Articulating the Information Architecture (IA) Problems

Glenbow is an art and history museum based in Calgary, Alberta. Glenbow is not only one of Alberta's top tourist attractions, but an organization where people from different communities and different cities gather to exchange thoughts and ideas through Western Canadian art. Upon first glance, the Glenbow website looks appealing and organized. However, some problems arise when trying to use the website. 

Methodology

Client

Card Sorting, Tree Testing, User Interview, Observation

Glenbow Website- Course project

Team & Timeline

User Experience Researchers, 4 Months

Tool Used

Figma, Mural, Office, Optimal Workshop, OnPoint Content Auditor

Information Matters!

What does the existing product fail to address?

  • Some of the categorical and page labels on the Glenbow website’s primary, secondary, and contextual navigation are ineffective in representing their intended content.
  • Some organization systems used within the Glenbow website cause confusion for the users due to their metaphorical scheme usage.
  • The bottom-up organization structure of the Glenbow website lacks in the sense that users may not be able to tell the kind of website and nearby pages they have landed on when reaching the website through its URL.

The case study addressed these gaps?

  • Renamed some categorical and also page labels, and restructure some categories per user feedback and the results of the card sorting study. Created a better and improved IA.
  • Increased usability of Glenbow's website through addressing issues around top-down and bottom-up navigation so that anyone could enter Glenbow's website or land on the third level pages. Recognized this is a museum website and what are nearby pages.
  • Redesigned booking form page.

Let's break down the IA of Glenbow's website...

Three Pillars of IA

To gain a better understanding of Glenbow’s website. First, we covered these context and content topics. We also tested the usability of navigation, organization and structure by representative users to find the problematic parts of the IA.

 My Role

  • Analyzed the content through producing content inventory.
  • Analyzed the existing IA components through visualizing the Top-Down and Bottom-Up approach.
  • Conducted remote usability testing via Zoom and analyzed the data.

From Context to Content

We developed our recommendations based on our understanding of the Glenbow Museum’s goals (Context) and the result of our analysis of the existing content (Content). We also conducted usability-testing, tree-testing, and card-sorting research on Glenbow’s intended audiences to have user-centric solutions (Users). Our recommendations are organized into three categories: Glenbow organization system (scheme and structure), its navigation system, and labeling system.
 

Use of Glenbow’s website

 
Through OnPoint Content auditor and manually I audited 100 pages to create an existing content inventory. 

Existing IA Analysis

I highlighted some of the IA components analyses. Improving the Glenbow search system would be impossible to do without handling the site’s logic; as a result, in this project, we did not focus on improving the search system’s logic and interface.

Organization Scheme

We found that Glenbow representative users were confused about some of the metaphor-driven schemes, such as “Glenbow From Home” and "Add some art to your inbox". Thus, our recommended organization schemes have fewer metaphor-driven schemes to communicate with the users directly, with less ambiguity. We replaced them with audience-specific, task-oriented, and topical schemes. Our suggested schemes are more relevant to Glenbow’s intended audiences and the type of tasks they might want to do on the website, and the topics they might look for on the website. 

 

Organization structure and navigation components are shown in the following analyses.

Click on photos to enlarge. 

Top-Down IA analysis: Based on Information Architecture : For the Web and Beyond” book by Rosenfeld et al. (2015)

Bottom-Up IA analysis: Based on the Keith Instone 's Navigation Stress Test

 My Role

  • Run remote hybrid card sorting research via OptimalWorkshop and think-aloud process via Zoom.
  • Wrote storyboards and user scenarios.
  • Produced a clickable high-fidelity prototype on Figma.

Tree Testing

We collected qualitative and quantitative data to have a better understanding of the intended audiences' mental model. We conducted the tree testing via Optimal Workshop. Based on the results that you can see some parts of them, we found the navigation problems.

Cart Sorting

We identified the problematic labels at the second and third levels of the navigation based on Optimal Workshop's Category, similarity Matrix analyses and the think-aloud results. We developed new labels based on the page content and their main categorization. As a result,  The modifications improved the internal and external labeling system and also the navigation and organization systems.

Synthesized Finding

  • The organizational system, specifically the structure; both the bottom-up and top-down structures need some modifications.
  • The navigation system is somewhat inaccessible. Currently, there is an embedded local navigation system in place, however, it is invisible to the user once they are on any page. This makes the local navigation system less accessible and creates confusion for the users as they do not have access to nearby and related pages, and instead have to rely on contextual links or a top-down approach for locating information. On top of that original Glenbow local navigation blocks the full page, which was criticized by usability testing participants.
  • The labeling system such as headings like About and Learn, which creates confusion for the users as they may incorrectly think both headings refer to Glenbow’s organizational role.

IA Schematic Diagram- Sitemap

Click on photos to enlarge.

Original IA Schematic Diagram

New IA Schematic Diagram

  • Navigation Element does not lead to any page on the website.

  • Anchor Element leads to a different page on the website. 

To illustrate the effectiveness and efficiency of the new IA we created user scenarios, storyboards, and low and medium fidelity prototypes that were based on identified intended audiences. The prototypes demonstrate our recommendation for the new IA and will address the areas of concern that were previously determined through our usability testing, tree testing, and card sorting study.
 

One sample of our user scenario & high-fidelity prototype

 
image.jpg
“My name is Jacob. I was born in Toronto. I am a 35-year-old professional artist (calligrapher). I will travel to Calgary next month, the Glenbow Museum is one of the tourist attractions I want to visit. I decided to purchase the museum's tickets in advance on the Glenbow website.”
 
 

We recommended some changes to help Glenbow’s users (Jacob) move around the website to find the information they need or perform a task they want.

Let’s see how I improved Jacob’s experience in purchasing a ticket on a high-fidelity prototype of the Glenbow website.
  • Added a new mega-menu that does not block the whole page.
  • Added a highlighted photo of an important link or news to the mega-menu.
  • Redesigned the booking form and checkout page.
  • Added breadcrumbs so that Jacob can move around the website easier.
  • Horizontally aligned the social media icons on the global footer.
  • Added an interactive map view of the address.
  • Added a new category “Support” for a better structure of the content.

Next Step

Iterative testing is the next step of this project. I should conduct usability testing to evaluate the modified IA of Glenbow's website.

Lessons Learned

People have a wide variety of thoughts and perspectives towards the content around them. Consequently, this makes it challenging to architect a piece of content to inform different users. For this reason, I learned that identifying intended audiences and knowing their capacity and cultures play big roles in users' experience. Not only that but also testing the usability of the IA by the identified intended audiences to justify that the IA or changes are engaging, satisfying, effective, and efficient.