Why I avoid demographic details in my personas
May 12, 2018 | 5 minute read
Image credit: Lukas (@goumbik)
When I first started engaging with UX about seven years ago, I often built personas that had lengthy biographies and were full of demographic details. Perhaps it was the by-product of my previous stints in marketing where everything is driven by demographics. Or perhaps it was my due to my belief that rich and engaging personas were the only way to foster empathy and understanding with stakeholders.
Luckily, I’ve changed that mindset. Lengthy biographies and demographic details have become the cornerstones for my creative writing projects. And unless the demographic details add specific value to a design artefact, I avoid using them.
Earlier today, I stumbled upon a really interesting article from Indi Young about describing personas with demographic details and the inherent problem with using demographic details.
Demographics can cause assumptions, shortcuts in thinking, and subconscious stereotypes by team members.
The article reminded me of some of the challenges I encountered on a project where I explored user needs for a training system. To foster empathy and understanding of user needs, I created four low-fidelity personas, each of which had age, education level, income level, and location details. While these details did enhance the personas and brought specific motivations to life, they also exposed the biases and stereotypes held by some of the stakeholders.
For example, the demographic details for the new graduate hire caused one senior manager to argue that if the training system was changed, the company would have to alter the new training based on the employees age. They felt that “older staff would need additional technical training to understand how to use a tablet” and that “younger staff could be left on their own to figure it out”. They also stated that a new graduate hire should do the training in the office as their tablet could be stolen because “they live in a run-down flat share in East London”.
When I asked the manager why they felt this way, they said it was because of the persona’s age, location, and travel pattern. They assumed that a young person who rides a bike and lives in East London probably lives in a flat share. They also assumed that all young people could learn new technology without the need for additional training.
Similarly, the demographic details for the experienced hire persona proved to be just as problematic. At the beginning of the project, it was difficult to get some of the senior managers to empathise with the needs of experienced hires. And so I created a persona - Louise Warren, a 32 year-old woman who lives in Richmond and commutes to the office by train.
For Louise, the core need for a flexible, anytime/anywhere training system was due to two key factors. First, her client commitments meant that she travelled around London and was often out of the office. And secondly, during part of her commute, she had no mobile or Wi-Fi signal and wanted to download and complete the modules offline.
Despite having listed other motivations driving Louise’s needs, two of the senior managers said that the underlying reason for Louise requiring flexible training was due to her “family commitments”. In the words of one of the managers, “she is probably a new mum with two children under the age of 4”. The manager went on to argue that because Louise’s job involves travel, she can’t do the training during the day and has to care for her children and household at night.
Again, when I asked the manager why they felt this way, they said it was because of Louise’s age, gender, location, and travel pattern. They assumed that a 30-something experienced employee is married with children and lives in the commuter belt of London.
In both cases, the actual user needs, pain points, and long-term goals were accepted as valid insights and not questioned. It was the motivations driving the user needs that proved to be most problematic. The demographic details allowed stakeholders to use their own biases and stereotypes to decide whether the motivation was valid - and by extension, if the user need was valid.
To overcome this issue, I decided to remove most of the demographic details when I created the high-fidelity persona for the new graduate user. Instead, I relied on iconography to better communicate insights rather than using a demographic detail that could be open to interpretation or misconstrued. For example, instead of listing a location, I included icons about travel patterns to demonstrate why some users wanted a flexible learning system with modules that could be downloaded and completed offline.
I opted to keep the degree and school information in the persona as my analysis of existing employee data found that graduates from non-Russell Group universities were likely to complete the training sooner. This was motivated by their belief that their degree could prevent them from landing a permanent contract at the end of their initial one-year graduate placement contract.
So what’s my take-away message from this experience?
Demographic details can be very helpful. And yes, sometimes you need to include them.
But if possible, try and use other data and insights to communicate the same idea, as suggested in Indi’s article.
"Keep the well-researched persona and replace those demographic descriptions with descriptions of the underlying reasoning. Reasoning rarely conforms to demographic lines."
Even the inclusion of what appears to be innocuous demographic details can result in key project stakeholders making assumptions that are grounded in their biases and stereotypes, not real data or insights. And it can be challenging to correct these biases after exposing them.
And while personas are still a great way to present insights, just be aware that demographic details can cause biases and stereotypes to bubble to the surface, which in turns negates the power of personas to foster empathy and understanding amongst stakeholders.