A popular saying in software is that 80% of users use 20% of your features. Maybe this is true, but it’s not useful in practice. The problem is it’s never the same 20%. The collective 20%’s intersect with 100% of features, meaning every fringe feature has an audience. This presents a design challenge if you’re looking for a simple interface.
One solution is to design an opinionated interface. Have an opinion on which features define the core experience. That opinion is guided by data, observations, and intuition. But ultimately, it’s a subjective call on how the product should work, and how it should be used.
Using that opinion as a guide, you can design the naive solution — an interface that only includes core features, brazenly omitting secondary features. The result is a product that’s fantastically fast and simple for the core experience, but lacking a depth of features.
The challenge now becomes adding that depth of features without slowing or complicating the core experience. Each additional feature now has a clear cost and benefit. You’re forced to implement features in an appropriate way, or not at all.
Great products evolve over time. It’s rare to get it right on the first try. So we iterate.
The ability to throw something out there and iterate is one of the biggest strengths of the Web — No software to update, no physical hardware to worry about. You can build out a lot of ideas and see what gains traction.
Iteration also leads to trouble. Take a simple, focused product and iterate, iterate, iterate. Pretty soon you’ve got a complex product with dozens of new features and a general lack of focus.
Every feature you added proved its worth when you initially tested it. Each one improved some part of the experience for some percentage of your audience. But taken as a whole, those new features made your product harder to understand, less targeted towards a specific use case, and more difficult to maintain.This is really hard to see when you’re nose-down on a project. It’s difficult to say “No”
to a new feature when the stats tell you it produces an X percent increase in Y.
The only solution to this problem is to take a step back and start over. Re-design the entire thing. This time you’ve got a deeper understanding of the underlying problem and will make a stronger first pass. Now test this new solution against the solution produced through iteration.
There’s a chance you’ll shift the stats to an entirely new baseline, far above where you could get through incremental improvements. Designing a monolithic change is a bigger commitment and risk, but it’s the only way to make a giant leap forward.
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