What is Information Architecture?
Information Architecture (IA) is the art and science of organising and labelling online environments, be that a website, mobile application, intranet or software system, to aid usability and findability.
We’ve found that the process of devising an IA is often overlooked, when it’s expected the structure won’t change from it’s previous incarnation, or is hurriedly carried out as part of the initial briefing process without consultation with any third parties.
For us, structure is a key part of a user-centred design and the only way to build a user-centred end product is to understand the audience that will interact with it and assist them to achieve their own objectives through using it.
When devising an IA, rather than think “where does this content fit?”, we prefer to consider “where will our audience look for this?”.
For our work with Central College Nottingham we carried out user needs workshops to better understand the array of audiences that use the Central website and the tasks they might be attempting to carry out.
With this knowledge, we devised a navigation structure divided into audience segments, helping the college’s users to quickly grasp where the content they are looking for is likely to be located, without the confusion that previously arose from the sheer volume of content presented at the top level of the website.
The result: A 45% increase in online applications as users found the information they needed to make a decision in one place.
Support for the top-level
A clear and straightforward primary navigation structure alone can make a big difference to the success of a web project, but considerations should also be made for other forms of navigation and wayfinding.
In-page navigation and breadcrumbs not only offer an alternative method of navigation, they help a user to understand their position within an IA which, when you bear in mind that the majority of users on a website do not land on the homepage, can only be beneficial.
On larger sites, navigation can be further supported with integrated search functions that can be constrained by assumed context. That is to say, if a user is carrying out a search while in a specific section of the site we can surface results from that section first.
We know from our experience and the collective analysis of the digital projects we have worked on that no two users are the same, and that the numerous ways users navigate a website can be surprising. Based on this knowledge, another approach for larger sites is to to support the primary audience-based architecture with an alternative, yet simultaneous, architecture based on some other classification scheme.
We’re currently working with the digital team at Nottingham Trent University to not only organise their content based on audience groups but also by the key content themes found throughout the site. This will allow their users to discover related or complimentary content without the need to know it’s place within the primary structure.
Reducing the risk of assumption
Adopting a user-centred approach to creating an IA is a sure-fire way to improve user experience and remove common frustrations, but basing your IA decisions purely on your own preconceptions of the audience is risky.
To mitigate against this, it’s always a good idea to test a candidate IA against groups of potential real users. At Kind, we use Optimal Workshop’s Treejack to set a series of tasks for users and analyse how successful their attempts to complete those tasks would be.
From this analysis we can spot issues with our candidate IA and alter as necessary, removing the risk of costly and confusing changes further down the line.
Creating an IA without visualising how a user will physically navigate it can prove difficult so it’s often useful to start creating some high-level wireframes to illustrate how the IA might work in practice.
This is particularly useful when certain elements of an IA are not defined as individual ‘pages’. For instance when a landing page is broken into sections, we might want to reflect this in the IA but those sections won’t exist as pages themselves.
Again, wherever we’ve made assumptions it is beneficial to test with real users and so for wireframes and prototypes we use Chalkmark, another part of Optimal Workshop’s testing suite, to analyse users’ first clicks when attempting to complete a task.
Using this information we can see how often users set off down the wrong path and make alterations to correct them.
Repeating these testing and analysis processes until we’re happy with the success rate will lead to the most intuitive and effective IA and navigation system(s) we could hope for.
There will always be those who do the opposite of what we expect, whether we’ve carried out user testing or not. But by focusing on the audience to create a structure, and ultimately a finished product, that is made for them we can vastly reduce the number of frustrated users and help both them and your organisation to achieve their goals.