When it comes to CX, don’t get hung up on the jargon
Published: Feb 02, 2017
Author: Gordon Littley
If you’ve been following the tech news, you‘ve probably read a lot about artificial intelligence (AI), machine learning and other similar technologies—and how they’re the key to great customer experience (CX). You might now be wondering which of these technologies is the right one for your business. You might be asking, “just what is the difference between machine learning and machine reasoning anyway?”
It’s easy to get caught up in the whirlwind of excitement around new technologies. But that shouldn’t distract you from what really matters—and that’s not the tech, it’s getting the customer insights that inform great CX.
Making sense of all the data
Today, you’re collecting more data on your customers than ever before. You can track their shopping habits online. You can analyze their conversations with your call center representatives or chatbots. And you can use Internet of Things (IoT) devices to monitor in-store behavior. But organizations are struggling to harness all that data—51% say they’re struggling with big data variety and complexity1. And if you have enough data, you can find just about any pattern.
Are you familiar with the Texan sharpshooter fallacy? It runs like this: A farmer in Texas has a habit of letting off a round or two into the side of his barn. Over the years this amounts to a lot of holes. Then one day his son paints a target around the bullet holes. A passing stranger comments: “That farmer must be one hell of a shot!”
Tech is great, but not a panacea
How can you harness all the data to drive CX? And, importantly, how can you make sure you don’t jump to the wrong conclusions—like the passing stranger? Technologies, like AI and machine learning, are providing a means to that end. And these tools are becoming ever-more sophisticated. They can handle more data, do it faster and find correlations. They can help you wade through all the data—and the most advanced might even suggest actions you can take to improve CX.
If you were at the National Retail Federation’s BIG Show in New York City last month, you might have seen Blue Yonder showcasing its replenishment optimization tool. It says that retailers using machine learning have seen a reduction of up to 80% in out-of-stock rates without increasing waste or inventory. There are plenty of other examples of AI being used to provide insights and deliver more personalized service—check out this post by our CMO, Tony Recine on marketing segmentation for more.
Less jargon, more insight
But can a machine truly see the whole picture? AI might tell you the connection between the time customers spend waiting in line and customer satisfaction. And it might then suggest ways you can optimize shift patterns to reduce waiting times. But it won’t come up with innovative ways of solving problems—like providing entertainment or keeping customers informed so that they they don’t become frustrated by a long wait. Human intuition hasn’t been replaced —yet—when it comes to CX innovation. If you really want to understand your customers, there’s still a strong argument for getting on the frontline and talking to them.
AI, machine learning, contextual learning and the rest are helping turn today’s big data into customer insights. But if you are going to turn that into great CX, they need to be acted upon and drive changes across all your channels. That means—wherever you’re getting your insights—they need to be shared, understood and actioned by employees that input into delivering CX—and that’s every one of them.
Gordon Littley is the Managing Director of Verizon’s world-class Customer Experience practice, which specializes in delivering high-value solutions and specialized sales for Verizon Enterprise customers.
1 Harvard Business Review Analytic Services report, sponsored by Verizon, 2017