The other day, I was in the car with a friend, and put on a playlist I’d created specifically to cheer me up. It was working; my bad mood had dissipated, and I was thoroughly enjoying our ride down the highway, when my friend turned to me and said, “hey, every song on this list is sad. I don’t understand how this makes you feel better.”
I was shocked. See, in making the playlist, I’d added songs loosely based on one essential assumption about music: any song with a full chorus, orchestra, or big band in it will make anyone feel better. Sure, there were outliers (songs I just considered great and added to the playlist, songs I’d mistakenly added, etc.), but for the most part, I’d categorized the music by this one specific common attribute.
In doing so, I’d failed to take into account any other possible categorization of the songs, which led to my friend’s (and presumably, many other potential listeners’) confusion and misinterpretation.
So, how could I have prevented this? If it had been my goal, how could I have created a collection of music that did cheer up others, and not just myself? Now say that playlist were a website. How could I have designed it to be used in the way I intended? How could I avoid confusion or misinterpretation by designing the site differently?
The answer lies in categorization. Categorization is an essential human function and one that our brain does without us even knowing it. So let’s take a closer look at the way my friend categorized the songs on my playlist. In saying the songs were “sad,” what did he mean? We call different methods of categorizing content classification schemes. In sorting my music based on the types of instruments or vocal techniques used, I categorized the music by attribute.
My friend, on the other hand, did something very common. He listened to a song about a break-up, or loss, or death, and categorized that song as “sad.” In this way, he categorized the music topically, which is the most common strategy humans employ in any classification project.
Unfortunately, when organizing the playlist myself, I got so caught up in the way I interpreted the music that I failed to take into account any outside perspectives. This led to me making flawed assumptions about the ways other would interpret the playlist because I did no research to confirm or dispel the idea that others would interpret it the same way I did.
I’m sure at this point, you’re wondering what this has to do with market and UX research, and why it exists on Discida’s website. Well, it’s simple, really—I made a mistake many of our clients can’t afford to make. In the real world, clients need to minimize misinterpretation and confusion about their products and websites in order to sell their products, and categorization is at the heart of website design and Information Architecture. If we expect the general public or a specific target persona to understand how to use our clients’ websites, we must first dig into the way we all innately categorize the content on these websites. Where do our systems of categorization match consumers’? Where do they differ? How does one consumer differ from another, and how can we find enough common ground to create an easy-to-use, understandable, and intuitive website?
These are all questions we ask ourselves at Discida, particularly when conducting card sorting or tree testing research projects. Through card sorting, we can help design or re-design existing IA by having participants sort website content into categories. In tree testing, we do the reverse and test how accurately participants sort pre-existing IA. Often, both are done hand-in-hand to ensure the IA of any client’s website is truly understandable before its launch.
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Also published on Medium.