Did design make the iPhone successful?

While good design helped Apple’s iPhone gain traction — it was not the driving factor of success, as many people assert. The driving factor was Apple’s  innovation in touch screen technology. Nothing like it had come before — before the iPhone, you could only view sparse, text-only versions of sites like the New York Times.

Examining the evolution of technology allows us to recognize that technologies we see as normal today, such as the internet, were once simply imaginations in someone’s head.

Industry had attempted to create a responsive touch screen for years. The first was a resistive design, a soft touchscreen that flexed under touch to tell the device which part of the screen was being pressed. This flexible screen wasn’t very accurate because the device couldn’t precisely tell where the screen was being pressed. HP took the touchscreen a step further by using lasers. Touching the screen broke the lasers and allowed the device to move the cursor to where the screen was being pressed. This worked but didn’t allow for multitouch. In 2007, Apple released a capacitive touchscreen — the most innovative touchscreen technology ever seen.

While novel touchscreen innovation was the main factor for the iPhone’s success, design had a helping role. When a product is well designed, people are more likely to use it. Think about how flipping through news on Flipboard makes it addictive.

You can also think beyond a UI level about the product’s design more broadly — for example, compare Instagram to Facebook. People can post, like, and comment pictures on Facebook just as they can with Instagram. Many still prefer Instagram over Facebook, despite the features on Instagram simply being a subset of Facebook’s. The reason is Instagram is a product highly focused on one thing — seeing a snapshot of your friend’s life. On Instagram, there are less possible navigation states in which someone could get lost. The minimal interface makes it easy to figure out what to do next. It is this kind of design that plays a much larger role in paradigm shift. Consider the graph for example. Before graphs were designed, we were interacting with numbers either individually or in a list format. The invention of the graph enabled us to see the relationship between different variables in a way that we could not have before.*

* via Bret Victor’s Future of Programming

Good design vs. bad design

When I first started as a designer, I thought frequently about experiences that were well-designed compared to those that were not, and how the outcome differed. What was the value of design? For example, when I click a button and expect a drop down menu to appear, I feel cognitive dissonance when the menu suddenly appears. When the menu slides out however, I feel more at ease. When I use a UI with a lot of jump-cuts (a la things suddenly appearing) my brain has to pretend that all the in-between frames are there. When we use interfaces that actually animate all those in-between frames, however, we can take a shortcut through our visual cortex. The change in interface no longer disrupts the main task.

Think of the human brain like a computer. You have first, a main thread, and second, a GPU – a graphics processing unit in your visual cortex. You can feed people a lot of information through their visual cortex without interrupting what they’re thinking about. Animation allows the user to continue thinking. Imagine using your smartphone without animations. It would be like a tap and jump cut for everything you do. Would you have still bought it?*

How to stop thinking of new ways to do things

I discovered a dangerous phenomenon the other day over conversation with my co-workers Bret Victor and Glen Chiacchieri.

Bret was speaking of the requests he gets to release the source code for his prototypes, and his rationale behind not doing so — that it could lead to innovation halting, because the release creates a standard for what products should be.

I realized,

When an inventor creates a tool and makes it available to the world, the public has a tendency to simply accept the tool as is, use it, and stop thinking of new ways to do things. (thus halting development of the product).

Example #1: the Pie Chart

Pie charts are pervasive…

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… And a poor way to represent information. (See Edward TufteThe Worst Chart in the World, and Oracle’s Reasons Not to Use a Pie Chart)

A simple example illustrates that pie charts make it difficult to make comparisons between two quantities. See:

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What if we represented the same information like this, instead? This illustration enables us to make direct comparisons between quantities.

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Because pie charts cannot spatially fit all information, they also are pleasantly accompanied by a key (right-hand side), which attempts to illustrate what all the components of the pie chart designate.

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Could we design another way to represent this information — one that doesn’t require you darting your eyes back and forth to understand the information?

What about this?

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Okay. Maybe not as aesthetically pleasing. Perhaps a reason why people use pie charts is because all the circles look pretty. But I would argue that we should not sell ourselves short — can we not have a representation that is both intuitively functional, and aesthetically pleasing?

I re-designed a representation of the same information; illustrated below.

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So what?

These are just pie charts. Okay, it’ll take a millisecond longer to process the percentages. Why does it matter?

I think we vastly underestimate how good the quality of an experience (in this case, understanding and exploring the world) could be, because we already have models in our heads of what the medium is currently like.

One step further: Understanding Bayesian Probability

Another way to view this idea is through the lens of Bayes’ theorem. The traditional example is the cancer testing scenario, where you’re presented with a series of probabilities.

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You are typically asked:

Assuming you have a positive test, what is the chance you have cancer?

When calculated out, the number seems much lower than expected.

Some of us simply accept that our brains do not intuitively grasp probabilities, but I’d argue that statistics is only unintuitive because we don’t have proper representations.

How would you design another representation of Bayes’ Theorem?

More to come.