Erin Fillingim
Anthropologie
“Card Sorting Exercise – Summer 2011 – Adjunct Professor Alisan Atvur
This assignment was for us to learn the importance and effects of specific user experience design called card sorting. Card Sorting is a simple technique that many information architects to assess how users categorize given information. This technique is best applied when the variety in the items to be organized is so large that no existing taxonomy is accepted as organizing the items, when the similarities among the items make them difficult to divide clearly into categories, and/or when members of the audience who use the environment may differ how in how they view the similarities among the items and appropriate groupings of those items. In this specific card sorting study, we first were to find the site map of our chosen company, in my case Anthropologie. We then created a card entry for the hierarchy that was listed in Anthropologie’s sitemap. After sharing our study with 10 people, we were able to review and analyze the results.
The participants sorted 110 cards into 6 different categories.
Here is a link to the results of my card sorting study:
The biggest observable trend was in the House & Home category. There was a total of 327 cards from all the participants in this the category. If I eliminate 3 of the categories that were formed by 3 of the participants because they were confused, the House & Home had the highest agreement rating of .62. I believe this trend occurred because home items are easy to distinguish from items such as blouse or wedge, which could easily confuse anyone who is not in tune with the fashion world. An anomaly could easily occur with this category because there was also a Sale category, and several of the House & Home cards were repeated and supposed to fit into the sale category. This was not only a problem for the House & Home category, but several of the other categories as well.
In fact, my second observable trend is that the Sale category had the lowest agreement rating of .18. When I was making this study, I knew this would confuse the users because so many of the items overlapped. For example, the card labeled Denim was supposed to go into the Clothes category, but there was also a Denim and Pants label that was designated to the Sale category. 80% of the participants put this card in the clothes category while only 20% put it in the correct sale category. I assume majority of the users just put both in the clothes category either because they were confused, or didn’t notice there was even a “Sale” category until after they had already put it in the “Clothes” category. Of course, the anomaly in this case would be someone who familiar with shopping on line and knows there is usually a “Sale” section; therefore, they were able to think ahead.
I was able to look at each individual persons results and since all the participants were my friends, I was able to recognize their email addresses and delineate between the boys and girls. There were 5 boys and 5 girls. In looking at the agreement ratings of the boys and then the girls, neither of the two groups ever reached a 1. The highest of the agreements were in the Accessories, Clothes, and House & Home categories. I believe this is because these categories are easy to distinguish amongst the others.
I was actually shocked to find such varying results in the Shoes category. Seems so simple, right? Wrong. The agreement rating was a low .43. Wedges, boots, and flats all seem so obvious to as fashion freak like me that they would fit in the Shoes category. At first I blamed the boys; however, when I looked at all five of their results together, they had an agreement of .54 and the girls scored a .49. The boys were pretty unanimous with putting the separate shoe labels in the right categories. It was odd to find two females to put "flats, shop all shoes, and wedges" into the Clothes category. These two were the anomalies in this case, and honestly not sure why they chose to put only these three into the clothes category.
The second lowest agreement was in the Lounge & Beauty category with a .36. This was expected because several of the Lounge & Beauty labels could have also fit under the Clothes or House & Home categories. For example, the Intimates label, which is supposed to fit under the Lounge and Home category was split 4 ways: 10% into “Accessories”, 60% in “Clothes”, 20% in the correct ‘Lounge and Beauty” and 10% in “Sale”. I can see how this would be confusing because all categories could technically be fitting for “Intimates”.
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