In Adtomic we believe that being able to measure how your marketing campaigns are working with correct data is what’s most important. This allows our technology to optimize and maximize the investment: by knowing what the results are, the investment is distributed, ads are turned on and off, and a great portion of the investment is assigned to a specific channel that works better than others.
🤔 What is an attribution model?
The attribution model is the way in which Facebook/Instagram and Google campaigns adjudicate (give credit for) sales. This means, the way in which user interaction with ads is measured on different platforms and its influence upon conversion.
😰 Problems with the attribution model
The attribution is a topic that isn’t completely resolved in digital marketing. It’s like that because each platform has its own model and, in general, each one carries their own water.
Google Ads will claim as its own all the sales that were made up to 30 days after a click on a Google ad. Facebook will claim as its own the sales that are made up to 28 days after a click on a Facebook ad. Neither one of the two keeps in mind that there is an omnichannel for sales.
That means that in order to make a sale, a persona generally goes into a website thanks to a Facebook ad, looks at the products, and then ends up buying by going in through Google.
Let’s look at an example: when you’re on your way back from work, you see an ad on Instagram for a pair of shoes, you go into the site, but you don’t end up purchasing. When you get home, the first thing you do is go to the computer, you open Google, you search for the brand name and finally, you buy. In that case, two channels intervened in order for the sale to be made. As much Instagram as Google will claim the sale, duplicating it.
Since our platform needs to really understand what portion of the conversions correspond to which platform in order to optimize the campaigns and maximize results, we created a Data Driven attribution model. This means that it’s based on data that we compiled from all the channels that we invest in: Facebook, Instagram, Google Search, Google Shopping. However, besides that, we’re also aware of the kind of audience that we can target in each campaign.
Based on all the compiled data, a real influence is assigned to each platform upon completing a sale, avoiding duplication and being able to know what is working better and with greater exactitude.
👉 Analytics Discrepancies
As we have already told you, our Data Driven attribution model will not coincide with what the Facebook platform reports (you will see fewer sales in our report) nor on Google’s (you will see fewer sales in our report). But it won’t match Analytics’ data either!
Analytics has a problem when the time comes to measure conversions: its attribution model isn’t fair for all the channels. Why not? Because it measures last click, which means that it attributes based on the last click of the user before the purchase.
As we mentioned before, generally a person that goes into a Facebook ad, doesn’t end up purchasing right then, but rather later, through a Google search.
But if they hadn’t seen the Facebook ad, they would have never searched for it on Google.
In that sense, it’s common for sales that should be credited to Facebook are adjudicated to Google campaigns because that was the person’s last click before purchasing. If you take the Analytics results into account in order to redistribute the budget, then this is how we would start based on a mistake diagnosis because we would overvalue Google and underestimate Facebook. Put simply, if it were up to Analytics, far less would be invested in Facebook.
Also, if a person interacts with an ad on one device and ends up purchasing on the other, Google Analytics will probably consider these two different sessions and the information is lost about what channel facilitated the purchase by taking the user to the site.
These reasons are why it’s also no good for our technology to feed on these metrics, because they don’t represent the true influence of each platform. And since our attribution model is fairer with each channel, there will be discrepancies with what Analytics shows.
As we saw, the attribution model is something essential and that’s why we work to develop a fairer one that will be useful for optimizing the investment in a more efficient way and obtaining better possible results.