Online marketing attribution

Online marketing has become an essential part of today’s world, because there are endless possibilities to reach potential customers online, which is why more and more companies are investing even larger budgets. But how do you measure the success of the various advertising measurements? This is where marketing attribution comes in.

Definition of marketing attribution

In marketing, we use attribution as a method of analysis to identify which advertising measures are responsible for which conversions and, above all, how many.

In most cases, a customer comes into contact with several advertising measures on different digital channels during his customer journey. A customer journey can look like this:
The customer surfs Facebook and sees an ad, then goes to Google and searches for the brand or product to compare prices, possibly comes across an ad banner in the meantime, finally clicks back to the online store and makes a purchase there.

Now the question arises, which channel gets this conversion?

Attribution is about analyzing the customer’s buying process from the beginning to the end, weighting all touchpoints to find out which has more or less influence on the user’s buying decision. In this way, it should ultimately be possible to make a statement about the efficiency of the advertising measurements.

Attribution methods

In general, a distinction is made between rule-based and data-driven attribution models.

Rule-based attribution

Rule-based means static.
A specific value is assigned to each contact point along the customer journey, based on a model previously defined by the advertiser.
However, these weightings manipulate the result and thus provide only rudimentary initial answers about the advertising effectiveness of the various measurements.

Data-driven attribution

Data-driven means dynamic.
In this model, a large amount of data is analyzed using an algorithm. The value of each click (converted and non-converted click paths) is calculated individually.
In this way, data-driven attribution evaluates which touchpoints along the customer journey had a positive influence on the buying process.
The current situation is always recorded and thus changes in the advertising impact are also automatically detected.

This dynamic attribution model is very objective and examines the real value of touchpoints along the customer journey to a conversion based on the situation. It has the advantage of a self-learning algorithm based on machine learning.

Rule-based attribution models

All rule-based models have one thing in common:
The value of the different touchpoints is defined and set in advance by the advertiser itself.
Therefore, the values always remain constant and it remains unanswered whether this corresponds to the true impact on conversion.

Last Interaction / Last Touch

(also “Last Cookie Wins” or “Last Click Wins”).

The chronologically last touchpoint before the conversion is assigned 100% of the success.
The Last touch attribution model is the most commonly used attribution model in practice.

First Interaction / First Touch Attribution

(also “First Cookie Wins” or “First Click Wins”)

The chronologically first touchpoint of the customer journey is assigned 100% of the success.
It’s particularly suitable for the evaluation of branding / awareness campaigns.

Time Decay

The touchpoints that are chronologically closest to the conversion are assigned the greatest value. The value therefore increases with each step towards the conversion.


All touchpoints in the conversion path are assigned the same value for the conversion.


(also “bathtub” or “U-shaped model”)

Higher values are assigned to the first and last touchpoint within the customer journey. All touchpoints in between receive a lower conversion share.
It’s a combination of a last touch and first touch attribution model, best suited for awareness campaigns (incl. final conversions).


Individually defined value contributions per touchpoint.

Choosing the right attribution model

The decisive factor for choosing the right attribution model is which goals the advertiser is pursuing.

The better approach for valid results on the effectiveness of different advertising measures is undoubtedly data-driven attribution.

However, this is not an option for every company, as certain conditions must be met in order to unleash its full potential.

For example, if data quantity and quality cannot be provided in sufficient form, rule-based attribution should be used.

The most important thing is to look at the user’s customer journey as a whole and not at each channel independently. This is the only way to draw conclusions about the actual effectiveness of all advertising measures across all digital channels.