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A Practical Guide to Creating & Using Personas for the Web
Question or comment? authors@practicalpersonas.com
PersonaBuilder is a web-based tool built by Tim Smalley that encourages effective creation of personas. It's a very smart interface that helps team members collaborate and provides a shared repository for user research notes and of course the personas themselves. Definitely check it out.
Here's a new personas blog by Angela Quail with an excellent idea: When we deliver personas, we should label them with just how much research rigor went into their creation. Many people realize that personas are at least partially "made up." The question that often arises is this: How much of the persona is invented vs. grounded in research? What parts of the persona can I trust for decision making, and what parts are just for flavor?
I like the idea of a scale or some kind of label to let people know what level of research went into each persona. Of course, I see a core distinction between qualitative personas and quantitative personas. Perhaps one metric behind any persona should be the number of actual users that shaped this persona (in interviews, field studies, surveys, etc.).
On Boxes and Arrows, Andrea Wiggins wrote an article a couple of months ago that I somehow missed on using Web analytics data during the persona creation process. In her example, a designer uses Google Analytics to provide real behavioral data for each of the segments or personas that she hardwires into the analytics tool. I absolutely agree with Andrea that behavioral data is critical to a well-rounded portrait of users. It's not enough to just talk with users and hear what they think - watching them (whether qualitatively through field studies or usability testing or quantitatively through Web analytics) is equally important. I like how she extracts data from Web analytics reports to make the personas more real.
The challenge is that the quantitative data could be placed on top of incorrect qualitative personas right from the start. If the personas you invent aren't right, no amount of data on top will help. That's why I'm a fan of the quantitative persona creation process, where data from surveys and Web analytics gets used via statistical analysis to generate the persona segmentation in the first place.
Make sure to read the good discussion after her article.
As a follow-up to a virtual seminar I did a while back, Jared Spool interviewed me for a podcast, which has now been excerpted as part of the UIEtips newsletter. Check out "Making Personas Work for Your Web Site: An Interview with Steve Mulder."
What do stereo equalizers have to do with persona segmentation? Check out Patrick Kennedy's post on diagrams to help lock in persona groupings, entitled "Squiggles help find personas." What I like about this is that it not only helps you establish and test segmentation in the first place, it also effectively communicates the process to others in a familiar visualization.
The very smart Todd Zaki Warfel ran a workshop on data-driven personas a couple of weeks ago, and I wish I could have attended. He wrote up a summary of the event, with a discussion on qualitative vs. quantitative data. He also includes his slides.
I agree with Todd that qualitative inputs are critical to persona creation. Many organizations get stuck on the quantitative side of research. But without qualitative research, data has no context or flavor, and flavor is everything when it comes to making personas feel like real people.
At E-Marketing Performance, Stoney deGeyter has some good thoughts on fundamental personality types that can be applied to personas. While I'm always skeptical of "generic" personas that can be used for any product or site, these personality types seem like they could be added to personas effectively.
Ziv and I wrote an article recently on "What Brand Attributes Make a Difference," covering how to use a survey to discover which brand attributes are most important to focus on. All of us talk a lot about key customer drivers (reliability, price, speed, blah blah) and brand perceptions (Company X is professional, innovative, blah blah) and we all know they're important, but seldom can we make the case for exactly why they're important and how to measure their impact. Lift models correlate these drivers and brand perceptions to other attributes that everyone agrees are important, such as share of wallet (how much of a customer's overall spending in this domain goes to us). At Molecular we run an analysis to show which drivers and brand attributes are most closely correlated to share of wallet, and thus where to prioritize brand efforts. The result is evidence about which brand attributes are most likely to lead to increased revenue when improvements are made.
I think the key point is the fact that there is no ONE method of user research that will give you all the insight you need into your users. Instead, people should strive to assemble different research methodologies which balance one another in terms of their strengths and weaknesses.
Thinking off the top of my head at likely combinations there is:
1. Web site click-stream analysis and usability testing
2. Customer phone interviews and customer surveys
I'm sure there are a bunch of other combinations that my sleep deprived mind can't think of. Hmm... maybe there's a chapter or at least an article somewhere in that...
Craig Cochran makes some excellent points in his article on the downsides of user surveys. In addition to his points about the timeliness of data and the difficulty in creating an unbiased survey (impossible, really), it's important to remember that a survey, like any research method, is good at some things and lousy at others. If you want data on real user behavior, go watch users in real life or via your log files instead of running a survey. If you want new ideas for solving problems or meeting unmet needs, asking users scaled questions probably won't help. But if you want to gather quantitative data on user goals and attitudes to generate persona segmentation, I find that surveys can provide helpful input. If you want to test a theory about what users think or what features they want, a survey is a fast way to gather helpful data.
Back in March at the IA Summit in Vancouver, I presented "Bringing More Science to Persona Creation," which was essentially a preview of some of the ideas in the book, particularly the techniques for bringing quantitative data and statistical analysis into the persona creation process.
After the session, Christopher Fahey wrote an insightful post to his blog worrying that I was saying all personas should be more scientific. He's concerned that if personas require so much more work to create, people are less likely to bother creating and using them.
It's a valid concern, and my point was never that all personas need to have quantitative rigor. Like everything we do, we need to know our audience. If our audience will buy into personas created based on a few quick user interviews, great. Go for it. But if they will be skeptical that we are drawing important conclusions from so few data points, then quantitative research and analysis can be very successful. As personas become more popular, marketing departments are looking at them more closely and demanding that we apply the same rigor to our personas as they have applied for years to their segmentation models. Not all personas require this type of research, but tools are emerging for when we do need to kick it up a notch.
In many cases, ad hoc personas based on quick qualitative research (e.g., one-on-one interviews) are all we need.
Should one of your personas always be a grump?
