Tutorial: Using Google Model Attribution Tool
What Is the Google Model Attribution Tool Used For?
Simply put, the Google Model Attribution tool allows marketers to view how their advertising channels would be credited with a conversion across seven different default models available in Google Analytics. The tool can be used to compare models to each other and be applied across custom channel groupings, source and mediums.
Why is the Model Attribution tool important to my marketing?
Every marketer should be familiar with this tool as it provides the full picture along with the assisted conversions tool of how channels work with one another to drive conversion or sale. Traditionally the last click (last interaction) model has been widely adopted as the default conversion attribution model. The problem with last-click attribution is that it unfairly attributes all of your digital media value to just one channel.
Before anyone in your company makes a decision on investing more/less money into advertising it’s important to make a decision based on the full picture of your marketing channel data. Generally, we find that channels will work together in a multi-channel approach to convenience a customer to purchase. You’d be surprised how many people disregard this approach and cut out meaningful channels because they didn’t have a dollar value assigned to them, only to put themselves in a situation where conversions are lower and cost per conversion soars even higher.
Understanding attribution models:
Within Google Analytics you can view attribution based on seven default models. Below will go into how Google defines the attribution model and why it may be important to sure the model.
Last Interaction (or Last Click): 100% credit of the conversion (lead/sale or goal) is given to the last channel that pointed the user to your website before completing the conversion.
Last Non-Direct Click: All direct traffic is ignored instead 100% of the conversion credit goes to the last the customer clicked through before converting. For example: if the user clicked through to your website via an email, left your site and later returned your site by typing in yourwebiste.com (direct), the model would ignore the direct touchpoint and give the email 100% credit for the conversion.
Last Google Ads Click: The last google ad that was chosen would receive credit for the conversion, this would ignore any other channel that may have assisted in the conversion.
First Interaction: The first channel the customer interacted with would receive 100% of the conversion credit. This can be a helpful attribution model to evaluate if you are interested in how users who take action are discovering your brand.
Linear: Each channel would receive equal credit for a conversion. If the user interacted with four channels then each channel would get .25 spilt of the conversion.
Time Decay: The channel closest in time to the conversion will get most of the conversion. Google states “ In this particular sale, the Direct and Email channels would receive the most credit because the customer interacted with them within a few hours of conversion. The Social Network channel would receive less credit than either the Direct or Email channels. Since the Paid Search interaction occurred one week earlier, this channel would receive significantly less credit.”
Position Based: 40% of the credit for conversion will be assigned to the first & last interaction (80% total) with the remaining 20% distributed among the rest of the channels that were between first and last clicks. An example of this could look like this.
- Google Ad (First Interaction): 40%
- Email (Middle Interaction): 10%
- Display Ad: 10%
- Facebook/Instagram Ad: 40%
Where can I find the model attribution tool in Google Analytics?
- Select “Conversions” on the left side navigation bar, select “Model Comparison Tool”
- Click “Select Model” to begin comparing each model to one another.
- Start making informed decisions based off your data!
Using the Google Modle Attribution tool can make you an instantly more informed marketer. Marketers can avoid the pitfalls of looking at only default level data and ultimately create a media plan that maximizes efficiency and caters to their user at every moment of the purchasing journey.