Case Study 1:

Navigation in Disarray

Posted on March 2nd, 2017

Our mega menu was a mega mess. It contained 115 different links. We had multiple complaints from users that they could not use the navigation. There was no hierarchy and the structure was confusing. Based on our Google Analytics most menu items were receiving less than 50 clicks per year. That is less than 0.01% of clicks for the year. We needed to find a fix that was quick and efficient.

Menu problem Notice how many menu items below "About Us" could be consolidated. The abundance of so many options will confuse the user.

Our immediate fix was to remove the 50+ links in the menu that were less than 0.01% of clicks per year. Although this seems easy on the surface, we had to consolidate the content of those links and either remove or combine the content with other areas of the site. Although this solution sounds correct it brings up a rather large conundrum. Were the unpopular links not what users wanted or did users want that information but couldn't find it due to the chaos of the menu?

Menu Fix The menu is now balanced and items have been consolidated.

There is no simple way to find the answer to that question outside of extensive user testing. But our resources were limited to Google Analytics, Hotjar recordings, and the waiting game. We waited and watched our analytics to see if any data improved. Which it did! Our exit rate dropped by 10% and our average time on page increased by 25%! We also watched our hotjar recordings to see how users interacted with the site. These recordings provided enough insight to see that our users were having an easier time finding the information they were looking for right away.

The project gave us enough information to come away with these key takeaways:

  • Keep an eye on your Google Analytics, those low numbers can always be approved upon
  • Consolidate content whenever you can. Dividing content into separate "islands" only causes confusion
  • Watch how real users are using your site and look for patterns in your observations
  • Experiment; if you think something will work, TRY IT! If you have the data to back up an experiment, go for it!