What Does It “Feel Like” to Be Your Customer?

This is the central question any customer experience professional must ask, when trying to understand the quality of a customer’s experience.

It is also a profoundly human question; it evokes the theory of mind that fuels all human interaction, and to answer the question requires empathy on the CX professional’s part. But empathy cannot be automated. No chatbot, algorithm, or line of computer code can possibly tell you how it “feels” to be one of your human customers.

As computer technology first became widely available in the latter half of the 20th Century the business world exploded with business strategies that relied on machines and automation to improve the efficiency of their business processes. Enterprise-wide initiatives such as business process re-engineering , Total Quality Management and Six Sigma relied on standardization and automation to ensure that a company’s processes were more predictably reliable, with reduced error rates. Over the last several decades, as a result, business efficiency has greatly improved, as has the overall quality of most products and services. ?

But as efficiency and predictability improved, business competition moved to an even higher level, and the problem facing competitive businesses today is much more complex than simply eliminating product flaws or service problems. Today’s smartphone-carrying customers have much higher expectations, and 21st Century technologies enable businesses to interact individually with customers, treating different customers differently, one customer at a time – even when they serve millions of customers.

So today much of the competitive battle revolves around each individual customer experience – trying to make sure the customer involved is not dissatisfied at any point. And perhaps the fastest way for a CX professional to identify points of friction in any customer’s experience is to look for feedback from the customer himself or herself. A low score on a voice-of-customer (VOC) survey, for instance, is a red flag signaling a significant flaw or problem with that particular customer.

To understand the reason behind the customer’s dissatisfaction, however, a simple numerical score will not be sufficient. Before undertaking any remedial steps at all, we must dig into the root cause of the customer’s disappointment, examining whatever comments might have been submitted by that customer, or by any other customers referencing the same processes, either as an element of the survey itself, or perhaps contemporaneously on some other format or platform.

And this is where AI and machine learning are beginning to play a significant role – in helping CX professionals explore the universe of customer feedback, messaging, and other signals, to assemble an empathetic view of the customer’s actual feelings more rapidly and cost-efficiently. Rather than deploying teams of people to scan thousands of text comments and messages manually, AI and machine learning technologies from companies like Alterna CX can quickly scan through gigabytes of such unstructured data, flagging troublesome comments for immediate attention, and identifying not just the keywords denoting problems, but even the likely emotional state of the customer as he or she submitted the comment.

One of Alterna CX’s Turkish clients is Ko?ta? (part of the UK’s Kingfisher Group), the number one home improvement retailer in Turkey, with 3,000 employees and 10 million transactions per year involving both big-box retail and e-commerce. More than 90% of these transactions can be associated with registered Ko?ta? customers, identified either online or through the company’s loyalty program.

As part of their CX measurement and improvement efforts, Ko?ta? provides post-purchase VOC surveys to its customers via SMS. Responses on about 6% of these surveys generate over 100,000 sentences of open-ended feedback from customers, an immense volume of text comments to sort through and understand manually. But instead of doing it manually, the company uses Alterna CX’s machine learning and text analytics tools to scan through this massive trove of unstructured data. Ebru Darip, the company’s Chief Marketing and Digital Channels Officer at Ko?ta?, says this allows them to “easily identify the root causes for satisfaction and dissatisfaction, and almost in real time.” At a company like Ko?ta?, it means that dissatisfied customers can be contacted immediately, and their problems resolved.

Moreover, according to Darip, this kind of analysis helped fuel Ko?ta?’s “Customer First” program, involving senior management and a broad group of executives in more detailed and impactful efforts to improve the CX. And within less than a year, the company had increased its NPS by a whopping 60%.

If you want to know what it “feels” like to be your customer, you must start by decoding the customer’s many signals, and streamlining that process today requires artificial intelligence and machine learning.?

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Full Disclosure: I am happy to be an advisor to Alterna CX, a company founded and operated by colleagues of mine from Peppers & Rogers Group.