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Google Analytics Attribution Models – What You Should Know
The majority of business owners use Google Analytics to help them understand their target audience. Few business owners however, know about the attribution models that are also available, which can be a gold mine for improving ROI on marketing.
To help you understand why these models are so useful for understanding your conversions, consider the following scenario: over a couple of days, a visitor clicks on your Google Ad but doesn’t convert, they then click on a link in your e-newsletter but again they don’t convert, finally they receive a Tweet from a friend and they convert.
Which channel converted this visitor? Was it the Google Ad, e-newsletter, Tweet or all three?
The answer is in the attribution models!
To use these models however, you must first set-up your ‘goals’ in Google Analytics, use campaign tags for all your marketing campaigns, and link Google AdWords to Google Analytics (if you are using Google AdWords of course!).
Let’s look at some of the basics about these attribution models, then at the seven different types of models, deciding which one you should use.
What is attribution?
Attribution is all about assigning your conversions to the right marketing channels.
Why is it important?
Identifying your best converting marketing channels helps you to fine-tune your marketing strategies and increase conversions.
What types of attribution models are available in Google Analytics?
There are seven different types of attribution models available in Google Analytics and you can choose one or more of these models, compare different models and even create your own custom attribution model.
Last Non-Direct Click: This is the default model presented to you by Google. It gives all the credit for the conversions to the last channel that was used by a visitor to reach your website. In our example where the visitor bounced from your Google Ad to your e-newsletter and then to Twitter, by default Google would assign the conversion to Twitter. In addition, since these are non-direct clicks, if a visitor typed your URL into their browser and converted, it would not be included in this model, because it was a direct click to your website.
Last Interaction: This model gives credit to the final channel that led to the conversion, whether via an in-direct or a direct pathway.
Last AdWords Click: This is the same as the last Interaction model, except that if the visitor clicked on a Google AdWords promotion at some point in their conversion pathway, then all credit is given to Google AdWords.
First Interaction: This model gives all credit to the first channel along a visitor’s pathway to conversion: in our example this would be the Google Ad.
Linear: This model spreads the credit equally across all channels in the pathway to conversion.
Time Decay: This model gives credit to all channels in the visitor’s pathway to conversion, however this isn’t evenly split between the channels. Instead, more weight is given to the channels closest to the conversion, with each previous channel receiving half the credit. Using our earlier example, the Google AdWords would receive ¼ of the credit, the e-newsletter ½ the credit and the Tweet 1 credit.
Position Based: This model splits the credit evenly between the first and last channels, but it’s also shared equally between the other channels. Using our example again, a common scenario is to allocate 40% of the credit to the Google Ad and 40% to the Tweet with the remaining 20% allocated to the e-newsletter.
What type of attribution model should you use?
If you are new to using attribution models, your best strategy is to compare three models using the Model Comparison Tool in Google Analytics: Time Decay model + Position Based Model + Last Non-Direct Click Model.
This approach flags channels that are converting better than others, allowing you to optimise your budget to fit which channels are providing you with the highest returns.
For more information on increasing conversions on your website, call us on (02) 8211 0668 or email us at email@example.com.