Do you ever feel that your design has become stale and that despite your making lots of little changes to it over time without any big overhaul there is just no way to drastically improve it?

If so you’ve probably hit what Andrew Chen calls the “Local Maximum”. The local maximum is a point in which you’ve hit the limit of the current design…it is as effective as its ever going to be in its current incarnation. Even if you make 100 tweaks you can only get so much improvement; it is as effective as its ever going to be on its current structural foundation.

The local maximum occurs frequently when UX practitioners rely too much on a/b testing or other testing approaches to make improvements. This type of design is typified by Google and Amazon…they do lots and lots of testing, but rarely make large changes. (Except, of course, Google’s homepage background change this week, which was quickly reverted)

While a cycle of smaller improvements is better than the dysfunctional design processes most of us are stuck with, one of the criticisms of this type of extreme optimization is that it’s always and only incremental: you can only make a few small changes at a time and therefore your design evolves slowly. And if you’re doing rigorous testing, by only changing one variable at a time, then you’re only changing one small part of your application in each iteration. This work cycle becomes dependent on how fast you can run tests. For Google and Amazon, who are blessed with millions of visitors per day, this is no problem because they can run tests extremely quickly. For most people building web sites, low traffic volume can be a huge hurdle because it means that tests have to run longer and thus slows down rate of iteration.

To illustrate the notion of local maxima Chen uses the example of a photo upload application, pointing out there are many ways to improve an offering by optimizing what currently exists. You can A/B test the current photo upload page, send out more emails reminding people to upload, add more calls-to-action to upload, etc. It’s easy to both design and test these options.

But after a while these low-hanging fruit get few and far between and as UX designer you have two choices: continue to try ever-increasing alternatives (optimize) that are small enough to test or to try and make a bigger, structural change that really shakes things up (innovate).

Chen points out that other approaches to improving a photo app besides optimization would probably have a higher return. These include:

Repositioning the product for a stronger value proposition

Going after a different kind of audience to target their needs

Recalibrating the “core mechanic” of the product to make uploading photos a natural part of using the product

Because these changes are much larger than a single design element you can effectively test, making a change to them requires making a daring design decision. Someone has to step up and take a chance based on their intuition: what they think will work instead of what testing has proven works.

In order to design through the local maximum we need a balance between the science-minded testing methodology and the intuitive sense designers use when making big changes. We need to intelligently alternate between innovation and optimization, as both are required to design great user experiences.

One strategy we might employ is to optimize until we reach a point of diminishing returns: design until changes just aren’t having a big effect. Then, stop optimizing and return to other kinds of analysis to figure out the next steps. Conduct interviews. Do user testing. Give surveys, ask questions. Find out the biggest existing pain points instead of focusing on tiny design elements at this stage. Focus at the activity-level. What are people trying to accomplish? What are their higher-level goals? What aren’t people doing that we want them to? What big hurdles keep them from taking the next action? This level of insight will allow you to make those bigger changes.

And when the time comes to make the bigger changes, when you decide to jump from your local maximum to some other design possibility, make the decision with conviction. But don’t forget that the optimization has only just begun.