How To Improve Your Design Process With Data-Based Personas
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Most design and product teams have some type of persona document. Theoretically, personas help us better understand our users and meet their needs. The idea is that codifying what we’ve learned about distinct groups of users helps us make better design decisions. Referring to these documents ourselves and sharing them with non-design team members and external stakeholders should ultimately lead to a user experience more closely aligned with what real users actually need.
In reality, personas rarely prove equal to these expectations. On many teams, persona documents sit abandoned on hard drives, collecting digital dust while designers continue to create products based primarily on whim and intuition.
In contrast, well-researched personas serve as a proxy for the user. They help us check our work and ensure that we’re building things users really need.
In fact, the best personas don’t just describe users; they actually help designers predict their behavior. In her article on persona creation, Laura Klein describes it perfectly:
“If you can create a predictive persona, it means you truly know not just what your users are like, but the exact factors that make it likely that a person will become and remain a happy customer.”
In other words, useful personas actually help design teams make better decisions because they can predict with some accuracy how users will respond to potential product changes.
Obviously, for personas to facilitate these types of predictions, they need to be based on more than intuition and anecdotes. They need to be data-driven.
So, what do data-driven personas look like, and how do you make one?
Start With What You Think You Know
The first step in creating data-driven personas is similar to the typical persona creation process. Write down your team’s hypotheses about what the key user groups are and what’s important to each group.
If your team is like most, some members will disagree with others about which groups are important, the particular makeup and qualities of each persona, and so on. This type of disagreement is healthy, but unlike the usual persona creation process you may be used to, you’re not going to get bogged down here.
Instead of debating the merits of each persona (and the various facets and permutations thereof), the important thing is to be specific about the different hypotheses you and your team have and write them down. You’re going to validate these hypotheses later, so it’s okay if your team disagrees at this stage. You may choose to focus on a few particular personas, but make sure to keep a backlog of other ideas as well.
I recommend aiming for a short, 1–2 sentence description of each hypothetical persona that details who they are, what root problem they hope to solve by using your product, and any other pertinent details.
You can use the traditional user stories framework for this. If you were creating hypothetical personas for Craigslist, one of these statements might read:
“As a recent college grad, I want to find cheap furniture so I can furnish my new apartment.”
Another might say:
“As a homeowner with an extra bedroom, I want to find a responsible tenant to rent this space to so I can earn some extra income.”
If you have existing data — things like user feedback emails, NPS scores, user interview notes, or analytics data — be sure to go over them and include relevant data points in your notes along with your user stories.
Validate And Refine
The next step is to validate and refine these hypotheses with user interviews. For each of your hypothetical personas, you’ll want to start by interviewing 5 to 10 people who fit that group.
You have three key goals for these interviews. For each group, you need to:
- Understand the context in which they need to solve the problem.
- Confirm that members of the persona group agree that the problem you recorded is an urgent and painful one that they struggle to solve now.
- Identify key predictors of whether a member of this persona is likely to become and remain an active user.
The approach you take to these interviews may vary, but I recommend a hybrid approach between a traditional user interview, which is very non-leading, and a Lean Problem interview, which is deliberately leading.
Start with the traditional user interview approach and ask behavior-based, non-leading questions. In the Craigslist example, we might ask the recent college grad something like:
“Tell me about the last time you purchased furniture. What did you buy? Where did you buy it?”
These types of questions are great for establishing whether the interviewee recently experienced the problem in question, how they solved it, and whether they’re dissatisfied with their current solution.
Once you’ve finished asking these types of questions, move on to the Lean Problem portion of the interview. In this section, you want to tell a story about a time when you experienced the problem — establishing the various issues you struggled with and why it was frustrating — and see how they respond.
You might say something like this:
“When I graduated college, I had to get new furniture because I wasn’t living in the dorm anymore. I spent forever looking at furniture stores, but they were all either ridiculously expensive or big-box stores with super-cheap furniture I knew would break in a few weeks. I really wanted to find good furniture at a reasonable price, but I couldn’t find anything and I eventually just bought the cheap stuff. It inevitably broke, and I had to spend even more money, which I couldn’t really afford. Does any of that resonate with you?”
What you’re looking for here is emphatic agreement. If your interviewee says "yes, that resonates" but doesn’t get much more excited than they were during the rest of the interview, the problem probably wasn’t that painful for them.
On the other hand, if they get excited, empathize with your story, or give their own anecdote about the problem, you know you’ve found a problem they really care about and need to be solved.
Finally, make sure to ask any demographic questions you didn’t cover earlier, especially those around key attributes you think might be significant predictors of whether somebody will become and remain a user. For example, you might think that recent college grads who get high-paying jobs aren’t likely to become users because they can afford to buy furniture at retail; if so, be sure to ask about income.
