Beyond The Conversation: Context-Fluid Experiences and Augmented Cognition

Written by: Cameron Miller

Have you ever felt like you were having a one-sided conversation with someone? It feels as if you are exerting much effort with minimal feedback or response in return.

When we use an application, we can think of this experience as a conversation between the user and the technology. Sometimes, it feels as if we are having that same one-sided conversation with the technology we are using. As modern people, we learn the ins and outs of the tech we are interacting with, from the information architecture to the layout of the UI elements. Because of this, we adapt to the technology. Just as we adapt to the technology, the technology should also adapt to us. This conversation should not be one-sided.

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How UX Will Save the World

Written by: Sasha Akhavi

Douglas Adams, in The Restaurant at the End of the Universe, tells the story of the Golgafrinchians. The people of planet Golgafrincham, the story goes, figured out how to get rid of an entire useless third of their population by duping them into thinking the planet was doomed and that they were eligible for the first ship out. This group was, apparently, designated by profession: Doctors, teachers, and (presumably) writers of humorous science-fiction were deemed worthy to remain; telephone sanitizers, hairdressers, and jingle writers were shanghaied.

Sometimes, remembering this story, I wonder whether this field of UX—to which I’ve given my professional life—would qualify me for the ship. After all, we create no shelter, food, or clothing for anyone; our work rarely inspires anyone to the point of tears (unless they be tears of frustration); and I’ve never met a 6-year-old who wants to be one of us when they grow up.

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The War on Information

Written by: Dash Neimark

The world as we know it today is rich in information. At whim, we can usually find (without much delay) an information source that answers a question, suggests nearby restaurants, tells us how to travel, or provides us with data for the paper we are writing. The internet, as well as the technological innovations that allow us to easily and enjoyably access it, has given rise to a new era where knowledge is plentiful and interpretation is vital.

This luxury has had a huge impact on the dynamic of society as a whole. For our ancestors, obtaining information was the primary challenge; the shifting technological landscape now means we must deal with, rather than search for, information.

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Ending the UX Designer Drought

Written by: Fred Beecher

The first article in this series, “A New Apprenticeship Architecture,” laid out a high-level framework for using the ancient model of apprenticeship to solve the modern problem of the UX talent drought. In this article, I get into details. Specifically, I discuss how to make the business case for apprenticeship and what to look for in potential apprentices. Let’s get started!

Defining the business value of apprenticeship

Apprenticeship is an investment. It requires an outlay of cash upfront for a return at a later date. Apprenticeship requires the support of budget-approving levels of your organization. For you to get that support, you need to clearly show its return by demonstrating how it addresses some of your organization’s pain points. What follows is a discussion of common pain points and how apprenticeship assuages them.

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Intent to Solve

Written by: Laura Klein

When we’re building products for people, designers often do something called “needs finding” which translates roughly into “looking for problems in users’ lives that we can solve.” But there’s a problem with this. It’s a widely held belief that, if a company can find a problem that is bad enough, people will buy a product that solves it.

That’s often true. But sometimes it isn’t. And when it isn’t true, that’s when really well designed, well intentioned products can fail to find a market.

When isn’t it true?

When I tell product managers and entrepreneurs that their dream customers might not buy this product—even if the product solves a problem—sometimes they get angry.

“No!” the managers and entrepreneurs yell. “This is a serious problem for my users! They struggle with this thing every day! They told us this. We saw them struggling with it. We did our research!”

But think about all of the problems that you encounter in a day. Some of them are almost entirely in your control, like deciding how to feed yourself. Some of them are largely out of your control, like sitting in traffic on the way to work. Some of them are almost entirely out of your control, like certain types of health problems.

So, there you are. Sitting in traffic, with a migraine, and trying to figure out what you want for lunch. Which problem do you solve?

Do you solve the worst one? The one that happens every day? The easiest one? Do you give up entirely and just turn around and go back to bed? The only thing that most people won’t do is to solve all of them all at once.

In other words, every day, humans use their limited emotional resources to solve specific problems while they choose to live with other problems or put off solving them until another day.

This tendency of humans to not always solve their worst problems is incredibly important for you to recognize when you’re doing early user research because it has implications for your product. Just because you’ve identified a serious problem doesn’t mean that anybody will pay you to solve it for them.

