Disruptive Marketing Blog

Adinton Attribution Model

The goal of this post is to clear up any confusion about the Adinton attribution model and how the tool uses this model to build and run the Adinton optimization rules and to create the Adinton Score.

What is an attribution model?

In order to answer this question, first we need to explain what attribution theory is all about. Attribution theory is the study of models to explain the process and the cause of something. Consequently, we could say that attribution models help us with the process of identifying events, interactions and behavioural patterns. In online marketing, we can use this knowledge to enhance the users experience of our website and to shorten the customer journey.

Theoretically speaking, an attribution model is a system to establish the way in which we assign a value to sales or conversions of a user throughout the customer journey.

In marketing, attribution models help us visualize the channel from a conversion/sale and how the user interacts within this channel. With all this information, we can make decisions and create personalized strategies for the benefit of our company. Working with attribution models allows us to quantify the influence between our channels (ppc ads, emailing, social media …) and help us take better decisions regarding where to invest and why.

The most common Attribution Models

There are many attribution models you can use.It depends on your business strategy as to which to choose. We are going to proceed by explaining the most common ones.

Attribution Model. Customer Journey
Attribution Model. Customer Journey
  • (1#) Linear attribution model: It assigns credit to all channels with the same value. Therefore, no matter where the last click came from, each of the participants are going to be recognized with the same amount. In our example above, all channels will be given 20% of the credit for that conversion. This model is great because it takes into consideration all the participants but its downside is that it does not tell us which channel gives us a higher revenue.
  • (2#) Last interaction: This model is also known as last touch or last click. The credit goes to the last interacted channel from where the last click comes from. Therefore, the rest of the participant channels are not taken into consideration. In the example shown above the attribution will be given to direct. This model is used by default in many analytic tools but not Google analytics which uses a last interaction model (#5) which does not consider direct as the last attribution channel. The problem with this model is that it might be that the last click is very important but we should also consider the other channels and at least know how they have influenced the last channel interacted with.
  • (3#) Last Adwords clicks: Even if a last click has been from another channel, if there was some Adwords click in between, the credit will go to Google Adwords. This model, as we can imagine, is used to benefit Google. For our example, the attribution will be given to Google Adwords, which of course it’s not really accurate.
  • (4#) First interaction: It assigns credit to the first interaction channel since it is the one that has triggered the conversion. This one is the opposite to the last interaction model. From my point of view this is a very complicated model and in most of the business models does not make sense to even consider it. This model does not take into consideration the last click or the clicks in between.
  • (5#) Last no direct traffic click: The credit will be assigned to the last traffic source without considering last clicks from direct traffic. As for the example above, the credit will be given to social media. As said before, this is the model used by Google Analytics by default. This model underestimates the power of direct visits. Just give it a try and compare how your brand awareness changes if you use this model compared to #2. Still, this model has the same issues as #2 since it does not take into consideration the rest of participants.

The Adinton Attribution Model

Adinton uses its own attribution model. After years of research, we have been able to create the most suitable attribution model for online marketing. We use a last interaction model (we give you the option of counting direct traffic as last interaction) with an added value, the Adinton Score. With Adinton you can visualize the attribution of each of your channels in a unique way. Moreover, Adinton shows you the real influence between channels and the real weight they have.

Adinton Score

The Adinton algorithm uses  the Adinton Score to assign weight to a particular channel and therefore suggest a more or less aggressive bid. Some of the factors influencing the Adinton Score are:

  • Each channel a unique user has gone through
  • Historical data from each of the clicks in each channel
  • Basket value
  • Page views by a unique user
  • Bounce rate

In addition, the Adinton algorithm works by collecting information in real time so the Adinton Score is constantly being modified. All this is possible because we have our own analytics system and therefore, we do not have to work with third party softwares.

Attribution Reports

Adinton offers detailed reports regarding the influence between channels, campaigns, ad groups, and even keywords. Because of the Adinton attribution model, you will easily visualize the influence that each channel has had during the conversion process of a user.

We can generate a total of 4 reports from the Adinton Attribution Model. We will briefly explain the difference and characteristics of each of them.

Report Adinton – Basic

Adinton Report Basic
Adinton Report Basic

This first report provides information by source or medium from all channels that have participated in the conversion process. The columns in this report give us information such as, what was the channel that initiated the conversion (started clicks), the channel that has finished conversion (finished clicks) and the channel that has been involved in the conversion (middle clicks). In addition we can also see which channel has more one click conversions.

Report Adinton – Summary

Adinton Report Summary
Adinton Report Summary

his other report gives us valuable information such as the number of page views per source or medium, the amount of clicks per channel that have provided sales and the amount of clicks by channel occurred when the user has already converted on your site (clicks post conversion).

Report Adinton – Reports

Report Adinton Atribución
Report Adinton Atribución

With this report, we can visualize the real influence that each source or medium has on each other. For example in the image above, we see that 4 clicks from SEO have helped or have participated in 4 conversions from Google Adwords. Also the difference between conversions from the same channel (self conversions) and conversions carried out with a single click (one click conversion).

Report Adinton – Tracing

Viaje del usuario
Viaje del usuario

Adinton gives you detailed customer journey information for each unique user. That way you are able to analyze every single user in a more comprehensive manner. This type of report is very helpful to investigate fraudulent clicks and or conflicting affiliates.

This same information is gathered by our tool to trigger the automated rules. Adinton collects and performs with accurate data, so the Adinton attribution model and rules are not based on an estimation of the sample.

Added value

As we are not working with third-party partners, we can provide all the information that the tool collected to our customers so they can export them and  or import them to Google Analytics if necessary.

Adinton also records and stores information after the conversion. So you’ll be able to analyze the behavior of the users after a transaction.

Finally, not only we work with our own attribution model, but also, the tool is able to perform specific rules to improve your customer journey or even generate an automatic keyword research . All with real time information and based on real and accurate data.