Category Archives: design research

The Most Useful Ways That People are the Same: Better Segmentation

(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.

Learn More: Synthesis is the New Analysis

(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

The Story of Hybrid Insights: Skunkworks in the Attic

(Day 2 of 5 Days, 5 Themes)

“Yeah, I’ve taught survey design.”
“You have? Want to help?”

With that, Johannes and I joined forces on an informal, internal project at IDEO; our goal was to integrate quantitative research methods into the traditionally qualitative landscape of work that the firm is known for. While we saw the tremendous benefit that clients were getting from ethnographic research, we wanted to do more. We started by building smarter surveys.

Surveys aren’t often thought of as a way to inspire design. They are generally pretty dry and written in a kind of robotic language that is appealing to a researcher seeking scientific rigor, but their disconnection with the real person responding to questions means that that person is likely not really paying attention or being thoughtful with their responses.

Surveys also usually focus on opinions and demographics, which are useful for some purposes, but those kinds of responses don’t usually help uncover opportunities for developing new kinds of products or services. We don’t know what people really want or need if we only know what they like and where they live. By asking smarter questions about people’s behaviors, attitudes, and needs, we are able to uncover places to develop truly useful new offerings. Also, by learning from hundreds or thousands of people at a time, our design teams and our clients can all feel more confident about what we’re learning.

As our research approach solidified, our team grew to four. We sequestered a weird attic space at our Palo Alto studio as our headquarters, and set to work codifying our research approach, which had been dubbed Hybrid Insights. We worked in the moments between projects, after work, and on weekends.

The team, hunkered down in a coffee shop in San Francisco, on a Sunday afternoon. From left to right: Alisa Lemberg, Juliette Melton, Johannes Seemann, Silvia Vergani.

Three years later, Hybrid Insights is a core component of many of the projects we do at IDEO. Hybrid projects have happened or are starting in our San Francisco, Palo Alto, Boston, Chicago, Singapore, and Munich studios. Our core team is growing, and our network of collaborators spans the globe.

At IDEO we help our clients innovate; this experience of helping IDEO itself innovate has been remarkable. Whether you call it a lab, or skunkworks, I’ve learned how supporting a small group of people with a vision can yield enormous benefits for organizations and individuals.

Read more about our approach here: Hybrid Insights: Where the Quantitative Meets the Qualitative Rotman Magazine, 2012

Learning to Love Data

To kick off my 5 Days, 5 Themes project, I’ll start with a guiding principle that has shaped my work over the past years.

Whether or not we have the confidence or the pedigree to do so, we all owe it to ourselves (and each other) to get comfortable working with data.

I don’t mean we all need to go back to school to earn degrees in data science. I do mean that if more people had some basic understanding of the process of measuring the world and an awareness of how meaning is derived from those measurements the world would be a little better.

Here’s what I mean: when non-data scientists get involved in the world of numbers, they are able to make their own work richer, because they can tap into new veins of insights. I learned this when I first started working with web analytics many years ago. Just seeing the charts depicting people’s behaviors — the most elementary sort of web analysis — made the conversations I was having with individual community members more meaningful. I began to understand how one person’s story could illuminate the actions of many others, and I could talk to people in person to understand more about the trends I was seeing in the visitor data. For me, it was like how learning another language made travel more enjoyable, allowing me to understand and integrate concepts that otherwise would have been outside my grasp.

Caring about data extends beyond the work we do into the role of data in our lives. That could mean taking a more active stance around such issues as surveillance and net neutrality, or it could mean that you develop an interest in understanding how ad targeting works so that you can choose how much information advertisers have about you. Helping more people be active participants in our increasingly monitored world has the potential to significantly change how organizations and governments collect data and make decisions based off of it.

For inspiration on data literacy and advocacy, two organizations to watch are Data & Society Research Institute and DataKind.

Tune in tomorrow for The Story of Hybrid Insights to learn more about how I’ve learned to love data and how I’ve helped other people love it, too.