Using card sorts with storytelling to get off to a great start

Working with non-profits often means large teams with multiple stakeholders, each with a list of desired features and improvements that may have been in the works for years. The expectation level is high – funding has finally been approved, a target launch date planned and everyone knows just what they want – in fact, a site map has been developed. While each stakeholder is trying to address the perceived needs of their audience, when it all gets put together the results can be unprioritized and sprawling. The team may have been meeting for months and are looking for the designers to pick up the ball and run. However, committee-driven IA can be so democratic that it can miss providing the information that site visitors are looking for in the way they expect to find it. Our favorite path to a successful project with multiple stakeholders is holding card sort/participatory design sessions with storytelling as soon as possible.

Traditional card sort sessions involve gathering data from participants by having them sort terms into groups that make the most sense to them. These sessions are a valuable tool for creating navigation that makes sense to the people who will use the site. A large numbers of participants (>30) is needed to provide the data, and software to sort the responses is helpful. While traditional sorts are not difficult to do, we have found that having sessions with fewer participants (5-12) and mixing approaches provides input that covers several areas beyond defining appropriate navigation categories.

When sessions include prompting participants to tell stories about a specific time they used a site, perform a card sort, and use cards to position content and features on a page the way they'd like to see it the data is deep and rich. The results provide a clear picture of each person's mindset, and how that mindset affects how they think about content, categories and what they value. Patterns emerge that point to pretty clear user group needs. While it doesn't provide deep statistical data, it does provide rich scenarios of use and an understanding of needs and values that is just what we need to get buy in from stakeholders to prioritize the feature list and focus the IA on the top issues and most common situations.