What is Attribution Modeling?

Marketers are constantly bombarded with new campaigns for products and services. The ever-increasing number makes it hard to pinpoint exactly which campaign yielded conversions. Attribution modeling can therefore be defined as a means of assigning credit to campaigns for conversions. For every purchase, credit must be assigned to a campaign.

Benefits of Attribution Models

Attribution modeling will help you to assign value to different campaigns. As a marketer, this is crucial because one needs to know which methods are working and which are not. With this information, a marketer can then figure out which marketing tactic should have a wider portion of the budget and where you can cut down on funding. Apart from making projections using concrete data, modeling will also help a marketer present findings to executives and bosses in a data-backed manner. This acts as evidence of the efficacy from marketing campaigns and as justification for changes in budget and tactics. It is especially important since most executives are not able to trace physical conversions back to digital campaigns. The latter might end up seeming insignificant but the attribution models help to demystify this. Some common models include:

First Interaction Attribution

The first interaction attribution model is one among many. It assigns 100% of the credit from a conversion to the buyer’s first point of contact. If a buyer clicked on an email link to get to a site and then visited the site again from a paid advertisement on another site, the credit will be assigned to the email campaign even if the buyer goes to the site to make a purchase after a couple of weeks. This attribution model ignores all other conversion paths and automatically claims that this first point of contact was responsible for kickstarting the buyer’s journey.

first-click-attribution

More info: First Click Attribution

Last Interaction Attribution

The last interaction attribution model, unlike the first attribution model, assigns 100% of the credit to the last point of contact. That is, the last campaign that the buyer used before making the purchase. In the previous example, the credit would be assigned to the paid advertisement, as it was the last conversion path used before the purchase was made.

last-click-attribution

More info: Last Click Attribution Model

Linear Attribution

For the linear attribution model, every campaign or marketing tactic used in the buyer’s journey is assigned equal credit. If, for example, a buyer encountered an email, a paid advertisement, a social media link and then directly visited the site to make a purchase, each of these conversions paths would be assigned 25%. This model takes into account each campaign as a contributing factor and ignores none.

linear-attribution

More info: Linear Attribution.

Time Decay Attribution

The time decay attribution model credits each conversion path depending on its position in time. The last point of contact gets the most credit while the first one gets the least. Every other campaign in between gets more credit assigned the nearer it is to the last point of contact.

time-decay-attribution

In the example of an email, a paid advertisement, a social media link and a direct visit, the direct visit gets the most credit, followed by the social media link, the paid advertisement and the email campaign gets the smallest percentage. The further away it is from the point of purchase, the fewer the percentage points credited to the campaign for a certain conversion.

More info: Time Decay Attribution Model

Conclusion

Each of these models is used by marketers. The problem with attribution modeling is that it is not perfect, but Algorithmic Attribution Model. No one can comfortably say that one model is foolproof while discrediting the others, as each of them has a shortcoming. The type of model that any marketer uses will depend upon his or her discretion. However, using mixed models will ensure that you get various viewpoints showing how each of these marketing strategies plays a role in the number of conversions.

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