In digital marketing, especially in e-commerce brands, remarketing is carried out to accelerate the purchase (conversion) journey of users and to finalize their unfinished purchases.
For remarketing, in addition to classic advertising platforms such as Facebook and Google, third-party platforms, which we will focus on in this article, are also primary tools.
So how these third-party platforms work and how are they evaluated?
Third-party platforms target users who have interacted with your site before and show them the products they viewed/added to their cart or the creatives preferred by the advertisers (campaign banners, etc.) in their contracted inventory (news sites, dictionaries, etc.). Thus, it aims to remind users of brands and products in various inventories and to complete the purchase journey of users.
While evaluating these platforms, the ROAS (Return On Ad Spend) metric is primarily evaluated by using the last non-direct attribution model, usually through measurement tools such as Google Analytics, although it varies depending on preference.
So what are these attribution models?
Attribution models are methods that assign a share to each step that users interact with the site in their purchasing journey, according to its importance in purchasing, and thus enable us to evaluate the performance of interaction steps (channel, source, medium, campaign, etc.).
The most used attribution models:
- In the Last Interaction attribution model, the last touchpoint would receive 100% of the credit for the sale.
- In the Last Non-Direct Click attribution model, all direct traffic is ignored, and 100% of the credit for the sale goes to the last channel that the customer clicked through from before converting.
- In the Last Google Ads Click attribution model, the last Google Ads click would receive 100% of the credit for the sale.
- In the First Interaction attribution model, the first touchpoint would receive 100% of the credit for the sale.
- In the Linear attribution model, each touchpoint in the conversion path would share equal credit for the sale.
- In the Time Decay attribution model, the touchpoints closest in time to the sale or conversion get most of the credit.
- In the Position Based attribution model, the credits of the interactions are allocated depending on the positions. For example, X% credit is given to the first and last interactions, while (100-2X)% credit is distributed to the middle interactions. ”(1)
Among these attribution models, the model we will focus the most in this case will be the Last Non-Direct Click attribution model, which is also used by Google Analytics and the most used measurement tool in the world.
We explained what remarketing is, how and with which tools remarketing is done, how the performances of third parties are evaluated and what are these attribution models. To summarize, one of our most important weapons in the purchase journey of users is remarketing, and we can do remarketing in many ways, we consider ROAS as the determining basis and we measure our results with Google Analytics. Google Analytics, on the other hand, uses the Last Non-Direct click attribution model, which does not consider direct traffic.
Let's talk about why we are explaining these. We have examined two third party platforms, which are the most widely used in Europe and whose names you can recall by now. Based on all this information, you can be sure that the results will change your view of these advertising platforms.
First, let's see if the conversions attributed to these two platforms are actually last click or are they written to these platforms because of the attribution model used, although the last interaction is a direct channel. We can use the "Direct Session" dimension in Google Analytics to look at this. This dimension tells us whether a sale came from direct or actually came from that channel's last click.
When we examine the Direct Session breakdowns of these third parties, the result we see is extremely interesting.
As can be e in the table, the rate of direct last click sales is 39% in Third Party B, while it is 19% in Third Party A.
This means, for Third-Party A only 1 out of 5 purchases actually came directly from users who last clicked on this channel, while 2 out of 5 transactions for Third-Party B are truly last clicks.
So, could the results be due to the proximity of users to the buying action, but not these platforms?
To answer this question, we examined similar audiences on Facebook with the same method and showed that the results were due to platforms, not the funnel.
The direct last click conversion rate of remarketing targeted ads on Facebook is 63%.
So, for Facebook, actually 3 out of 5 purchases are coming directly from users who last clicked on this channel, which clearly shows the difference.
Let's dig deeper into this analysis and look at the conversion path reports of these sales. These reports help us to see the interaction steps of each conversion journey and at what stage the relevant channel is positioned in these journeys.
When we examined the transformations that included Third-Party A and Third-Party B channels by considering direct traffic, we saw that the results we encountered confirmed our hypotheses in this area.
Back to the results, the rate of having a direct path before the third-party path in a user's purchasing journey is 62% for Third-Party A and 67% for Third-Party B, on average.
In addition, the number of direct paths from each user's third-party path is 5.15 on average for Third-Party A, while this level is 6.46 on average for Third-Party B. This result actually shows us that these platforms target users who are prone to generating direct traffic and that these results are completely misleading.
Well, before answering questions such as what should we do with this information or should we stop using these platforms, we recommend that you apply this analysis to your brand before making a decision, reminding that this analysis was made for a single brand. But you are likely to get a similar result :)
As AnalyticaHouse, we remain to use these platforms and interpret the data correctly, and we perform our optimizations and evaluations in the light of this information.
If you think it is very painful for you to analyze information at this level, then we are here for many advanced analyzes and applications. Then again, this is a very small part of what we can do!
Once again, if you are taking actions just by looking at Google Analytics and your regular reports, you do not realize what you are missing. If you would like improve your current information about Google Analytics and this kind of technical issues we can give a recommendation to have a look at our React and GA4 content which can be very beneficial about this kind of technical things that has been mentioned in this article
- Overview of Attribution modeling in MCF. Retrieved from: https://support.google.com/analytics/answer/1662518?hl=en&ref_topic=3205717
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