In the past 10 years, I’ve sat in countless meetings in multiple countries where Product Managers at technology companies talked with enthusiasm about the company’s “tech savvy” or “power users”.
These users were never clearly defined but there was a lot of confidence that these were the really important users: the ones who were the most technically knowledgeable and the most proficient. They were at one end of a spectrum, whilst at the other we had “late adopters” or the “non savvy” users.
There are a few problems with this approach to product design. First, assumptions you and your company make about the technical knowledge of your users are often inaccurate. Scratch the surface of the assumptions about who is “tech savvy” and who is not, and you’ll discover a wealth of unconscious bias about gender, age, race, education and wealth. “Tech savvy” is often synonymous with “people like me” whilst being non-tech savvy is associated with otherness; people not like me. If you’re designing based on inaccurate assumptions about your users, then your product is either going to flop or you’re going to miss out on a lot of users you could have engaged with.
Second, the idea that all humans can be plotted along a linear spectrum of technical “savviness” is outdated. These days, as a User Researcher who has interviewed hundreds of people about how they use technology, I believe it’s more accurate to think of a person’s technical knowledge as something that’s multidimensional. It’s so common these days to observe someone recruited to a research study because they matched a “non savvy” demographic profile and they end up blowing you away with their sophisticated critique of a product. As technology becomes ubiquitous in every area of life, people’s usage is becoming ever more diverse. A new pattern is emerging to reflect this changing world; each person is developing their own unique technology fingerprint.
The birth of the Technology Fingerprint
So what is a technology fingerprint, exactly? Your technology fingerprint is made up of all the technology you use today at home, at work, for play. But it also contains the past experiences you have had with different devices, UIs and UI terminology. It’s everything you used as a child, the devices you used at school, the software you used everyday for work 10 years ago. It’s all the computer games you’ve used and all the gaming devices that went with them. It’s all the different credit card machines you’ve used around the world, it’s the display screens in your car, it’s everything you experienced shopping online. It is the sum total of all these experiences. Your own unique technology fingerprint.
One city, two different experiences
One way of thinking about the uniqueness of your technology fingerprint is to consider how human experiences of the same city can vary. Let’s imagine two people, Anna and Alexa. They are the same, demographically speaking: the same age, same gender, same race, they even went to the same university in a small town in the UK. They move to live in London in 2000. Over the next 10 years, they live their separate lives. They live in different parts of the city, and work in different jobs in different locations in London over the 10 years. They explore, they go out, they meet people, they fall in love. Life happens. There are highs and lows, triumphs and hard times. After 10 years, Anna and Alexa’s personal maps of the city are very, very different. Some locations on their maps might be the same but the memories, experiences and meaning embedded in those places are very different to each person.
However, there are some strong shared patterns on these two maps. Both Anna and Alexa took public transport most days during the 10 years they lived in London. The map of the underground system they used was the same. They used the same ticket machines, the same Oyster card system. The transport system uses the same signage, maps and protocols across the whole network of trains and buses. They tapped in and tapped out at every station. Both Anna and Alexa had a shared understanding of the system. Every day they, along with 8.6 million people, flowed to and from work, facilitated by the shared patterns and symbols of the London transport system. Patterns create shared understanding. They make life easier for everyone.
What does any of this have to do with digital product design?
Users today are far more complex than crude demographics or one-dimensional personas. Each individual user of your product, or prospective user, has their own individual fingerprint when it comes to the things they have been exposed to before, and the order in which they were exposed to them. The order is key: if the first mobile phone you used was a Nokia, it’ll take you some time to adapt to using an iPhone. If your first mobile was an iPhone, it’ll take you some time to adapt to an Android. People’s preferences for how technology should work are heavily influenced by this order effect. The resulting assumptions that these users make are called their “mental model”.
It’s critical to understand that these mental models are not static; your user base is organic and it’s evolving fast. As technology becomes ubiquitous and people spend more time online, and are more connected to devices, their internal mental model of how things work, and how they expect new things to work, constantly evolves. A user may find a product they thought used to feel intuitive (e.g. their bulky Nokia flip phone) would be easier to use if it adopted a more recent pattern (e.g. their friend’s iPhone).
What are the implications for product design?
The more technology fills every area of life, the more critical it is that technology shares common patterns. This will be the only way to make sense of the volume of content and navigate our new world. Here are some of the implications for product design:
Design Patterns, and the standardised use of them, will be more important than ever.
Frameworks like Jobs to be Done will replace user personas, with products being built around the unique motivations of the user, rather than their demographic attributes.
Research participants will be recruited based on past behaviour (e.g. how many apps have you downloaded on your smartphone?) rather than demographic data (e.g. gender).
Designing around a user’s mental model – their technology history, their use of specific products – will become a cornerstone of successful product design.
What other implications do you think there might be for digital products?
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