When Apple announced iOS 17 this year, there was an outcry among many marketers. The main reason for this was the announced “Link Tracking Protection (LTP)”, which is intended to automatically remove tracking parameters from URLs. The big misunderstanding was mainly due to the fact that many assumptions, including UTM parameters, would be removed. After a quick check, I was able to give the all-clear (see LinkedIn: Apple is ending performance marketing?!” > Bullshit!).
With about a 6-month “corona delay” I would like to finish my series of articles “Flying blind through online attribution”. When I published the first articles at the beginning of the year, nobody could foresee how the whole topic would develop. But the involuntary pause has something good about it – a lot has happened and a certain development can be seen.
In the previous articles on the subject of “Flying blind through online attribution“, I primarily dealt with the technical causes. In this article, I would now like to look at the topic from a legal perspective.
October 1, 2019 is the day that many online marketers will remember. It was the day on which the EUGH made a groundbreaking ruling – the so-called “cookie ruling”. Although the proceedings did not directly deal with cookies, but rather with how the defendant (Planet49) obtained the consensus, the ruling has since caused some headaches in the online marketing world. The court made it clear that consent for the setting of cookies must be voluntary and active (opt-in).
Modified image of “11 Reasons Why You Should Use Google Tag Manager”
First of all: I am a big fan of the Google Tag Manager (GTM). It’s a great and powerful tool that – with the right training – saves developers a lot of work and can outsource many tasks to marketing.
But what exactly is a tag manager? According to the Ryte Wiki, a “tag management is a possibility in online marketing to exchange tags automatically with the help of special containers without having to intervene in the programming of a website every time a change is made”. Usually a tag manager is embedded into the page by JavaScript. Afterwards you can integrate any other JavaScript or other tags via a web interface and then control the delivery via conditions and rules. Side note (important for later): Usually the tag manager is delivered via a cookie-less domain – i.e. the tag manager itself does not need its own cookies.
“Single Point of Failure”
As practical as tag managers (e.g. GTM) are, there is one problem: they represent a “single point of failure” for online attribution. If this fails, e.g. by being blocked, all tracking behind will also fail. Now one could argue that if GTM is blocked, all other trackers will be blocked anyway – this is also true for the most popular trackers like Google Analytics, Facebook, Pinterest, eTracker & Co. But it is not the case for less popular services and tracking solutions, like direct cooperations or custom events.
The Brave Browser blocks GTM by default
So there are some smaller affiliate networks or even private programs that are not on the blocking list of Easylist & Co. Also direct cooperations, e.g. our tracking pixel which is available for our partner shop in the course of order data processing (DSGVO), are not on the blocking list. However, if these tracking pixels are integrated via the GTM, they are – so to say – still indirectly blocked.
Unfortunately, it is not possible to determine the extent of the problem or the failure rate in general. It depends on which third-party services you use (whether they are blacklisted) and on the AdBlock rate of the respective site. But Tom Capper provides an interesting analysis with his article “How Much Data Is Missing from Analytics? And Other Analytics Black Holes“. According to his measurements, the tracking deviation when using GTM is about 5%.
Difference between tag-manager vs. tracking-switch
But the so-called “cookie- or tracking switches” have a much greater effect on the online attribution than tag managers.
“A tracking switch is a container that manages multiple code snippets (tracking pixels and scripts) and publishes them according to pre-defined rules. The code of the turnout must be integrated on all sub-pages and internally the possible transaction pixels (e.g. different affiliate pixels, Facebook and AdWords conversion pixels) must be stored. In addition, rules must be defined that determine the hierarchies between the individual pixels (attribution model).” Source: Projecter-Blog (German)
Similar to the Tag Manager, a tracking switch is a kind of container. While a tag manager is typically integrated on the entire page and primarily controls individual (additional) functions, the tracking switch takes care of the attribution. Its use therefore usually begins BEFORE the website and ends when an action (for example, purchasing) is completed. A tag manager usually does not need a cookie or other functions to recognize a user – a tracking switch does. It is almost always based on some kind of identification feature (cookie, fingerprinting etc.).
Simplified representation of a tracking switch and container
The diagram shows that the tracking switch is usually called up before the shop – in other words, the merchant does not use direct links to his offers in his paid channels, but uses tracking links from the tracking provider he works with.
