Let’s talk about user personas
A user persona is a semi-fictional character based on user research that is used to represent the needs of a larger group of users. Most often used in the tech industry in the practice of user experience design, the concept of building archetypical users also has roots in marketing and communications for the purpose of understanding market segmentation and target audience identification.
Generally speaking, the first step in creating a persona is to take a closer look at the data that you’ve gathered, which is ideally at least five user interviews done in the Discovery Phase of your work. While surfacing insights through research, make sure to also listen to the needs and wants of the business area; your client/stakeholder can often be a separate user persona because they have their own motivations. While we want to ensure that your product is user-centred, the reality is that striking a balance between users and the business area will be essential for successful product development.
Taking a closer look at data will allow you to identify patterns in the goals and motivations of different users that you have interviewed in the Discovery Phase. Each user will have a unique set of distinct characteristics and behavioural patterns. Understanding the variations between each users’ behaviours will help guide product development.
In user experience design and tech, a user persona usually looks something like this. The specific touch points within a user persona are:
- Name (fictional) and photo (I like illustrations)
- Demographics: age, location marital status, income
- Relevant patterns of behaviour
- Goals, needs, and motivations
- Pain points (if your user research has them)
There is a debate within the user experience community about whether or not user personas are a true representation of real target audience data. It is generally accepted that this tool can help place audiences with their own unique attributes into categories that guide how product features are prioritized, designed, and developed. Yet many designers and researchers have decided to stop using them altogether. Why?
- Stereotypes: User research done with the intent of creating a user persona can be based on assumptions and stereotypes about a group of people.
- Cutting Corners: Since a persona is an archetype of shared behaviours, user research needs to be done with several people within each audience grouping for it to accurately represent reality. Most designers cut corners and only do a couple of interviews.
- Exclusion: It’s nearly impossible to adequately represent all users of your product with just a few user research interviews, which can result in design choices that exclude users based on ability, gender identity, and cultural experiences.
Yes, the ideal persona is the composite archetype of shared behaviours, but how can someone achieve that? It’s very challenging. As a researcher and designer, my advice would be to create user personas as a way to build empathy and gain perspectives on who might be using your product, but do not have your user personas be your exhaustive decision making tool for all of your design. As you iterate through your design and research cycles, continue to add to your user personas — keep them as an output of your work that is continuously modified as more insights are surfaced. I’d also suggest using a blend of qualitative and quantitative data to build your personas. Grounding your user personas in current demographic data, product analytics, and market research while also layering behavioural research will get you closer to an accurate representation of who you need to be designing for. Lastly, acknowledge the outliers — they are always relevant to the story you are trying to tell and should never be dismissed.
Designers who are building experiences need to step back and understand who they are designing for, and a user persona can help with that process, but it should not be the only tool for building empathy with users. Just remember that your personas are only as good as the research that is behind them — don’t make assumptions, dig deeper to understand people’s context, and use data to drive your outputs.
