Audience-based IA vs. Findability

The subject of information architectures based on audience types has cropped up around the office a lot lately. A number of our clients are talking about it. It is a popular approach, one that seems to logically support user-centered design principles. But it is an approach that can reduce the findability success rate of a site and disrupt the user experience.

Before I launch into why audience-based information architecture can be problematic and a little backstory, I will clarify what I mean by audience-based IA. Universities are frequent, and I should add, some of the most successful users of audience-based information architectures. I’ve seen approaches to audience-based segmentation on university websites, the more effective ones look something like this: students, faculty & staff, alumni, parents, employers, media. In this case it is easy for the user to  identify what audience he or she belongs to. What is more challenging under an audience-based system then is to guide that user to the appropriate program and campus information and from there to determining what specific courses to take. That is because audience-based IAs are affected by four key problem areas:

1. It is can be hard for users to clearly identify with what audience they belong to because while we may have neatly segmented and personified the audience, real people are messier than that  and usually think of themselves from multiple perspectives.
2. Because of issue #1 you wind up repeating and having to maintain key topics in multiple channels or bouncing users out of one channel to another creating either a repetitive or a disjointed user experience
3. The audience-based classification is very difficult to maintain past the first level and falls apart quickly and turns into a topic-based information architecture. You wind up creating micro-sites for each audience type which or course is problematic if your audience is affected by issue #1 and #2.

A few months ago we were working on the information architecture for a corporate intranet. After user research and requirements gathering, we started in the same place that most people do when creating the IA for an intranet, with a system based on well-understood and well-defined audience segments.

The cool thing about this project was that we tested three iterations of the information architecture. After the fist round of testing we found that users could not easily self-identify with the segments. The audience segments were well understood by the users and they did apply, the issue was that a users did not fall into only one segment at a time. For some issues users may consider themselves to be in one segment, for others another segment, and then for yet other issues users could conceivably self-identify as being in more than one segment at the same time.

We used a navigation testing tool we developed in house (coming soon to Beta) that clearly showed where users were going astray with the proposed navigation structure. The IA on the project, Jessica Dill, proposed that we wipe out the top layer (the audience-based segmentation) and simply drive users straight to the topics of interest regardless of the audience type. We tested the new topic-based information architecture with 14 and then 16 main categories. Both tests performed far better than the audience-based IA. In each case the findability success rate was over 90%, compared to a the audience-based IA with a rate of about 40% to 50%.

This is not to say that audience-based information architecture can never be successful.  It’s just that audience-based IA comes with some pitfalls that either need to be avoided or a different approach to the IA should be considered. Further, given the pitfalls, and counter to intuition on this, audience-based information architectures run the risk of reducing findability.