Google Analytics and the 5 Lies that has you fooled

Google analytics is a super cool tool. We love using it and generally recommend our clients mainly because of the ability to integrate it with remarketing and other Google products as well as easy UTM tagging. Oh yeh, its free too!

This doesn’t mean we are not learning new things or that we know it absolutely back to front. Like everyone we make mistakes and sometimes these mistakes don’t even seem like mistakes! It’s just that Google Analytics simply fooled us and probably fooled you.

Lets look at its little tricks (we call them ‘lies’) to confused even the best of us:

1. The attribution model lie

Ah the good old attribution model in Google Analytics. When I first started linking up Adwords and using Google Analytics to measure conversions for clients something really bizarre happened. Conversions didn’t match.

Same date range, same campaign, same everything. Different conversions when comparing Analytics and Adwords.

We initially put this down to the variance Analytics has when it comes to measuring traffic. However when studying the data, clicks, traffic and costs numbers we aligned. Why then would conversions suddenly be so different?

It’s all about the attribution model Google Analytics uses as a default. Google Adwords, Facebook and pretty much every other channel that requires a pixel to fire as a default uses a 30 day pixel life to track a conversion. That is, when someone clicks on your ad in Adwords they are cookied. A conversion will fire when they take action as long as that cookie is still on their browser (usually 30 days).

That means a user can click on an ad on the 1st July, visit your site through organic, direct and social over the next few weeks and then finally convert on the 25th July after directly coming to your site and Google Adwords will claim it.

Analytics doesn’t not look at it like that. Analytics, as a default, uses what we call a linear attribution model. This means that all the other sources that brought a user back to your site are evenly included in the calculation as to how many conversions are attributed to that source. A linear attribution gives an equal weight to all the channels a user may have used to visit your site until the convert, with Adwords simply taking up a portion. Hence why there is a variance.

Of course there are ways to fix this, either through changing your attribution in Analytics or changing the attribution modelling in Adwords.

To change your attribution settings in Analytics you can go to Admin > go to the ‘View’ and then adjust your attribution settlings. See image below:

edit google analytics attribution modelling

You can now also change your Google Adwords attribution modelling. Here is a good resource on how and where to do it.

2. The direct traffic lie

Don’t let direct traffic fool you. Direct traffic means that someone has visited your website directly through typing it into their browser. Or does it?

There is more to it than that. An abundance of direct traffic may not necessarily mean that the new display campaign is leading to fantastic branding so people are jumping online and coming back to your website. If it seems too good to be true then chances are it is.

There are many reasons why traffic will be regarded as ‘direct’ even if they are not direct at all.

A few possible reasons for an influx of direct traffic:

  • Lack of url tagging – sometimes when you have an ad placement on a 3rd party the referral attribution may be lost. This is especially prevalent for users browsing on their mobile, where a clicked link may open a new browser and have the url directly put in the url. If Google cannot define the referrer and there is not ‘UTM’ or identifiable tag in the link, most likely it will be thrown in the direct pile.
  • Redirects – If your website has a redirect when the traffic lands on a page, this redirect will generally wipe out any acquisition or referrer data and stick it into the ‘Direct’ pile most of the time. Check your landing pages for even the slightest redirect. This could also fall into the referrer section which isn’t much better than direct to be honest.
  • Cross domains – If your traffic is taken away from your site and/or removed off your Google Analytics property ID, without proper cross domain tracking implemented, the traffic coming back may be put in the direct traffic pile (or the referrer pile). Check for any cross domain movement or the traffic moving away from your Google Analytics property (your UA number).
  • HTTPS to HTTP and vice versa – do you have a section of your site with is encrypted with an ssl certificate? If you do and your traffic moves from the http and https versions then it could be the reason why your direct traffic is going through the roof. When there is a switch from http to https this will mean that all your url tagging and previous referrer information will be stripped as part of the encryption. This will also mean that your concerting traffic is all referrer traffic or direct traffic which is a concern when trying to measure the best performing traffic sources.

