About a month ago, we redid the FullStory home page. We were excited about the new design, but it dawned on us that we didn’t have a particularly clear sense of how our customers were better served by it–which is the point of a redesign, after all.
I wanted to better understand Google Analytics anyway, so I took the opportunity to explore GA by using it to analyze the performance of our new home page.
Full disclosure: I’m new to GA and analytics in general. I was muddling around with GA when I discovered a blog post from FastCompany that mentioned Google’s Page Analytics Chrome extension. For a visual person like myself, the extension’s in-place annotations were much easier to use than the standard GA web UI, and I was able to quickly gain some data on how our new home page was performing.
I thought I’d share what I learned. First, I’ll go over how to setup and use the Google Analytics Chrome extension so that you can do it, too. Later, I’ll summarize the broad insights we gained on our new home page.
Sign up and install Google Analytics.
Install the Chrome Extension.
Make sure the page(s) you want to view includes your customized GA script, and then turn on the extension. You should see something like this:
Apples to Apples.
Google Analytics doesn’t show the home page as it looked prior to our updates, so we weren’t able to do a visual before/after comparison. Oh, if only such a magic tool existed … one that retained historical versions of a website and all customer interactions — imagine that! But I digress…
As long as the URL (e.g. https://www.fullstory.com) for your home page is the same and the paths (e.g. /signup, /pricing, /features) you are analyzing haven’t changed, you can do an apples-to-apples comparison by simply configuring the date range. In the bottom left corner of the data panel, you can alter the date range. In this case, I set the range to start at our initial public launch and end the day before the home page revamp.
To see what happened post-revamp, I simply changed the date range to start the date the new home page went live and extend through today.
What am I Seeing.
Note the orange box; this is the percentage clicked for all HTML elements that navigate to the same path. It shows up in the upper left corner of the element. Multiple elements (e.g. the “Free 14-day trial” and “See Pricing & Sign Up Free” buttons) will roll up to the same percentage since they both navigate to the /signup path.
Hovering over one of the elements will give you a detailed breakdown including click counts as well as showing you how many other elements on the page are linking to the same path. Also, the data is page sensitive so navigating to, say, the “Features” page will show a different percentage breakdown.
A quick tip regarding the GA data panel: right click on the Page Analytics extension button in Chrome and go to Options. Set the data panel setting to Bottom. Okay, now that the data panel is no longer blocking the primary nav of your website, let’s go over the data panel.
We discussed earlier the handy date range selector, useful for the purposes of this blog post. But taking things a step further, you can scope and filter by various visitor criteria such as New Users, Returning Users, etc. These segments are available to the far left of the data panel. You can play around with the segment setting as well as choosing a metric to display on any of the other columns by clicking on the small down arrow at the top of the column. In any case, the data panel is a high-level summary for the page so you can visually pick up on trends through the graph at the bottom of each column.
Finally, we can enjoy the fruits of our labor simply by comparing the before and after numbers. Here are some insights that stood out by doing this exercise:
- Signups are slightly up (8.6% vs 7.7%)
- More people are heading to the Pricing page (7.7% vs 2.3%)
- Visits to our Features page went way down (6.9% vs 22%), but of those who did see the Features page, many more of them visited the Pricing page in subsequent clicks (26% vs 11%). Why? One possibility is that the new, more detailed home page better motivates visitors to jump straight to the Pricing or Signup page rather than doing a deep dive into FullStory’s features. This was, indeed, one of our goals for the redesign, but we would need more fine-grained session data to properly test this hypothesis; GA really only provides aggregate data, which doesn’t provide conclusive evidence.
In Part 2, we will show how FullStory can zoom-in from the forest (i.e. GA aggregated data) into the trees (i.e. FullStory fine-grained data) to provide more actionable insights.