For the purposes of this guide, 'page data' refers to the metrics describing user visits to any individual website page. A few of these numbers are covered by your conversion funnels (users, sessions, and the calculated conversion rate) but with page data we can find metrics that give more detail than just whether a user was present or not.
Whilst a conversion funnel should represent your primary metrics, these numbers can form your secondary metrics. If you make a change that doesn’t improve conversion but it does lower bounce rate, you’ve seen a beneficial secondary effect.
These secondary metrics are worth studying to build a better picture of user behaviour on your website and can help you define what to look for in research such as user testing. For example, if you find a key information page has a high exit rate, it should be a task in your next user test to try and understand why.
To learn what is happening on your web pages I recommend installing Google Analytics, which is by far the most popular tool for tracking this kind of data. Once installed, the following are a few pieces of key page performance data to consider:
Defined as how many separate sessions of browsing a user has had on your page. A user visiting your page multiple times in a session would only record one unique page view—a session of browsing is reset after 30 minutes of inactivity.
Each session represents a period of intent for a user to achieve something on your site and isn’t the same as an individual user (this is something Google Analytics can only guess at so doesn’t display).
Defined as how many times a page (defined as a URL loading) has been viewed. If this is a lot higher than your number of unique page views then you'll know that each user is looking at that page many times each time they visit. This could suggest that the content on the page is so great they keep returning to it or that they can't work out where to go next.
Defined as the average time a page view lasted. This is often used as a measure of engagement with a web page but it will depend on the type of page as to whether you want this to be long or short.
If it's a long blog article or 'about us' page you'll be hoping for users to spend several minutes on it, whereas if it's a checkout page, you'll be wanting people to whizz through in seconds. If it's the other way around then users aren't being intrigued by your content in the former and they're probably getting stuck working out how to enter payment details in the latter.
Defined as the percentage of sessions that saw someone land on this page and then leave without visiting another page on your site. This is almost always seen as a ‘negative’ metric that you want to reduce and is most applicable to landing pages. If bounce rate is high on a homepage or landing page then your entrance experience to your site is turning people off, which is a strong sign that you should change something.
Defined as the percentage of sessions that saw someone leave your site on this page. Not to be confused with bounce rate, this is a bit more ambiguous, as the user could have visited several other pages before their exit and they may have found everything they needed. After all every journey has to end somewhere.
Obviously you won’t mind if the exit rate is high on pages that appear after a goal (like a post-purchase page). If it's high on a critical page in the middle of your flow, it's worthy of further investigation.
This is tracking that you manually set up for non-URL based interactions and tells you whether or not an event has been triggered (such as clicking a button or page element). Whilst it is a binary metric you can attach meta data to each event to give you more details, such as the name and type of button if there are multiple on a page.
It can be hard to find benchmark metrics for what represents a ‘good’ number of users or bounce rate, so take it with a pinch of salt when someone makes a blanked declaration that you should be targeting a certain figure. It’s more reliable to use this data to judge your pages in relation to each other or themselves over time. Use it to help you prioritise which pages need fixing before others and for spotting outliers and problems.
Looking at the raw metrics is a fine starting point for discovering website issues but to get more actionable data you need to segment your results. Try segmenting by device or by traffic source to see if users behave differently depending on where they’ve come from and how they’re viewing the page.
Be careful if you’re using regular expressions to track groups of pages via the Google Analytics API and looking at the totals. Due to the way users and sessions are counted by URL there may be some duplication in there because people who visited several pages may be counted multiple times. Use it as an indicative measure rather than a precise one.
Don’t make assumptions on what a piece of page data in isolation might mean. As outlined above, other than bounce rates, most metrics could be positive or negative depending on the context.
My standard disclaimer with quantitative data applies: it doesn’t tell you ‘why’ something is occurring. Always investigate further with qualitative evidence such as heatmaps, session recordings, and remote user tests, to build a more complete picture.
As mentioned above, I'd recommend Google Analytics (free) for gathering this data, because it's free and hugely popular around the world (so you'll be able to compare your stats across different sites and find plenty of help guides). If you're tracking something that isn't based on page views, such as a native app, then a lot of the above metrics won’t apply.
Once your tracking is setup, checking the data for a page takes only minutes. If you regularly track the same few stats then I recommend pulling it into a dashboard.
A template for creating a lightweight report on a website's analytics. 15 pages including sections for summarising the important findings from quantitative data, heatmaps, and visitor recordings.