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Optimization based on Attribution Model Analysis

We are paid to improve the ROI of our company: Sales Increase + Cost Control = Improvement in ROI.


Since we have an attribution model system working for us, we are going to start from there.


CPC Optimization

In the second column we can see that if:


CPC = (Conv x CPA)/clicks


And the number of clicks equals 3000 and we want to keep the value at CPAs = €35.96, because we want to sell this CPA at a maximum value. Then:


CPC = (262 x 35.96)/3000 = € 3.14
New CPC = (315.37 x 35.96)/3000 = € 3.78


Consequently, if we’re talking about Adwords, if we up the ante 20% to a total of 3.78, we can:


1. Improve our median position
2. Improve CTRs
3. Improve QS
4. Increase the number of clicks
5. If % conversion is maintained
6. We will increase sales
7. To a CPA we want to work with.


If this data had been lower than the CPC we were already working with, we would have had to lower the CPC.


Post-Conversion Optimization

We have an attribution models system, or at least Google Analytics has put one into place, for the purpose of controlling the full customer/user journey up to the time of purchase. But, what happens to those users who already made purchases or have converted, and continue to click on our advertisements?


It would be useful to measure the number of post-conversion clicks, to perform an attribution model analysis We have noted that these numbers can be low, in some cases, totaling 1% of the total number of clicks, or up to 10%, which would be alarming.


What’s the probability that that percentage lies closer to 1% than to 10%?


My product’s design. In other words, if you are a SaaS company, or a hosting or an airline company, and after conversion the user needs to return to my website for documents, to get to the dashboard, etc…


A tool such as Adinton allows you to:


1. Know exactly how many post-conversion clicks you have.
2. Adopt measures:
a. Once we know that the user has become our customer, we can send him/her to a landing page in order to encourage cross-selling, or to Customer Service,…
b. Disappear: Inverse remarketing campaigns can be created, so that we no longer appear in Adwords (Search & Content) for this user for a certain number of days.


Optimizations according to the time of the click


Looking back at the Clicks and conversions Table by order of clicks, we’ll add another column:

If we look at the last column, we see it’s the conversion rate, keeping in mind that in order for conversion to occur on the 2nd click, the first click has to be made. In other words, a user that converts on the 3rd click:

Position Clicks Conversions %CR (Click) %CR (User)
1 1 0 0% 0%
2 1 0 0% 0%
3 1 1 100% 33%


In other words, according to Google, the conversion rate of users that reach the 3rd click is 100%. Our statistics have this number at 33%. Imagine how this simple change in perspective could affect CPCs, CPAS, etc.


Therefore, there could come a time, after the 3rd or 4th click, that we would simply choose not to appear. On the other hand, maybe we would still like to appear. 🙂


To this end, a tool such as Adinton would allow you to create rules like:


– If the user is doubtful and has already clicked 3 times on your ad, the 4th click would:

a) send him/her to a landing page with a huge discount.
b) send him/her to a landing page that would at least get his/her email (Growth Hacking).
c) make your ad disappear, with Inverse Remarketing.


Download our Attribution Model white paper. It’s set out in clear language, with answers based on experience and specific examples of the following topics:

1. A clear definition of Attribution Models and the Customer Journey.
2. A chart with the pros and cons of every attribution models.
3. A case study showing how to find out at a glance if attribution models are needed.
4. Optimization actions based on analyses of the attribution model.
5. Use a very simple tool in order to understand the influence between channels.
6. Duplicate conversions do not mean duplicate sales.


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