(Day 5 of 5 Days, 5 Themes)

This week I re-read George Saunders’s graduation speech that he delivered at Syracuse University last year. If you haven’t read it yet, please just go do that now. It’s really lovely.

He spoke about the value of kindness: “It’s a little facile, maybe, and certainly hard to implement, but I’d say, as a goal in life, you could do worse than: Try to be kinder.”

As a design principle that holds up pretty well. Try to be kinder. Design research can guide us by helping us see what problems people are having and where we can help.

This week I dove in deep with a set of fairly complex research methodologies. While I enjoy pushing the edges of where we can go as design researchers, on this last day of 5 Days, 5 Themes I wanted to end on a lighter, simpler note: to find new opportunities for design, just be kind. Pay attention to your customers, to your users, to people who could potentially be customers or users. Care about how people are experiencing the world, and notice where you can offer your services to make things better.

The skill of interviewing research participants, the primary arrow in the design researcher’s quiver, is grounded in these simple (but not easy) notions of being kind and paying attention. Start from there and keep going.

Read more:
For some thoughts on attention and research, read this article I wrote for UX Magazine a couple years ago:
Paying Attention: The Most Valuable Skill in UX Research

For a great in-depth exploration of building in-person research skills, read Interviewing Users: How to Uncover Compelling Insights by Steve Portigal

(Day 4 of 5 Days, 5 Themes)

Yesterday I wrote about how I used the Hybrid Insights approach to innovation research to uncover opportunities for the OpenIDEO platform to grow its user engagement. In that essay, I focused on a key finding about revealing the potential for fostering in-person connections among the OpenIDEO community.

In this case, the opportunity for delivering a useful innovation was relatively clear-cut; we learned that most people in the OI community were already connecting in-person to some extent, and once the OpenIDEO team became aware of this, they were able to build on the pre-existing behavior through hosting more meetups and other organized activities.

The community united around a desire to meet each other more often in-person, rather than just online. But in other ways, we could see some significant differences emerging. We saw very different patterns of behavior on the site based on usage data, and knew from user interviews that some people were using the platform as a way to lightly experiment with design thinking, while others were much more deeply involved.

When I’m seeking to uncover useful patterns in a population, I often turn to a segmentation approach. What this means is that I figure out what types of differences and similarities are the most meaningful for the design problem at hand, and use those distinctions to carve up one big group of people into, say, four or five smaller groups who each share a set of similarities. (I will be using the terms groups and segments pretty interchangeably here; bear with me.)

At this point I could have just divided everyone up into groups based on, say, age, or gender, or where they lived. It would have been easy, and would have felt nicely familiar. How often do you hear about “18-34 year old males,” for example? The problem with that approach is that it’s really hard to design for groups like that, since just knowing some basic demographic characteristics about someone doesn’t tell you what their real needs are. You are unlikely to discover something new or feel inspired to create a really novel new offering if you’re just designing for “young people” or “suburban moms” or whatever.

A more useful way to think about groups is: what are the behaviors, needs, or attitudes that unite and divide the population? Knowing what people are doing and needing helps us know how to design for them.

Since we learned through our initial interviews with community members that the impact they wanted to see from the experience was likely to be an important point of difference in terms of how they were using the site, we started from there. People’s attitudes about impact would then serve as the way to determine these groupings.

Here’s how we did it. In a survey that went out to the entire community, we asked “Looking forward, what do you most hope to see from OpenIDEO?” and provided sets of statements that respondents prioritized. (For the the true research nerds out there, we used maximum difference scaling to infer overall prioritization.)

I go into much more depth here, but the short version of the story is that we used a statistical method called two-step cluster analysis to understand how people formed segments based on how they prioritized these statements about what kind of impact they wanted. To make sure that these segments were meaningful, like that they made sense and were different enough to inspire good design, we crossed different types of data — from both the survey and from the registered site actions — against the segments. We then had four groups that we called Browser, Thinker, Maker, and Broadcaster, based on what we knew about what they were doing and what they wanted.

For example, for the Broadcaster segment, we knew that they were motivated by building a global community, and that they prioritized spreading ideas, but they were less likely to submit their own concepts than other segments were, and their primary form of engagement on the site was through “applauding” (the OpenIDEO form of “liking” something). As designers, we could look at this set of characteristics and ask ourselves: “How might we help Broadcasters start meaningful conversations on the platform?” Now we’re at a point where we can really start designing something valuable for this set of users.

This is what I find the most inspiring about approaching research with a human-centered and data-friendly set of tools: we can both support people in ways that matter to them and at the same time have confidence that what we’ve learned is significant across a wider population. By exploring the relevant similarities and differences in our sample, our offerings can be both more inspired and more relevant.

(Day 3 of 5 Days, 5 Themes)

People in 128 countries collaborate on some of the world’s toughest design challenges through OpenIDEO, IDEO’s open innovation platform. The platform is thriving, with new challenges, new sponsors, and a growing focus on in-person collaboration groups.

Step back a year, and the OpenIDEO organization was at an inflection point. They needed to know what their community really needed, in order to make some key strategic decisions. I jumped in to help out, using the Hybrid Insights research approach.

We started by talking to users. Every day we had a Skype call with a different community member, selecting people who ranged from the super engaged power user, to the person who started one “challenge” (the OpenIDEO name for a collaborative project) and then left. In these conversations we learned about what people wanted from OpenIDEO, and what purpose it served in their life. We heard about how it provided an outlet for creativity in an otherwise rigid career; how it served as a career stop-gap between times of employment; how it helped people feel involved in their communities, or connected to others far away.

The richness and nuance of these conversations was impressive, and we could have stopped there. But we wanted to learn the story of everyone’s experience, and not just the people we were able to chat with.

We turned to the data that we had available, which were the recorded actions that registered users could take on the site. So we could know, for example, how many people submitted ideas, “applauded” other users, or evaluated concepts. That told part of the story; we had a nice overall picture of the engagement trends, but that didn’t help us move forward with knowing what to do from a strategy or design perspective.

But knowing what we now did about the kinds of things all of the registered users were doing, and what the interviewed people were experiencing, we could design a really thoughtful and targeted survey to have a conversation with a much broader swath of users.

With survey results, action data, and conversations, we essentially had three data sets that we were able to knit together to unpack the bigger story of what the community was doing, needing, and expecting. Because we had this broader view, for example, we knew that the in-person collaborations that a couple of our interview subjects were experimenting with actually represented a much broader phenomenon, as a surprising 80% of the survey respondents reported doing this.

Because of this research, OpenIDEO started launching and supporting many more in-person events, and the response has been resounding.

We wouldn’t have known to ask a survey question about people meeting in-person to work on an OpenIDEO challenge unless we’d had those conversations with actual users at the very beginning. Without the survey, we wouldn’t have had the data it gave us, since that information wouldn’t have been collected in any other way.

This one aspect of this one study illustrates why we believe in the notion that “synthesis is the new analysis.” Rather than relying strictly on existing data, analyzed in a vacuum, stronger and more useful insights arise when you synthesize, pulling together different qualitative and quantitative input streams.

For OpenIDEO, this meant a more engaged community. For other projects and clients, it’s meant everything from uncovering new uses for iPads to unexpected emotions about dessert. It’s a place where the current world meets the future, and where emergent behaviors can become new offerings.

Read more about our project: Stories and Numbers: How we’re understanding what really matters to you, OpenIDEO Blog