword. Customers rightly expect to have their individual circumstances considered, which most rules-based AI platforms fail to do.

Mico acknowledges that Thankful can’t honor every customer request, but he explains that it can make exceptions. “We remember who you are — if you’re a longtime customer or VIP member, Thankful takes this information into account and responds appropriately,” he says. “Additionally, our AI is smart enough to understand context such as key information, like an order number, so you won’t have to repeat yourself later on — it will retain information conversationally just like a human, but with a better memory.”

In the future, Mico hopes to make Thankful even more “human” in its personalization skills. “We get tons of thank-you responses from customers with smileys. Customers assume that because the problem is being solved in a human-like way that a human is responsible. Customers don’t tend to send heart emojis to robots,” he says.

But if more companies start adopting a similar approach to customer service, customers just might.

About ReadWrite’s Editorial Process

The ReadWrite Editorial policy involves closely monitoring the tech industry for major developments, new product launches, AI breakthroughs, video game releases and other newsworthy events. Editors assign relevant stories to staff writers or freelance contributors with expertise in each particular topic area. Before publication, articles go through a rigorous round of editing for accuracy, clarity, and to ensure adherence to ReadWrite's style guidelines.

Brad Anderson
Former editor

Brad is the former editor who oversaw contributed content at ReadWrite.com. He previously worked as an editor at PayPal and Crunchbase.

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