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Properly selected communication, building engagement, ad targeting and remarketing - all this certainly positively affects the sales results of individual companies. In this article, I will write a little about how to use Google Ads or Facebook Ads machine learning to scale eCommerce activities. You will learn how to increase revenues while maintaining a profitable ROAS. I will focus primarily on how to increase sales by changing the view on attribution modeling, and especially on the popular last-click model. I will write about the implementation of the data-driven attribution model, which is called the only right source of truth in eCommerce.Attribution models in eCommerce The topic of machine learning of Facebook or Google Ads systems is worth starting from the basics. In analytical systems, we have several attribution models to choose from . The one that agencies and their clients pay the most attention to is the last-click model . It makes us look only at the last point of france phone number list contact between the user and the website. We verify one hundred percent responsibility for sales success as a last-click model. In addition to this model, in the Google Analytics system, we can analyze activities based on: first click model, model linearny, model based on the position of users on the shopping path (position based) or a model adapted to time time decay.
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Attribution models The new attribution model is data-driven – a data-driven model that considers not only one point of contact between the user and the website, but all elements in the funnel. , as a percentage, to distinguish which have a greater share in improving the effects of the page, and which have a smaller share. Let's break everything down to prime factors It's worth telling yourself a little more about attribution models and how to implement them correctly, so that in the final step of machine learning it works properly. When analyzing Google Analytics charts, we are most often guided by the last-click perspective . As an example, from the entire traffic acquisition system, let's take the "source: Google/cpc" report. Based on it, we are able to verify: how many users we delivered to the site, what is the quality of this movement, but above all last click effects, i. conversions related to the last click.
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