{"id":7618,"date":"2020-12-03T07:00:00","date_gmt":"2020-12-03T13:00:00","guid":{"rendered":"https:\/\/juneux.com\/blog\/?p=7618"},"modified":"2020-12-03T08:40:38","modified_gmt":"2020-12-03T14:40:38","slug":"how-to-conduct-an-open-card-sort-analysis-using-miro","status":"publish","type":"post","link":"https:\/\/juneux.com\/blog\/2020\/12\/how-to-conduct-an-open-card-sort-analysis-using-miro\/","title":{"rendered":"How to Conduct an Open Card Sort Analysis Using Miro"},"content":{"rendered":"\n

Open card sort analysis is part science, part art. The raw data will tell you generally how people group and organize items, but luckily, UXers have a little creative license when writing the exact right label for each category.<\/p>\n\n\n\n

If you\u2019re a visual learner or want to experiment with a different way (other than Excel) to analyze an open card sort\u2026 onward!<\/p>\n\n\n\n

\"Cluster<\/figure>\n\n\n\n\n\n\n\n

I\u2019m always experimenting with new methods and techniques, and so was excited to conduct an open card sort analysis using a free app I recently discovered in Miro, called \u201cClusterizer.\u201d <\/p>\n\n\n\n

To add this app, click the ellipses icon in the menu, then the blue + icon (in the upper right corner). Search for and install Clusterizer to your Miro account.<\/p>\n\n\n\n

\"Clusterize<\/figure><\/div>\n\n\n\n

Step 1: Copy and Paste<\/h2>\n\n\n\n

First, I opened up my card sort results in Excel and conducted some minor hygiene (i.e., deleted unsorted cards).<\/p>\n\n\n\n

After I finished cleaning the data, I copied and pasted the cards and their respective user-generated labels into Miro. Hint:<\/em> When you copy cells from a spreadsheet into Miro, each cell automagically creates a sticky note. This same shortcut works in Mural, too.<\/p>\n\n\n\n

Next, I color-coded the cards (in yellow) and categories (in blue), and then put them into their respective groups. Each group represents a unique card.<\/p>\n\n\n\n

\"Groups<\/figure>\n\n\n\n

Step 2: Look for Patterns<\/h2>\n\n\n\n

To begin looking for patterns, I created simple affinity maps within each group. I looked for \u201cexact\u201d or \u201cclose\u201d matches and put each unique sub-group of sticky notes in its own row.  <\/p>\n\n\n\n

For me, an example of a close<\/em> match might be \u201celectrical appliances\u201d and “electric appliances.” I’m not making judgments yet about what the final labels should be. To remain open-minded, I use stricter rules for my groupings in this initial phase. <\/p>\n\n\n\n

Above, you can see rows of blue sticky notes. Each row includes only close<\/em> and exact<\/em> match labels. The green sticky notes are outliers.<\/p>\n\n\n\n

Step 3: Look for Broader Patterns<\/h2>\n\n\n\n

Satisfied with my first round of affinity maps (i.e., blue rows), next, I completed a second pass by drawing a circle around similar sub-groups. Now<\/em>, I’m starting to make subjective judgments about which groups are closer or further in similarity. My goal is to create distinct themes.<\/p>\n\n\n\n

For example, I might now draw a circle around \u201celectrical appliances\u201d and \u201csmall appliances,” and combine them into a larger group called \u201csmall appliances.\u201d<\/p>\n\n\n\n

Hot tip: I recommend consulting with an SEO specialist at this point in the analysis. They are a tremendous resource who can help weigh in on which label option is better (and why).<\/p>\n\n\n\n

Step 4: Add Labels to Cards<\/h2>\n\n\n\n

To do its magic, the Clusterizer app requires the use of the \u201ctags\u201d feature in Miro. So next, I added color-coded tags to each card. <\/p>\n\n\n\n

\"Add<\/figure>\n\n\n\n

Note: There are only 12 possible tag colors in Miro. You can add more than 12 tags as long as you don’t mind duplicate colors. For me, it wasn\u2019t a big deal that some tags shared the same color-code.<\/p>\n\n\n\n

Below, research participants are telling me they would find toasters under “housewares” as well as “small appliances.” I don’t know which category is best yet, so I add both tags.<\/p>\n\n\n\n

\"Color-code<\/figure>\n\n\n\n

Once I’m finished tagging everything, now my sticky notes look like this: <\/p>\n\n\n\n

\"Tagged<\/figure>\n\n\n\n

Step 5: Clusterize<\/h2>\n\n\n\n

Finally, I selected all yellow sticky notes at once (but not the blue or green sticky notes), clicked the \u201cClusterizer\u201d app icon, and then the \u201cBuild\u201d button. Within a few seconds, my colorful cluster analysis appeared. I dragged it over to a blank area on my Miro board, and then moved the individual cards and circles around until I was satisfied with the visual presentation.<\/p>\n\n\n\n

Here, I can easily see that people tended to group more non-electrical kitchen items under \u201cHousewares\u201d and fewer electrical kitchen items under \u201cSmall Appliances.\u201d Sure, I probably could\u2019ve reached the same conclusion conducting the analysis in Excel, but wasn\u2019t this way cooler?<\/p>\n\n\n\n

(For confidentiality and illustrative purposes, I didn’t really<\/em> conduct an open card sort analysis about housewares and small appliances. It was about something else.)<\/p>\n\n\n\n

\"Cluster<\/figure>\n\n\n\n

Have you tried the Clusterizer app yet? What did you use it for? Please tweet me at @KristineRemer<\/a>, I’d love to learn about other use cases. <\/p>\n\n\n\n

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