SmartAssist Blog

Welcome to the Future: The Customer Service Landscape is Changing

Machine Learning is changing the customer service landscape“I miss waiting on hold for 20 minutes while an automated system misdirects my call, then a representative denies the request they never fully understood,” said no one. Ever.

Sad to say, despite some companies’ best efforts, this is how most people view traditional customer service. A necessary evil. A painful way to waste far too long for an all-too-slim chance of satisfaction. Given half a chance, almost anyone would have avoided calling Customer Service back in the day.

Today, that chance does exist, and the modern customer — especially Millennials — is increasingly demanding the opportunity to seek out satisfaction in alternative ways: text, chat, social media, email… pretty much anything other than picking up the phone and calling the dreaded support line.

As the technology making these alternatives possible grows more ubiquitous and reliable, companies are being forced to alter their support methods and tools to meet customers where they want to be. But doing so brings with it some challenges that must be met successfully if companies expect to thrive in this emerging customer support landscape.

Training support representatives

Training a customer service representative (CSR) used to involve teaching one set of “hard skills” (usually one or more computer systems and the physical use of the phone) and one set of “soft skills” (how to appropriately interact verbally with a caller). Today’s CSR needs to be able to effectively do their job through every format and channel available to the customer.

Not only does this create a more complex training requirement and a longer learning curve, it also makes it more difficult for companies to maintain consistent messaging across every channel.

Routing nightmares

While on hold, we’ve all heard, “your call will be answered in the order it was received.” In most call centers, that’s the extent of the system’s routing and prioritizing capabilities: since there’s no way to know what your issue is until your call is answered, there’s no way to prioritize more serious problems.

With support requests coming in from all manner of digital channels, however, the available input becomes a double-edged sword: While there’s a lot of value in being able to know immediately who’s contacting you, what their basic challenge is, and a host of other information that’s been gathered automatically to provide context, this also means that customers have grown to expect that issues that deserve higher priority will instantly receive that priority.

Regrettably, the technology supporting that level of real-time prioritization has lagged behind expectations.

Insourcing vs. Outsourcing

In a commendable effort to more effectively own the customer support experience, many companies are doing an about-face from the last two decades’ trend of outsourcing customer service. However, the economics of the situation hasn't changed: domestic CSRs still cost far more than CSRs working overseas. So, a delicate balance has to be struck between the experience they want to create for their customers and the cost of maintaining that experience.

To compensate, companies have had to be creative and merciless in their efforts to streamline the support process, understandably relying on technology to bridge the gap where necessary. Sometimes, the results are amazing, but at other times, it’s resulted in diminished quality.

For example, chatbots were introduced as a viable option just a couple of years ago, and the explosion in their popularity has been incredible. But, when you really look at their current capabilities and limitations, it becomes clear that most chatbots offer a pretty poor experience in comparison to a human CSR. Without including that valuable “human touch,” a significant opportunity is lost and finicky customers won’t forget that.

Bridging the gap with technology

While the proliferation of  technology can be seen as the cause of some of the challenges noted above, an objective assessment can only conclude that the AI initiatives will become a core madate of every customer support orgnization and the  landscape is in a much better position now to effectively help customers than it ever has been before. Although evolving technology has created some challenges for companies, technology has also provided incredible solutions.

Consider, as a prime example, the SmartAssist (formerly Wise Support) intelligent support ticket routing system:

Serving as an automated layer between incoming support tickets from any channel and available CSRs, SmartAssist uses sophisticated artificial intelligence (AI) algorithms to:

  • scan incoming tickets
  • analyze the language
  • review past customer interactions
  • select the most appropriate available representative to handle the request
  • offer suggested responses pulled from an approved database
  • provide automated responses without transferring to an agent for repetitive questions

This powerful solution uses AI (which only grows more effective the more it’s used) to achieve instant prioritization and split-second routing, and shortens the CSR’s learning curve on each ticket by pre-scanning the contents and suggesting appropriate responses based on past interactions.

In practice, SmartAssist results in dramatically faster turnaround times for support tickets without ever losing that vital human touch that only a flesh-and-blood CSR can bring to the customer support experience. Even at volumes exceeding one million tickets per month, the SmartAssist solution keeps a streamlined customer support team humming along at maximum efficiency day in and day out.

To learn more about SmartAssist and what it can do to meet your customer support challenges, contact us today.

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Topics: Customer Support, Machine to Machine Learning