3. Counting yourself (the Ego Lie)

Ever notice how the harder you work on your website the more traffic you immediately get? It’s like almost too good to be true right? Well… you know where I’m going here.

One of the most common things I see when auditing or looking through a client’s Analytics account is how they don’t filter their own IP addresses. I’ve seen organisations of hundreds if not thousands of people with no IP filter!

Yes its good for the numbers, stakeholders etc but who are you kidding? It’s easy to get your view filters in order and if you want the hero shot, leave the ‘All Website Data’ as it is but ensure you have a filtered view so you can really analyse your website traffic. Otherwise you have some really eager return visitors who like to read 10+ pages per visit.

Filtering your IP is super easy to do. Login to your Google Analytics and head to ‘Admin’.. go to the View Filters section (far right) then add your filter.

For IPs it should look something like this:

Filtering IP Google Analytics

Do it for work, home, staff homes etc so you filter out as much ‘ego’ traffic as possible. Jimmy’s looks something like this:

Ip filtering analytics

Do this for an many known IP addresses as you can and also use this opportunity to remove Spam referrers… which gets me to.

4. Referral Spam Lie

If you haven’t seen referral spam coming through then you obviously have not been looking into your Analytics! Over the last 6 months it has absolutely exploded. Not one single Analytics account that I know of doesn’t have some sort of referral spam infiltrating the data.

In the example below, when you quickly look at the acquisition channels it looks pretty normal. It actually look quite clean with a good number of referrals, direct traffic, organic traffic and more:

acquisition channels google analytics

All looks good right? Hmm.. lets dig deeper in that Referral channel:

Screenshot 2015-07-17 10.15.18

Hello referral spam.

Obviously I filtered all the others for this purpose but this is just an example of some of the referral spam getting through. There are hundreds of others.

The good news is that you can do something about it. First thing you could do is wait (not ideal) because Google is working on a solution for this. However if you can’t wait then you can do some manual labour to get it sorted. Don’t worry, its easy to do.

In the “3. The Ego Lie” example above where you can filter the IP you can do the same for urls that are coming in as referral spam. Here is an example I’ve done below:

referral spam removal excluse

Don’t use the Tracking info -> Referral Exclusion List in your property settings as this will just come in as Direct traffic and that’s even worse.

There is a good list of known referral spam websites here.

If you want to learn more about removing spam and bots from your Analytics then read Himanshu Sharma’s post. In fact for anything technical around Google Anlaytics Himanshu is the guy.

UPDATE: Here is a super easy way you (99% of you) can stopĀ referrer spam in about 30 seconds.

5. The Google Analytics session lie

A session is a session.. uno.. a visit.. right? Well that’s where Google Analytics has you fooled. A session used to be a visit but now it isn’t simply a ‘visit’. Nor is it a visitor. Google explains it like this:

A session is a group of interactions that take place on your website within a given time frame. For example a single session can contain multiple screen or page views, events, social interactions, and ecommerce transactions.

Pretty straight forward right? Did you see it though? Have a look again.. first line. The bit that just slots in there without much notice that the sneaky “within a given time frame“. As a default this given time frame is 30 minutes of inactivity.

So why is this a problem? Well what it means is that leaving it as a default could give you some data that is not necessarily true or doesn’t show you the information you need to know.

For example if you have a streaming website where the content like a podcast is 40minutes on average then you best users will have a 100% bounce rate and for every you would be seeing several.

A couple of things I’ve seen go wrong when your session times settings do not match your content:

  • High bounce rates recorded for good quality traffic
  • The appearance that your users keep coming back as return visits giving the impression of great user engagement. I see this a lot with big media buys or pop under traffic, where a user clicks and then only sees your landing page a while later resulting in multiple sessions masking the negative user experience.

There are lots more but its important to note how your site experience should be reflected in your session settings.

I’m sure you have experienced a few more Analytics lies or maybe I’ve cleared a few things up as you could be trying to work out the above problems as we speak.

If you think of anymore feel free to post it in the comments section below.

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