Learning how to go from research to design insight.
Last week I conducted my first set of user interviews for a new project in my user experience graduate program. I felt the interviews went smoothly. I interviewed five people, asked them to complete a simple task online and recorded the results. I recorded the interviews using a screen recorder. It wasn’t until I began reviewing the video of the interviews that I realized what I was up against. Where do I start, what is important? I wish I had slowed down here, why did I not ask them about that choice? Feeling slightly overwhelmed, I got some coffee and I set out to define a system to transcribe the interviews on to hundreds of colored post it notes. Okay, I’m feeling a little better, things are taking shape. My next hurdle revealed itself as I peeled and stacked post it notes, What should I do with all this data? How do I turn it into something meaningful? Now, there are a myriad of tools, in an experienced user experience designer’s toolbox, that they can use to extract meaning and interpret the data. Personas, card sorts, mental models, user scenarios, or usability tests to name a few. Knowing the right ones to use comes with experience, and even then its a daunting task to transform raw data into insights that you can use to inform user experience.
Luckily for me (or maybe not) my tool was predetermined. We were assigned with the task of creating an affinity diagram as the lens through which to view our data. It wasn’t until I had all of the post-its laid out on the room sized white board that I realized how much data was collected. Good, I think to myself, I can actually see it now. But, what do the rows of pink, yellow and blue squares mean? What do I care about that is in this data? What can it tell me? My partner in this endeavor and I begin to read off and then group together our post-its, clusters form, and the once separate data from each interview starts to form a new picture. But still, what does it mean, what is it telling me? All of these people were asked to complete the same task, and yet they all completed it differently. The clusters were telling us that there are similarities. Maybe everyone did not take the same path, but they did have similarities in the choices they made. Some used the same search methods, others made purchases from the same websites.
Maybe I was asking myself the wrong question. Maybe I was not trying to see the forest through the trees, maybe what I was trying to find out was, why trees? It was not until I began to notice the underlying motivation for these similar choices that I realized what I was looking for. Their choices were based on their best efforts to accomplish the task, and each person had a different base knowledge of where to start and their next steps to get them to the end. Some took a really long time to get where they wanted to go, others used different logic and had more familiarity with the task or websites. So, maybe this is where my insight lies. How do I remove that obstacle, how do I level the playing field. Is there a way for me to inform them through design? Is there something I can do to increase their chances of having the same starting point, so they do not have to rely so heavily on their past experience. …Light bulb!
