Posted by Tom.Capper

One of these sessions is not like the other. Google Analytics
data is used to support tons of important work, ranging from our
everyday marketing reporting all the way to investment decisions.
To that end, it’s integral that we’re aware of just how that data
works.

In this week’s edition of Whiteboard Friday, we welcome Tom
Capper to explain how the sessions metric in Google Analytics
works, several ways that it can have unexpected results, and as a
bonus, how sessions affect the time on page metric (and why you
should rethink using time on page for reporting).

https://fast.wistia.net/embed/iframe/qrb30lk6iz?seo=false&videoFoam=true

How do sessions work in Google Analytics?

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Video Transcription

Hello, Moz fans, and welcome to another edition of Whiteboard
Friday. I am Tom Capper. I am a consultant at Distilled, and today
I’m going to be talking to you about how sessions work in Google
Analytics. Obviously, all of us use Google Analytics. Pretty much
all of us use Google Analytics in our day-to-day work.

Data from the platform is used these days in everything from
investment decisions to press reporting to the actual marketing
that we use it for. So it’s important to understand the basic
building blocks of these platforms. Up here I’ve got the absolute
basics. So in the blue squares I’ve got hits being sent to Google
Analytics.

So when you first put Google Analytics on your site, you get
that bit of tracking code, you put it on every page, and what that
means is when someone loads the page, it sends a page view. So
those are the ones I’ve marked P. So we’ve got page view and page
view and so on as you’re going around the site. I’ve also got
events with an E and transactions with a T. Those are two other hit
types that you might have added.

The job of Google Analytics is to take all this hit data that
you’re sending it and try and bring it together into something that
actually makes sense as sessions. So they’re grouped into sessions
that I’ve put in black, and then if you have multiple sessions from
the same browser, then that would be a user that I’ve marked in
pink. The issue here is it’s kind of arbitrary how you divide these
up.

These eight hits could be one long session. They could be eight
tiny ones or anything in between. So I want to talk today about the
different ways that Google Analytics will actually split up those
hit types into sessions. So over here I’ve got some examples I’m
going to go through. But first I’m going to go through a real-world
example of a brick-and-mortar store, because I think that’s what
they’re trying to emulate, and it kind of makes more sense with
that context.

Brick-and-mortar example

So in this example, say a supermarket, we enter by a passing
trade. That’s going to be our source. Then we’ve got an entrance is
in the lobby of the supermarket when we walk in. We got passed from
there to the beer aisle to the cashier, or at least I do. So that’s
one big, long session with the source passing trade. That makes
sense.

In the case of a brick-and-mortar store, it’s not to difficult
to divide that up and try and decide how many sessions are going on
here. There’s not really any ambiguity. In the case of websites,
when you have people leaving their keyboard for a while or leaving
the computer on while they go on holiday or just having the same
computer over a period of time, it becomes harder to divide things
up, because you don’t know when people are actually coming and
going.

So what they’ve tried to do is in the very basic case something
quite similar: arrive by Google, category page, product page,
checkout. Great. We’ve got one long session, and the source is
Google. Okay, so what are the different ways that that might go
wrong or that that might get divided up?

Several things that can change the meaning of a session1. Time zone

The first and possibly most annoying one, although it doesn’t
tend to be a huge issue for some sites, is whatever time zone
you’ve set in your Google Analytics settings, the midnight in that
time zone can break up a session. So say we’ve got midnight here.
This is 12:00 at night, and we happen to be browsing. We’re doing
some shopping quite late.

Because Google Analytics won’t allow a session to have two
dates, this is going to be one session with the source Google, and
this is going to be one session and the source will be this page.
So this is a self-referral unless you’ve chosen to exclude that in
your settings. So not necessarily hugely helpful.

2. Half-hour cutoff for “coffee breaks”

Another thing that can happen is you might go and make a cup of
coffee. So ideally if you went and had a cup of coffee while in
you’re in Tesco or a supermarket that’s popular in whatever country
you’re from, you might want to consider that one long session.
Google has made the executive decision that we’re actually going to
have a cutoff of half an hour by default.

