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Improving the way the information in your site or application is organized and presented is one of the most cost-effective ways of increasing user satisfaction and engagement. Information architecture can help you find out how your users think about the world, and transition those lessons to your product. In this course, Chris Nodder teaches you how to perform card sort research to get information about user interactions, analyze the results, and create a validated information architecture plan. Then translate your plan into refined menus, content classification, and page layouts. Finally, test the success of your new structure with reverse card sorting and by monitoring feedback from server logs, site searches, and help desk calls.
Before we roll our sleeves up and design our site's menus and other components, we need the abstract information architecture. To make this abstract information architecture, we take all of our observations, along with a cluster analysis information and create our best impression of the ideal structure for that information, regardless of how it will be used. Items that participants grouped most clearly will form the basic lower level structure. For instance, one grouping consisted of minimal impact flower borders, native landscaping, yard plants, and plants that would require no extra water.
We'll form several search groupings. Then, the lower level groupings that have the most in common, in participants minds, form the categories above this lower level structure. Names that participants gave to the groupings might suggest category headings. You probably won't choose to use the exact names that participants wrote down, but their terminology and the thinking behind their choices will inform your final decisions. If you created standardized names, those might be closer to the final names you'll plan on using. Names that people gave to this group were outdoor plants or live plants, but all of these items describe ecologically sound outdoor plants which is probably a more descriptive category.
Your abstract information architecture hierarchy should emerge from this data. I have to say there's as much art to this as there is science. Luckily, because we'll be verifying our assumptions by running a reserve card sort later, it's okay that this isn't the most rigorous process. So long as you make sure you're always working form the data that you have from your analysis tools or spreadsheet you should be fine. A good information architecture structure will provide a balance between breadth and depth.
Too many top level categories might make it hard for customers to pick the right option. Too few might require them to drill down through too many levels. It will also provide good sign posts so that people know where they are at each point in the architecture based on the category and sub-category labels. The right information architecture also means showing items in the correct order. That means making sure that sub-category items are arranged in the order that people expect or that information can be sorted and filtered based on a set of criteria you've derived from user research.
The arrangement will depend upon the type of data being displayed and how your users think about it. There are several options. Importance based puts the most important items first. Frequency or recency based are the most common or recent items. Topic based is used to describe content. Task based to show available actions. And time based, depending upon when an event occurred, for instance, in your web history. Product based arrangements are split by feature set.
This is a special type of topic based classification. Location based could be geographic location, by stockroom shelf, or by office number. Audience based could be categorized by employee information or customer information. Series based are things such as shoe or clothing sizes. And alpha, or numerical, are based on the quality of the label of the item, like, book authors. Across your site you might use several different orders. The order of top level menus might be frequency based so that the most common appear first or they might be a built-in arrangement dictated by the type of task being performed on the site.
Lower level menus could be sorted pretty much any way. Again, depending upon the type of data they contain. Homepage content might show the most popular items, which is based on frequency of purchase. Often these days, footer areas contain the list of product categories and on corporate sites, they might also have content for different audiences, such as the press, investors, and job seekers. So you can see how your categorizatoin decisions are driven not just by the groupings the particpants choose, but also the types of use that you expect them to have for the items in those gropuings.
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