The first-generation attribution models: last click and first click test
The last click model consists in giving all the credit to the one who made the final transaction possible. This method has the advantage of being simple. The trigger is rewarded, the one who puts it in the net, the scorer. This model is successful because it is based on an obvious fact: the Internet user has indeed had a final session at the end of which he made his transaction. Given the complexity of the customer journey and the multiplicity of contact points, this certainty is a good thing.
This method is simplistic. It doesn’t match the complexity of traffic acquisition. Take Sophie who has decided to part with her old iPhone that was too slow for a new smartphone. She immediately looks at the offers of her supplier, but also types in a few keywords, looks at a comparison site, gets an initial idea… Too many choices. Sophie postpones her decision. Two weeks later, a friend urges her to buy a Samsung. Sophie asked around on Google and a few forums. Until the day she logs on to Facebook and comes across a Samsung Galaxy insert with a discount. Only three days left. Sophie clicks and buys.
In the last click model, Facebook would pick up the tab. But, without this upstream chain of events, this last action would not have happened. Since we have the means to know all the touch points of Sophie’s journey, this model is largely outdated. Yet, for many, it remains the reference. In fact, it is the default attribution model in Google Analytics.
The first click model chooses to reward the first touchpoint (post-view remuneration), the one that made your site known upstream of the conversion path. It assumes that without this first touchpoint, there would not have been all the others, so it relies on it. The strength of this model is to value the first impression. The strength of this model is that it values the first impression. It allows you to work on notoriety and natural traffic, levers that are too often left aside. This is also its weakness. How can you be sure to have the real first touchpoint? All models include what is called an attribution window. In attribution solutions, this consists of setting the number of days of data retrieved. Usually 30 days. But who’s to say that the user hasn’t experienced your site before?
There is missing data. Let’s take Sophie’s example again. In the 30-day window, her first click on Samsung is on Google, but her real first click took place before, on a comparison site she knew by heart. The time windows of the model do not necessarily correspond to the duration of the conversion path.
Today, we can go much further in our thinking and be much more precise in our observation of purchasing paths than these models. This reflection has given rise to the second generation of attribution models, the so-called “positional” models.
Positional or multi-touch attribution models
As the name implies, these templates give credit to touchpoints based on their position in the conversion chain.
The linear model takes the number of touchpoints and divides the credit equally to the channels involved. With Sophie and her broken iPhone, you would have to give 25% to Adwords, 25% to Facebook, 25% to the comparison site, 25% to the emailing of her phone provider. The increasing model considers that the closer you get to the transaction, the more influence the touchpoint has. Sophie first saw the Bouygues emailing so 10%, then 20% for the comparison site, 30% for Adwords, and 40% for Facebook. The U-shaped model chooses to give credit for the transaction to touchpoints upstream and downstream of the transaction. The intermediate levers are less rewarded. So, for Sophie, it would give 40% of the credit to emailing, 10% to the comparison site and Adwords, and 40% to Facebook. These models have the merit of wanting to reward all participants in the chain. But they are arbitrary. For example, if the last three touchpoints happen in an hour, giving only 25% of the credit to the first one that happened two weeks before is probably unfair. We don’t know deep down.
These models all assume that the user has the same behavior behind each touchpoint, and that each chain of touchpoints corresponds to an experience. This is not true. Everyone reacts differently, even on the same sequence of ads.
Third generation attribution models, known as data-driven