As mobile gains ever more ground and mobile first continues to be knocked around as the design strategy for the web, I encourage people to look at their use statistics.  Although mobile first is a great strategy if you don’t have statistics to draw upon for your site, or your reorganization needs a mobile injection, nothing will serve you better than the statistics regarding what your user does on your site.

You know what makes Google awesome? Their search algorithm is pretty darn smart. You know what makes DOMZ awesome? Every stinking link on there has been verified by a real, honest-to-goodness human being. You know what sucks about site search on most websites, even when they’re running a Google custom search? The only time a human ever looks at the search is when they are actually using it to find something.

I’ve read several different books and articles about web projects and how to make sense of what needs to happen when. Everyone has their own slant and it flavors how the entire process should go. Meanwhile they hope the “magic” in another group is happening.

We’ve all been there at some point or another. A new project is just about to start. Everyone in the know is bracing for impact and the people who are going to contribute are blissfully unaware of the monster lurking around the corner.

Information comes in all shapes and sizes. Some is simple. It’s copy that goes on a page. It’s an image. It’s a sound file. It’s a single PDF. It’s whatever atomic piece of information you can imagine. Then there is the molecular level, for instance, whole web pages with mixed content. Then there is listed information: movie titles, collections of documents, retail products, animals with feathers, types of beer, whatever.

Listed information can get tricky.

As is typical with the break room at many offices, we have a microwave. Actually, we have two, but there is one in particular that everyone knows about and avoids. Everyone but me*, that is. It is a machine crafted in the forges of bad usability and total misunderstanding of user journey.

The main requirement for a microwave is that one be able to set the time for which their food will receive a nuclear blast, converting last night’s roast beef into magma. Either there is a “time cook” button, or you simply enter the time directly. The uranium-235 does the rest.

Most of what I have seen on the web regarding Information Architecture has been related, primarily, to what the user sees and interacts with directly. This means, what users see, and how the site is, ultimately, hierarchically constructed. Very little consideration is given for what is ACTUALLY going on with the site.

I’m taking a brief detour and talking about something other than user tolerance and action on your site. I read a couple of articles, which you’ve probably seen yourself, and felt a deep need to say something. Smashing Magazine published Does The Future Of The Internet Have Room For Web Designers? and the rebuttal, I Want To Be A Web Designer When I Grow Up, but something was missing.

Both articles focused on content and how it gets passed around. The problem is, there is a lot more going than just content on the web. What both articles overlook is the work of the web developer or web engineer. No, this isn’t an attempt to shoehorn engineers into this discussion. It’s about the fact that they are needed to produce function.

Beyond the world of content is a whole slew of function on the web. Web apps have become increasingly important in the landscape of the web. As a matter of fact, you’re currently visiting a web app. Yes, you’re seeing content, but you are also interacting with an application which allows me to manage and publish that content for you to see.

Congrats, you’ve made it to the third part of my math-type exploration of anticipated user behavior on the web. Just a refresher, the last couple of posts were about user tolerance and anticipating falloff/satisficing These posts may have been a little dense and really math-heavy, but it’s been worth it, right?

I have one last function to look at. This function will let us sort out how long a certain percent of our users will hang out at a site, trying to accomplish their goals given a random population interacting with a page or site they have never visited before. By having the ability to calculate the output of the user falloff function, we can compare user test results to our falloff curve without plotting the entire curve to show anticipated versus actual results.

As we discussed last week, users have a predictable tolerance for wait times through waiting for page loading and information seeking behaviors. The value you get when you calculate expected user tolerance can be useful by itself, but it would be better if you could actually predict the rough numbers of users who will fall off early and late in the wait/seek process.

It is reasonable to say that no two people have the same patience for waiting and searching. Some people will wait and search for an extraordinary amount of time while others get frustrated quickly and give up almost immediately. To expect that all of your users will hold out until the very last moment of the predicted 13, or so, seconds hardly reflects reality.

Instead, we can say that we have some maximum tolerance, L, which we can compute which the very last holdouts will actually wait for. Unfortunately, we also know that a majority of users, if they have to wait very long, won’t even see your site since they will fall off before the page finishes loading. This means that the bulk of the users which see your site will be something less than the number of users who actually attempted to load your site.