It's a known fact that to build a successful product, you need to understand your target audience and empathize with them. With this goal in mind, startup founders, product designers, and product managers turn to research. We talk to our target audience, read reviews of our product, and invite potential customers to test our apps and give us feedback.
When done with research, we use various methods to analyze the data and share insights. This is where product makers often turn to user personas and empathy maps. While I can't speak for other professions, these tools are UX bootcamps’ darlings – I see them in nearly every bootcamp graduate's portfolio. It's just another reason for their popularity in my field of expertise.
I have to admit that this popularity bothers me. I'm not entirely against personas and empathy maps, but they have their flaws. Let's explore how they can be misleading and how to use them effectively despite their shortcomings.
What Are Empathy Maps and User Personas?
Both empathy maps and user personas are visualization tools helping product teams synthesize user research insights. They are commonly recommended for product managers, product designers, and startup founders to better understand their users.
Currently, the most widely used template of an empathy map includes four zones: what a user says, thinks, does, and feels. A persona, on the other hand, is a detailed portrait of a typical user of your product. It's fictional but based on real people you are meeting through research.
What's So Problematic About Them?
In my experience, both empathy maps and personas share similar issues:
#1: They Reinforce Stereotypes
Data interpretation is a complex matter, and, as product people, we bring our own biases into the process. I saw a few times how fellow designers and product managers were adding farfetched ideas to personas. For example, by describing some type of users as creative solely based on the brand of their smartphone.
Oftentimes, people don't even notice how they extrapolate their own personality and what they know about their friends to people they don't have much in common with. This can lead to misleading assumptions and overconfident product decisions.
Not long ago, I worked on an app helping people master mindfulness practices like meditation and breathing exercises. My product manager, a man, conducted extensive user research. When sharing insights, he confidently said that men use apps like ours to enhance their intellectual work, while women need such apps to manage emotions.
In reality, both groups were describing the same need to regain control over their mental state. They simply used different terms according to how they were taught to think and speak about themselves based on gender. Men framed it around “thoughts,” while women framed it around “emotions,” reflecting social conditioning rather than a fundamental difference. Thoughts and emotions are interconnected, one triggers the other.
Kudos to the product manager for listening to me and acknowledging his mistake. But without me being there at the moment, he might have ended up reinventing all over again the sexist stereotype that men are rational beings and women are the emotional ones.
#2: They Encourage Emphasizing Irrelevant Data
Personas and empathy maps often include details that have little to no impact on a user's wants, needs, and behaviors. This can lead product teams to draw false correlations and assumptions. For example, age or occupation doesn’t necessarily determine how someone will use an app, yet these details are often included simply because they’re part of the template. Meanwhile, truly significant factors can be overlooked just because they don’t fit into a template.
#3: They Are Too Generic
Even when we define multiple user types, empathy maps and personas still portray average users rather than real individuals. This can lead to overlooking edge cases and even whole audience groups. A single persona may blend distinct audience segments that should be looked at separately.
Are There Ways to Work Around These Problems?
The key is to recognize that these frameworks can be elusive and use them thoughtfully.
#1: Adapt Templates to Fit Your Needs
The most widely used empathy map template encourages you to make assumptions and, in my opinion, doesn't make a lot of sense. A core principle of healthy communication is never assuming what people think and feel without asking them about it directly. You know what a user thinks and feels only because they've said it. On top of that, even if they say how they feel, misinterpretations are still possible.
Look at a different template below; this one comes from the Interactive Design Foundation. It arranges sections differently but still raises a lot of questions. Does it really matter how they appear to others? Why do we assume they are always going to act the same in public?
These templates can create confusion. On the bright side, they also contain good ideas on what factors to consider when making user-centered products. Instead of following them rigidly, create a version that better serves your needs.
Personas come in various templates, too, and you decide what data to include. Personally, I prefer segmentation, which I see as a variation of personas that groups users based on how and why they use a product. For instance, when designing an app for pet owners, I categorized users into such groups as anxious pet owners, frequent travelers, and busy owners, focusing on behaviors and contexts rather than age or appearance.
#2: Question Every Data Point
Before adding information to a persona or empathy map, ask yourself: Did I directly ask about this in research, or am I making assumptions? Did I obtain this information through a direct answer that said exactly this? In my opinion, it’s better to acknowledge gaps in your understanding of your users than to fill them with guesswork.
#3: Use Alternative Tools
You don't have to use personas and empathy maps at all. Consider other frameworks, like Jobs to Be Done (JTBD). Focus solely on your users’ pain points, tasks, and goals instead of random data fluff.
#4: Use AI for Pattern Recognition
AI can help analyze interview transcripts and detect recurring patterns. While it shouldn’t replace human analysis, it can complement it by highlighting details you might overlook.
Why Not Ditch These Methods Altogether?
Every discipline evolves, discarding outdated practices and adopting new ones. UX should be no different. We should absolutely question the frameworks we use, but these tools exist for a reason, and they are adaptable.
Personally, I find personas valuable for storytelling, especially when presenting ideas to my teams, other teams, and stakeholders. They also help me break out of creative blocks during brainstorming. Likewise, when categorizing research data, I often borrow sections from empathy maps.
