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Cognitive Biases and the Customer Experience

How biased is your impression about the experiences your customers are having?
Before you answer with any certainty, you might want to watch this:

Be honest. Did you notice the gorilla the first time around?
If not, you definitely aren’t alone. When a 1999 study by Harvard’s Psychology department showed this video to a group of test subjects, only half noticed the gorilla walk through the middle of the crowd.
And if you find that surprising, you should see what happened when the stakes were raised. When a 2010 Harvard Medical School study challenged a group of doctors to examine several x-ray images for signs of cancer, only 17 percent noticed the inch-high gorilla picture that had been pasted onto each image. These doctors were trained to notice the smallest abnormalities in a medical scan — yet most of them overlooked the large one that was staring them right in the face.
So what’s the reason for these gaps in attention? And what does it have to do with customer experience?
The first answer lies in a common psychological phenomenon called ‘inattentional blindness,’ or the failure to notice an unexpected stimulus. Put more simply: we often overlook things we’re not expecting to see. We’ve all experienced the phenomenon in our personal lives, whether it’s hunting for a wallet that’s already in our pocket or looking for a pair of glasses we’re already wearing. But as the above studies show, our expectations can also cause us to miss glaring inconsistencies in the work we do.
This can even happen when you’re working with customer feedback.
Text analytics engines offer an example. These tools are often used to identify important trends and topics in customers’ written comments, whether on social media, in online reviews, or in surveys. While quantitative survey questions ask about the issues your company believes to be important, written comments often reveal issues you wouldn’t have thought to ask about. And with massive amounts of this unstructured feedback now available online, text analytics tools can help companies quickly identify unexpected issues without having to sort through every comment by hand.
But there’s a problem. Historically, text analytics engines have come with a limitation: they require you to choose which concepts and keywords they will look for in customer comments. This is an opportunity for your prior expectations to muddy the water. Your customers’ needs and problems are always changing — how can you be expected to set search parameters that surface topics you aren’t yet aware of?
Try as you might to be all-encompassing, inattentional blindness will make it difficult to know what you’re overlooking.
It’s clearly important to keep track of the emerging and unexpected issues your customers are telling you about. We’ve just released a next-generation text analytics engine to help you do so. It uses machine learning to automatically identify patterns and concepts in large amounts of written text — without relying on human input. If you’d like to learn more about it, check out the announcement here.
And whenever you’re listening and talking to your customers, keep an eye out for ‘gorillas’ hiding in plain sight.

Photo credit: Jason Rosenberg


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