If you leave for half an hour, then again you’ve got two
sessions. One, the category page is the landing page and the source
of Google, and one in this case where the blog is the landing page,
and this would be another self-referral, because when you come back
after your coffee break, you’re going to click through from here to
here. This time period, the 30 minutes, that is actually adjustable
in your settings, but most people do just leave it as it is, and
there isn’t really an obvious number that would make this always
correct either. It’s kind of, like I said earlier, an arbitrary
distinction.

3. Leaving the site and coming back

The next issue I want to talk about is if you leave the site and
come back. So obviously it makes sense that if you enter the site
from Google, browse for a bit, and then enter again from Bing, you
might want to count that as two different sessions with two
different sources. However, where this gets a little murky is with
things like external payment providers.

If you had to click through from the category page to PayPal to
the checkout, then unless PayPal is excluded from your referral
list, then this would be one session, entrance from Google, one
session, entrance from checkout. The last issue I want to talk
about is not necessarily a way that sessions are divided, but a
quirk of how they are.

4. Return direct sessions

If you were to enter by Google to the category page, go on
holiday and then use a bookmark or something or just type in the
URL to come back, then obviously this is going to be two different
sessions. You would hope that it would be one session from Google
and one session from direct. That would make sense, right?

But instead, what actually happens is that, because Google and
most Google Analytics and most of its reports uses last non-direct
click, we pass through that source all the way over here, so you’ve
got two sessions from Google. Again, you can change this timeout
period. So that’s some ways that sessions work that you might not
expect.

As a bonus, I want to give you some extra information about how
this affects a certain metric, mainly because I want to persuade
you to stop using it, and that metric is time on page.

Bonus: Three scenarios where this affects time on page

So I’ve got three different scenarios here that I want to talk
you through, and we’ll see how the time on page metric works
out.

I want you to bear in mind that, basically, because Google
Analytics really has very little data to work with typically, they
only know that you’ve landed on a page, and that sent a page view
and then potentially nothing else. If you were to have a single
page visit to a site, or a bounce in other words, then they don’t
know whether you were on that page for 10 seconds or the rest of
your life.

They’ve got no further data to work with. So what they do is
they say, “Okay, we’re not going to include that in our average
time on page metrics.” So we’ve got the formula of time divided by
views minus exits. However, this fudge has some really unfortunate
consequences. So let’s talk through these scenarios.

Example 1: Intuitive time on page = actual time on page

In the first scenario, I arrive on the page. It sends a page
view. Great. Ten seconds later I trigger some kind of event that
the site has added. Twenty seconds later I click through to the
next page on the site. In this case, everything is working as
intended in a sense, because there’s a next page on the site, so
Google Analytics has that extra data of another page view 20
seconds after the first one. So they know that I was on here for 20
seconds.

In this case, the intuitive time on page is 20 seconds, and the
actual time on page is also 20 seconds. Great.

Example 2: Intuitive time on page is higher than measured time on
page

However, let’s think about this next example. We’ve got a page
view, event 10 seconds later, except this time instead of clicking
somewhere else on the site, I’m going to just leave altogether. So
there’s no data available, but Google Analytics knows we’re here
for 10 seconds.

So the intuitive time on page here is still 20 seconds. That’s
how long I actually spent looking at the page. But the measured
time or the reported time is going to be 10 seconds.

Example 3: Measured time on page is zero

The last example, I browse for 20 seconds. I leave. I haven’t
triggered an event. So we’ve got an intuitive time on page of 20
seconds and an actual time on page or a measured time on page of
0.

The interesting bit is when we then come to calculate the
average time on page for this page that appeared here, here, and
here, you would initially hope it would be 20 seconds, because
that’s how long we actually spent. But your next guess, when you
look at the reported or the available data that Google Analytics
has in terms of how long we’re on these pages, the average of these
three numbers would be 10 seconds.

So that would make some sense. What they actually do, because of
this formula, is they end up with 30 seconds. So you’ve got the
total time here, which is 30, divided by the number of views, we’ve
got 3 views, minus 2 exits. Thirty divided 3 minus 2, 30 divided by
1, so we’ve got 30 seconds as the average across these 3
sessions.

Well, the average across these three page views, sorry, for the
amount of time we’re spending, and that is longer than any of them,
and it doesn’t make any sense with the constituent data. So that’s
just one final tip to please not use average time on page as a
reporting metric.

I hope that’s all been useful to you. I’d love to hear what you
think in the comments below. Thanks.

Video
transcription
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