You’re looking for predictable patterns. If you bring in 5 members of your persona and 4 of them have the problem you’re trying to solve and desperately want a solution, you’ve probably identified a key persona.
On the other hand, if you’re getting inconsistent results, you likely need to refine your hypothetical persona and repeat this process, using what you learn in your interviews to form new hypotheses to test. If you can’t consistently find users who have the problem you want to solve, it’s going to be nearly impossible to get millions of them to use your product. So don’t skimp on this step.
Create Your Personas
The penultimate step in this process is creating the actual personas themselves. This is where things get interesting. Unlike traditional personas, which are typically static, your data-driven personas will be living, breathing documents.
The goal here is to combine the lessons you learned in the previous step — about who the user is and what they need — with data that shows how well the latest iteration of your product is serving their needs.
At my company Swish, each one of our personas includes two sections with the following data:
| Predictive User Data | Product Performance Data |
|---|---|
| Description of the user including predictive demographics. | The percentage of our current user base the persona represents. |
| Quotes from at least 3 actual users that describe the jobs-to-be-done. | Latest activation, retention, and referral rates for the persona. |
| The percentage of the potential user base the persona represents. | Current NPS Score for the persona. |
If you’re looking for more ideas for data to include, check out Coryndon Luxmoore’s presentation on how his team created data-driven personas at Buildium.
It may take some time for your team to produce all this information, but it’s okay to start with what you have and improve the personas over time. Your personas won’t be sitting on a shelf. Every time you release a new feature or change an existing one, you should measure the results and update your personas accordingly.
Integrate Your Personas Into Your Workflow
Now that you’ve created your personas, it’s time to actually use them in your day-to-day design process. Here are 4 opportunities to use your new data-driven personas:
- At Standups
At Swish, our standups are a bit different. We start these meetings by reviewing the activation, retention, and referral metrics for each persona. This ensures that — as we discuss yesterday’s progress and today’s obstacles — we remain focused on what really matters: how well we’re serving our users. - During Prioritization
Your data-driven personas are a great way to keep team members honest as you discuss new features and changes. When you know how much of your user base the persona represents and how well you’re serving them, it quickly becomes obvious whether a potential feature could actually make a difference. Suddenly deciding what to work on won’t require hours of debate or horse-trading. - At Design Reviews
Your data-driven personas are a great way to keep team members honest as you discuss new designs. When team members can creditably represent users with actual quotes from user interviews, their feedback quickly becomes less subjective and more useful. - When Onboarding New Team Members
New hires inevitably bring a host of implicit biases and assumptions about the user with them when they start. By including your data-driven personas in their onboarding documents, you can get new team members up to speed much more quickly and ensure they understand the hard-earned lessons your team learned along the way.
Keeping Your Personas Up To Date
It’s vitally important to keep your personas up-to-date so your team members can continue to rely on them to guide their design thinking.
As your product improves, it’s simple to update NPS scores and performance data. I recommend doing this monthly at a minimum; if you’re working on an early-stage, rapidly-changing product, you’ll get better mileage by updating these stats weekly instead.
It’s also important to check in with members of your personas periodically to make sure your predictive data stays relevant. As your product evolves and the competitive landscape changes, your users’ views about their problems will change as well. If your growth starts to plateau, another round of interviews may help to unlock insights you didn’t find the first time. Even if everything is going well, try to check in with members of your personas — both current users of your product and some non-users — every 6 to 12 months.
Wrapping Up
Building data-driven personas is a challenging project that takes time and dedication. You won’t uncover the insights you need or build the conviction necessary to unify your team with a week-long throwaway project.
But if you put in the time and effort necessary, the results will speak for themselves. Having the type of clarity that data-driven personas provide makes it far easier to iterate quickly, improve your user experience, and build a product your users love.
Other Resources
If you’re interested in learning more, I highly recommend checking out the linked articles above, as well as the following resources:
- “Running Lean: How to Iterate from Plan A to a Plan That Works,” Ash Maurya
- “How to Build Robust User Personas in Under a Month,” Tony Zambito, ConversionXL
- “Resurrecting Dead Personas,” Meg Dickey-Kurdziolek, A List Apart
- “A Closer Look At Personas: A Guide To Developing The Right Ones,” Shlomo Goltz, Smashing Magazine.
Further Reading
- When Words Cannot Describe: Designing For AI Beyond Conversational Interfaces
- Everything I Know About UX Research I First Learned From Lt. Columbo
- Improving The Double Diamond Design Process
- Five-Second Testing: Taking A Closer Look At First Impressions (Case Study)
(rb, ra, yk, il, mrn)
