We’ve just published a free Google Analytics checklist to help you analyze and optimize the performance of your landing pages.
In order to optimize your landing pages, you need to first analyze the numbers and take a good look at all the data available to you. Your goal is to understand how your landing pages are performing, find any leaks or opportunities in the funnel and identify any technical issues that need solving (e.g- loading time).
This Google Analytics checklist is the one we use to analyze our clients’ pages. We’ve updated it time and time again according to new abilities in Google Analytics, new reports and metrics that come out and we will be updating this checklist over time too.
Add your two cents:
What are the metrics and reports you look at to analyze your landing pages?
What MUST we have in our checklist?
Grab your Copy of Our Google Analytics Checklist
Data alone isn’t worth much without insights. Once you’ve found where the story is, it’s time to figure out what the story is and how you can optimize it. We’ve done exactly that in the complete guide to optimizing your landing pages: From data to strategy and design, we’ve mapped out the play-by-play to evaluating your pages and optimizing them. Check it out here.
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If it’s a in depth landing page and specifically for mobile segmentation I would look at scroll depth events to see how far users get.
Thanks for the input Anna! Scroll depth is often overlooked and can actually tell you a lot about what content visitors are looking for!
I’m with you @Talia… but unlike @Anna, I personally care more about “Scroll-Depth” on desktop than mobile. Because people usually scroll a lot more on mobile and I found this data less relevant.
Anyway, everything concern the visitor’s behavior is very useful for optimizing Landing-Pages.
Great insight about mobile! People do scroll a lot more on mobile. In fact, many times the goal is to stop them from “auto”‘scrolling and to focus on the USP/CTA which is why scroll maps can help in seeing if we achieve that.
I look at traffic source, bounce rate, and time on page.
Well not metrics but dimensions: working mainly with blogs I o use: word count (categorized), publishing date, scroll depth. I also calculate a quality score using bounce rate, time on page, scroll depth, comments and unique pageviews.
Great input about measuring a blog’s success Chris! Thanks!
Talia, for me it all depends on the specific goal of the landing page. But some quality metrics i might be looking at are: exit rate, avg. time on page, avg. session duration, pages/session, goal conv. rate
Think intent > What do I want a user to do, and make sure this is measured.
Some interesting reports for me: devices, age, device, browser and load times.
Thanks Arnout! I couldn’t agree more about tracking what’s necessary per landing page and according to its specific goals. As everything in marketing and conversion specifically, there isn’t just one thing that “always” works.
There is no single metric that shows us the performance when its isolated from another metrics 😉 But I like to stay focused, so first I look at bounce rate and time per session when landing page is new and we don’t have many conversions. After some time I focus on conversion rate, the number of conversions and page value. I like this last one, as it also gives me some clues about when (at which steps of the funnel) visitors browse this specific page. The higher the value is, the closer to purchase.
Many have been said so far, also about the segmentation. When I want to check what is the impact of my landing page / or specific piece of content/event on the overall funnel performance, I create two segments of visitors – who saw and didn’t see landing page (or event e.t.c.) Then it’s easy to compare them and see what the real impact is.
Love it Zuza! Thanks for sharing!
I usually make sure to track:
– Conversions (properly)
– Scroll Depth
– Page interactions (with video, popup, navigation if fixed, etc.) or just implementing Heatmaps
– Active time on page (Riveted plugin is a better way to do it)
And then I look at:
– Where are they coming from? (source, medium, campaign or keyword)
– Are they new or returning visitors?
– What device are they using?
– How much time does the page take to load? (page load time based on browser & device)
– How much time are they spending on the page? (which can be segmented based on the above)
– What are they doing? (interactions, conversions)
– Bounce rate
There can be a lot to look at but it’s important to find what’s relevant as soon as possible and not spend too much time over thinking or over analyzing. If you’re wrong about something you can always iterate.
Thanks for the in-depth answer Magda!
Many times people overlook page interactions such as pop up engagements, video or navigation – really glad you brought this up!
Looking forward to this checklist, T!
After a new page, page variant, or post has been live for 72 daytime hours, I first check for outlying data. This gives us a chance to identify and address any issues with hosting and serving the content. (Set broad estimates based on past experiments for key indicators like page load speed and source country. Extract data. Compare. Do a manual scan of any outliers.)
After that, I look at the frequencies of my data personas. Because I’m always testing, GA can become a time warp (and a fascinating one!) without this kind of structure.
For a landing page, for example, one persona is a prospect who arrived from a controlled (tracked) channel and performed a controlled action. Because I’ve identifies issued already, I can look at pathways now and do deeper analysis.
Third, if I have unanswered specific questions that will help to make ux and conversion test decisions, I dig for those last. I try not to spin up the data and “look for clues” nor to continually revisit it. Speaking to the data without a hypothesis in mind can make you tell stories that aren’t true!
Great answer Kelly! Thanks
Great post Talia! My favorite features have to be the cross domain tracking and user id. I love seeing the blog and our other websites influencing each other and users different interactions with each one.