27 Feb 2012
Optimizing for productivity
I came across this interesting anecdote about a waiter in a Swedish restaurant using his computer screen like a static whiteboard. Forgetting for a moment what his real intentions might have been for doing that, let us just take his word for it: by requiring him to click too many times, the computer system was just not optimized for his productivity.
This post reminded me of a client for whom we were designing an interface to record eye readings. The client, being an opthamologist himself, was extremely vocal about how the entire user experience should be. He wanted an interface in which you can enter eye readings by clicking on a series of buttons that had various prescription numbers. He showed it to a few people and they felt that it was fairly easy to use – everything was obvious and lucid. I had some reservations in taking a mouse driven approach, and just as I had suspected, this really ended up slowing down people who were using this interface over and over again. What seemed intuitive and having zero learning curve eventually turned out to be pretty slow and cumbersome for regular and repeated use.
When designing user interactions, one should balance the long term productivity goals of a active user and the apparent immediate ease-of-use of the system for new users. Kevin Fox had recently written about how Google seems to be “simplifying the UX for current users at the expense of the new user learning curve”. I’m sure Google had reasons for doing that, but nevertheless, it’s not trivial to optimize a user experience for both new and power users.
On the other hand, there are also lots of applications that treat all their users equally. In reality, user behavior and engagement changes over time, and so do their needs. Yet, run-off-the-mill analytics software only offer a broad picture of user engagement. This is where cohort analysis becomes useful.
A cohort analysis is a tool that helps measure user engagement over time. It helps UX designers know whether user engagement is actually getting better over time or is only appearing to improve because of growth. – Cohort analysis – measuring engagement over time.
With the help of cohort analysis, one could evolve the user experience to make it more productive the for power users, while at the same time, making it easy enough for new users to get going with the system. We already use graceful degradation as a strategy for enhancing the user experience in modern browsers, while still not completely dropping support for people with older browsers. I see optimizing for productivity the same way – user interactions should offer alternative hooks for the power users to exploit without making the external interface complex. A good example of that would be the spotlight tool on OS X. It stays out of the way, but it’s still just a keyboard shortcut away. A well-designed, modern command line interface can really complement the graphical user interface.
Finally, while designing interfaces, one should be making decisions based on facts and data, rather than gut feeling. I will end this post with another anecdote. We’re currently trying to convince a client to get rid of the confirm email address field in their signup page. In addition to making the user fill an additional field, the current form also prevents the user from copy pasting their email address from the previous field. When we asked the client why they are doing that, they replied, “We don’t want our users to accidentally endup typing a wrong email address”.
This is a classic case of trusting the gut blindly, and it’s clearly not the best way to build a user interface. They are pissing off a lot of users, while all the time thinking that they are actually helping them.
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