Picking out an actual “guiding principle” from a transcript is difficult. A guiding principle is a sub-conscious philosophy that guides how a person makes a decision. When you look at a transcript, the words “I believe,” “I think,” or “my philosophy” sometimes trick you into thinking┬áthat you’re looking at a guiding principle. But this is not always the case.┬áHere is a perfect example. It’s a page from an architecture firm’s web brochure. The page is titled “Our philosophy.”Philosophy statement of the architecture firm Philiippe Timmerman Read More

audience segments as characters

I was recently helping a few people create audience segments for their projects. It’s so hard to get outside the normal way of thinking about people by title or role or demographic. As a way of getting past that, and additionally as a way of emphasizing that audience segments are merely a way to help you talk to a wider swath of people than you might get simply by selecting by role, I suggested thinking of each segment as a character. Like, you know, “Oh, Mike–he’s a character!” Someone who is larger than life will help you look around the edges of the role-defined world. (One of the people I was helping works in an industry where some of the workforce they are studying is referred to as “roughnecks,” which just begs for the creation of characters!) Furthermore, thinking of these segments as characters in a movie will help you slip away from generalizations and focus on a particular set of extraordinary fictional personalities. Read More

This panel discussion took place during the Interaction Design Conference 2010 in Savannah, Georgia, USA. (IxD10) It was not on the program, but filmed separately as a part of The UX Workshop.tv series, sponsored by mad*pow. Design Research Discussion panelists include: Indi Young, Daniel Szuc, Eric Reiss, Steve Baty, and Chris Avore.

examples of tough combing labels

You wanted to test your combing/labeling skills … right? You wanted to hone your ability to grab the most descriptive verb possible, and pull out the implications of what the person is really trying to say? Here is a set of examples with a little discourse about why I suggest the label I suggest. The original labels have been suggested by people I am mentoring through the combing process.

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support intentions, not existing workflows

This week I was chatting with someone who works at an organization that does not yet recognize the value of generative research before defining products. She said to me, with exasperation in her voice, “The product managers here still go around collecting needs from our customers and giving us lists of features to implement.” She had some money left over from a budget (Leftover money?! That doesn’t happen often!) and wanted to spend it on a small research project that would get to the root of what people were trying to do–people who were not yet customers. Her dream is to be able to show the product managers and executives at her company results from the generative research illuminating several new, previously uncharted activities that her company can support. Read More

mental model of australian public radio/TV contributor

Jeremy Yuille has been working with the Australian Broadcasting Corporation (ABC) on a project to give viewers and listeners around the country a chance to upload their own content. Why would people upload their own content? The most cited reason for doing such a thing is, “it’s a place to display my work,” followed by “give my work a chance of being used by the ABC” and “get recognition from the ABC.” So Jeremy and his team (Chris Marmo, Reuben Stanton, Marius Foley) put the mental model together of various types of contributors, analyzed it in terms of how to support them, and created a site. Best of all, the team is sharing their work with the world.

mental model diagram with mental spaces shown as "platforms" supporting towers, which support boxes above them
 

Jeremy has generously shared the diagram with us here. (135Kb PDF).
Note how Jeremy, being a communications designer, reformatted the diagram to show mental spaces and towers as foundations to the boxes, rather than as headers. Jeremy blogged the details of this project here.

Team Twitter handles:
Jeremy Yuille @overlobe
Chris Marmo @kurisu
Reuben Stanton @absent

conversation instead of an interview

I’ve been guiding the fabulous folks at the University of Buffalo (and the team at their design partner mStoner) through the interviewing process this week. One of the university stakeholders for the project wanted to be interviewed as a participant–as someone who keeps track of what an organization is doing and crafts his decisions based on what he learns. As expected, the interview kept bouncing back to what this stakeholder does at the university, rather than branching out to similar habits he might have in other circumstances. When I tried to explore how he tracked information about other organizations than the university, he was surprised and said he wasn’t prepared to talk about other topics. It was disappointing because the other topic we glimpsed was unique to him, and I could sense that we would have been able to go deeper into what was motivating him. Read More

updated python script to generate the mental model diagram

The Python script used to generate mental model diagrams has been updated to allow for added flexibility in output. Originally, box labels greater than about 48 characters would render outside the margins of the box, requiring close monitoring of the label length and often caused the label to be rewritten awkwardly or with abbreviations. Read More

granularity & repetition of task labels

In the first five months of 2009, I’ve guided four teams through making their mental models. We have combed transcripts, labeled quotes, and grouped the labels from the bottom up to create the structure of the mental models. We have made 11 different models together. What has come up again and again is the difficulty of choosing what to include in the model and what to exclude. I see two recurring prominent mistakes. Avoiding these mistakes will greatly simplify the complexity of piecing together the data.

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go the more nutritious route

At nearly every presentation and workshop I give, someone comes up to me and asks, “Have you tried (insert tool name here) with your method? It’s really cool.” I shake my head no and politely ask them what they think the tool would do. Every explanation boils down to this: it would automate the analysis of all those interviews, make affinity groups, and do away with all that manual work. I ask them to email me the tool name, then I file it away untouched. I always thought this was just a personal quirk of mine–that I want to do the analysis myself. I don’t want to use a tool to comb through transcripts for me because I’m the one who is reading between the lines and guessing at implied meanings.

I finally realize this is not a personal quirk. Manually analyzing the transcripts is a requirement. Read More