Choosing the correct attribution model for your business may seem difficult and confusing so it is important that you fully understand the pros and cons of each model. With 6 potential models to choose from, it’s important that you choose the right one that best suits your requirements.
Last Click
Last Click attribution is the default model chosen by Google Ads. This model will attribute 100% of the conversion to the last click in that conversion path. Therefore, the 100% will be attributed to one ad and one keyword which was the last touchpoint that led to a conversion. This model is definitely efficient and easy to understand, but is it the best choice? No.
Using this model may lead an advertiser into believing some keywords/ads are simply not working for them because they have “never converted” when this may not be the case at all. A user may have found your site by searching for a specific term then clicking on an ad, but not converting at all. They may have then found you again by searching for an alternative term and clicking on a completely different ad, then converting. The Last Click model will then attribute all of that conversion to the last ad and the last keyword used when actually, the first search term and ad used are the reason that they found you in the first place. This could then cause these ads/keywords to be paused in future as they appear to have not converted.
Overall, this model does not allow you to see a true picture of the performance of your campaigns.
First Click
First Click attribution assigns 100% of the conversion to the first click in the conversion path. Therefore, the first ad and the first keywords used will each receive 100%. Now, this may sound like a better alternative to Last Click but it has the same disadvantages. Using this attribution model, you will not be able to see the true extent of which ads or keywords have contributed to the conversion that has occurred.
Similarly to Last Click, when reviewing data in your Google Ads account, this model may lead you to believe that specific ads and keywords are not profitable for your campaigns and then cause you to pause/remove them. When in reality, without the alternatives you may have never received the conversion. This model does not paint a true picture of your PPC campaigns performance.
Linear
Linear attribution assigns credit to every touch point in the conversion path and is considered by many a “level playing field”. For example, if there are 10 touch points in the conversion path, then 10% will be attributed to each ad and keyword that has played a role. By assigning credit to each vital point in the path, this will aid optimisations hugely. It will allow the advertiser to clearly see which ads and keywords/campaigns have been the most profitable for them and it will not overshadow key points (which would occur in First/Last Click attribution).
However, not all touch points are of equal value to the advertiser. Although they have technically all lead to the conversion action taking place, some points/ads will have played a much bigger role in the entire process. This may then cause significant/key points to be undervalued.
This model is thoroughly recommended for those who want to view all stages within the conversion paths in Google Ads. As all points are given credit, it is an incredibly good model to take advantage of so you can optimise your performance using more reliable and accurate data. Although there are limitations, the benefits far outweigh those of First and Last Click.
Time Decay
Time Decay is a little more in-depth than Linear as it assigns different levels of credit to each touch point, depending on how close it is to the final conversion. Therefore, the majority of the credit will be attributed to points that are closer to the last interaction made before a conversion and those at the begin are given less of the credit.
Time Decay is very beneficial for those who are looking at the final interactions made before a conversion has occurred, and it does not overlook the points at the beginning of the path. However, the first points in the path may have been the most valuable to that conversion occurring. Although this may be the case in a lot of occasions, the credit is not hugely placed on this and instead goes towards those at the end of the path.
Time Decay is a hugely beneficial model to take advantage of as all points are assigned credit, however, more is attributed closer to the final interaction. As good as this is, valuable points can be overshadowed by others and it’s important that advertisers are aware of this.
Position-Based
Position-Based combines the benefits of both the Linear and Time Decay models as it attributed 40% to the first and last actions and the remaining 20% is distributed evenly among the remaining touch points. This model is known for finding a “happy medium” between the previous models and is often called a “U” Shape. Like Linear, every single touch point is acknowledged so that the advertiser is aware of every interaction in that path but it will prioritise the first and last actions.
As always, there are some limitations to this model in the idea that the majority of the credit is given to the first and last clicks, assuming that they are the most important but this may not always be the case.
This model is definitely one of the ones that is highly recommended by PPC experts but it is vital that you ensure it meets your goals before using it.
Data Driven
This attribution model is not available for all Google Ads users as there are some specific requirements that need to be met. The account must have at least 15,000 clicks and 600 conversions within 30 days and this has to be consistent monthly (10,000 clicks and 400 conversions).
This model gives credit based on how people search for your business and then convert. It uses the conversion data to calculate the actual contribution of each keyword across the entire conversion path, making it incredibly accurate.
It is important to note that this model is still in beta so requirements may be lowered as more accounts begin using it. However, if you are in the threshold of Data-Driven attribution then it is thoroughly recommended that you take full advantage of it.
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