Throughout this article, I will outline the key principles of IA, discuss key information-seeking theories, looking at methods of IA research and testing techniques, as well as the importance of labelling within IA.
What is information architecture?
Information architecture, or IA, is the structural design of information systems and interactive products and services. IA helps users to discover and find information using prompts, labels, language and context to help make their journey as simple as possible.
IA, in principle, has been around for as long as humans have been able to organise; however, it wasn’t until 1975 that the phrase “Information Architect” was officially coined by the architect Richard Saul Wurman. Wurman was famously driven by “his desire to know, rather than from already knowing”, and his focus on information architecture through a lens of ignorance and inability rather than intelligence has shaped how we see IA today. It’s the framework which enables a user to find the information they need without already knowing where to find it, in the most efficient and intuitive way.
Within digital design, when people refer to IA, they are most commonly describing a product, platform or website’s central navigation. This navigation is the first point of contact a user has with a product, and it needs to provide a seamless journey to the information they need.
Findability and discoverability
The way that we think about information architecture is often in terms of how you can increase the findability of something. However, it is also important to think about discoverability, which is when a user is looking to find something new or may not know quite what they are looking for yet. By opening up these two user paths and looking in more detail at the possible journeys that your user could be taking while navigating your IA, you will be able to create a structure that can support each of their needs.
“Scent theory” or “Information foraging theory” is a concept used to help us understand users’ behaviour when seeking, hunting or foraging for information. Simply put, the theory is that users are like wild animals: they blindly follow a scent while hunting for the information they are looking for. As they are following the scent, there are certain key attributes that they pick up on along the way, which assist them with their search. These can vary from labels, wording, content, clues and context that can all promote recognition and recall within the user, which they can apply to their search.
Good IA will successfully decrease users’ “cognitive load” by reducing the amount of recall they have to do when finding information. It is key to promote recognition via the labels, context and imagery rather than expecting the user to recall knowledge from a previous journey. This might sound familiar as it is Jakob Nielsen’s usability heuristic number 6, which is “Recognition rather than recall”, a key principle throughout interaction design.
The evolving search: berry picking
We have touched on the importance of findability and discoverability and this can be further broken down into three seeking journeys: Known item seeking, exploratory search and re-finding.
Each seeking journey has its own nuances; for example, an exploratory search may need specific terminology, in which case there could be a phase of “key word foraging” which precedes the search. A re-finding journey, on the other hand, is often supported by tools like recent search recommendations and wishlists.
There is no one straightforward journey when a user is seeking information; it is, more often than not, an evolving search. The evolving search, or “berry picking” as it is sometimes called, describes the way users move between query variation, gathering information and analysis before repeating the process, collecting these insights to assist them in finding the information they are seeking.
Testing and research methods
There are two primary methods that are used to test and build your IA and these are “Tree Testing” and “Card Sorting”. They both focus on different aspects of IA and have their own benefits and limitations as methods.
Tree testing is a user research technique that tests the findability of content within a platform’s IA. A user is given the task of finding a specific area, or category, within an existing IA structure, and their journey is tracked to see how and if they reach their destination.
Tree testing is most useful if you have mutiple IA options that you want to test against each other. It can provide key quantitative and qualitative information on grouping, hierarchy and labelling. It is worth noting that tree testing should only be used to look at viable IA options otherwise, it is a waste of UX researchers’ time and resources.
There are several limitations to be aware of when tree testing. Firstly, the testing usually requires users to select a specific “leaf” as their destination, meaning users will keep going through a site’s IA until they find an endpoint, but that might not accurately reflect where they would actually stop or drop off.
Secondly, as we have already discussed, there are many contributing factors, or contextual clues, that users use when they are searching for information and in tree testing these are ignored as it is focused on the structure and labelling only. Finally, it can be hard to identify the cause of an issue, whether it is structure or label related. This can be solved by holding moderated tree testing where you are able to gain much more information as the user talks you through their decisions and pain points as they navigate the IA.
A card sort is a popular UX research technique that helps you sort and organise content in a way that is best suited to your users’ needs and expectations.
Card sorting is synonymous with IA, but this doesn’t mean that you should conduct a card sort for the sake of it. It is a great tool that should be used when appropriate, which should be determined on a case-by-case basis. Some examples of when this technique works best are: when you are building a site from scratch, conducting a complete IA redesign; if there isn’t a strong understanding of the data you are working on and its grouping; or if you’re looking in detail at a very specific section of an IA structure.
Card sorts can provide invaluable information on how your audience expects to find information and how things can be grouped together in the most user-friendly way. As mentioned with tree testing, and indeed with any kind of UX research, it is always best to hold moderated card sorts to get the maximum insights from the participants.
Another key area of information architecture is labelling. It is an important part of building up the scent that users are following when they seek information. An unfamiliar label or vague category title can end in a user dropping off a page completely.
When developing labels, they should be as concise and as familiar as possible. The results from a card sort and search log analysis can help to determine groupings and naming. It is also important to conduct a competitive analysis to find any standardised terminology that can be used as this will promote recognition for the user and help to lighten their cognitive load.
As we know, IA is a key part of UX design and is integral to the success of a website, product or mobile app. A strong IA means a good user experience, which ultimately means lower bounce rates, repeat customers who keep coming back, and a better product.
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