When the tracking switch is called, this is recorded and usually written to a cookie together with the source of the campaigns. If a goal is reached (e.g. purchase), the tracking container of the tracking provider checks whether and where the customer comes from – for this purpose it reads the set cookie. Based on the source, further tracking pixels are then triggered “on-the-fly”.
Admittedly – this representation is an extremely simplified model of reality and represents only the simplest variant based on a 3rd party cookie. In reality, there are several, much more complex solutions to either improve tracking or simply to bypass anti-tracking solutions (e.g. ITP/ETP). These include fingerprinting, local storage, 1st party cookie via JS include or CNAME or server side tracking, master tags etc.
But the basic procedure does not change much – the tracking switch identifies the source and the user and decides on the attribution at the destination based on this information.
What’s the trouble?
There is a variety of tracking solutions that are offered either as a standalone solution or as an additional feature of other products – be it eTracker, DoubleClick, Ingenious Technologies or multichannel providers such as Channelpilot, DataFeedWatch etc. Also, agencies and service providers sometimes provide their own developments. The problem does not even arise with the solutions themselves, but rather with the lack of maintenance or adaptation to new conditions – not by the providers, but by the customers themselves!
After all, a good tracking solution continuously adapts to market conditions and reacts to changes: ITP/ETP, SameSite cookie update, 3rd party cookie problems – just to name a few challenges of the last months. However, not all adjustments can be made exclusively on the part of the tracking service provider – in some cases the customer must also react and, if necessary, revise an already implemented solution. Master tag or CNAME tracking is a good example.
However, many customers shy away from the effort or delay the adjustments with the risk of getting into a “single-point-of-failure” situation – if the tracking switch fails – for example due to privacy blockers – the entire underlying attribution is lost.
What should I do?
As mentioned at the beginning I’m a fan of the Google Tag Manager – in my opinion tracking switches & containers are a good thing, too. BUT: You shouldn’t think that you have to include them only once and then let the tracking provider manage everything. You have to implement recommended updates – even if they are more extensive. Otherwise you risk a creeping loss of tracking quality, which then affects attribution and thus decision-making, especially in paid channels.
A common phenomenon is that mobile traffic converts worse than desktop traffic. There are dozens of articles (or here) and extensive studies on the net.
The difference is often explained by the fact that the screen is too small and navigation is difficult. The user likes to get information on mobile, but then switches to the desktop device and makes the purchase. This is the so-called “cross-device problem“. In this article I would like to not only talk about this problem, but especially about the rather underestimated “cross-browser problem”.
Comparison of usage time and completion rate Desktop/Mobile
Every one of us online marketers knows it and every one of us has used it before – the “private” or “incognito” mode. In the private mode of the browser you surf without leaving permanent traces. The browser history and form data are not stored and cookies, as well as website data, are deleted after the application is closed. So far everything is known – but what influence does the private mode have on the attribution of conversions and, above all, how widespread is its use?
First of all: This article is about the perspective of an online marketer. Of course, neither privacy nor AdBlock are dangerous for users. On the other hand for online marketing and the correct assignment of the conversion to the channels a type of danger exists.
When I talk to colleagues from online marketing about tracking and attribution, we quickly come across ad blockers, GDPR, cookies and the associated increased requirements. If you go a little deeper into the discussion and look at the traffic channels, it turns out that for many, the channels SEO and Direct are the most successful – i.e. most conversions result – while the paid channels (e.g. SEA and social) stagnate or even go back. The great success of the direct channel is then explained by their strong brand.
We at everysize can also confirm this statement based on our Google Analytics numbers. But why is that? Out of curiosity, I went into the matter and took a closer look at the reasons for our high proportion of conversions in the direct channel. I came across some very interesting aspects, including: The more we scale our paid channels, the higher our traffic and especially our conversion numbers in the direct channel increase!
Recently I had an interesting exchange with an online store. It was about the deviation of his Google Analytics reports with the numbers delivered through his marketing channels. In his case, this involved PSMs (price-comparisons), his social channels (Facebook, Instagram) and his affiliate program. Due to the breadth of marketing channels in which the problem occurred, I became curious. So I asked him if he also got some payment service providers such as Paypal, Sofortüberweisung etc. in his Google Analytics reports. When he said yes, I knew immediately where the problem lies.