And remember, when we’re talking about “payment,” we’re not necessarily just talking about money. Free products are often only free if your time has no value. Sure, some products cost money, but people also pay with their time, attention, and effort. If you’re asking somebody to spend hours learning how to use your product, you’ve just charged them a fairly high hourly rate for your free product. You’d better make it worth their while.

You can do something about this

So, how can you separate out the problems that people will pay you to solve from the problems they won’t? Sure, intensity, frequency, and difficulty of solving the problem can influence whether a user will try to solve it. But there’s an even more important thing to look for: Intent to solve.

For example, if you’re a gym owner, and you talk to three women, all of whom say they want to get into better shape, which of the following sounds like the person most likely to join your gym?

a) I’m in terrible shape, and it’s really affecting my health. I’ve never joined a gym, but I’m definitely going to do it this year.
b) I really love running and swimming at my neighborhood pool, and I consider myself to be in pretty good shape. But I’m not a fan of gyms.
c) I’m in ok shape. I’ve belonged to several gyms in the past, but I don’t currently belong to one.

Did you say C? You should have.

Sure, A specifically states that she is going to join a gym and her perceived problem is larger than the other two, but we’ve all declared that we’re absolutely going to do something this year and then not done it. That’s what New Year’s resolutions are. B seems perfectly happy with her routine and doesn’t really have the problem that we’re solving.

C, on the other hand, shows both motivation and a past intent to solve the problem in the way that you, as a gym owner, would like. In other words, she has previously sought ways to get into better shape and has even spent money on gyms. She has shown an intent to solve in the past which is an excellent predictor of her behavior in the future.

There is one notable exception

But hang on. I know what you’re thinking. You’re thinking “but what about Twitter?” Or maybe Snapchat, or WhatsApp, or a dozen other products that solve problems that people didn’t know they had.

It’s true, there are products that don’t solve an obvious problem. Things like Twitter create new behaviors (sort of) and don’t seem to solve anything that anybody ever intended to solve before Twitter came along.

Now, we could argue all day about whether or not Twitter solves a specific problem or perhaps many problems—or even creates problems. The important thing to point out here is that, when you’re creating a product that is truly going to create a new behavior, it is just much, much harder to validate before you build. That’s doubly true if the product relies on network effects, like Twitter does.

Honestly, there may simply be no way to tell if something like Twitter is going to take off before you build anything at all. That’s why, although we do have things like Twitter, we also have tens of thousands of social networking sites and apps that nobody’s ever heard of.

What to look for

If your product does solve a problem that people likely knows exists, though, there’s a very useful technique for figuring out if it’s a good one to solve.

We’ll assume for the moment that you’re already doing user research and customer development. You’re building something, so obviously you’re talking to people who you think might be in the market for such a product—or at least people who have the problem that your product solves.

Just talking to people though, isn’t enough; you have to ask them the right questions. Instead of just asking them questions designed to confirm whether or not they have a specific problem, you need to ask questions designed to find out if they have already shown an “intent to solve” that problem.

What you’re looking for is not just a problem—in the case of the gym owner, a potential user wanting to get into better shape—you’re also looking for a previous behavior of trying to solve the problem. Bonus points if they have spent money trying to solve the problem.

When you find a serious problem that people have tried and failed to solve, you can generally count on their trying to solve it again in the future. Ideally, you want something that they’re actively searching for a solution to right now.

If you want to convince somebody to join your gym, it’s much easier to start with somebody who already wants to join a gym. At that point, you’re being compared to all other gyms. You’re not being compared to literally everything else that the user could do with her money and time.

Humans encounter all sorts of problems every day. Most, we just ignore or deal with. Only a few reach a level that we will spend our precious resources to solve. If you find a problem that is serious enough that people have already shown an intent to solve, it will be far easier to convince people to try your solution.

If you think you have a brilliant idea for a product that creates a brand new form of user behavior and may or may not solve a particular problem, more power to you. It’s not impossible to make it work, but it’s significantly harder to get it adopted than the millions of things that solve real problems that people encounter every day.

For the rest of you who want to make sure a problem really exists before you try to solve it, try evaluating your user’s intent to solve before you build anything. It’ll give you tremendous insight into whether or not your product will be adopted.