Attribution modelling is the process of finding and fixing attribution issues, understanding the buying behaviour of your website users and determining the most effective marketing channels for investment at a particular point in time.
Following is the more technical definition of attribution modelling:
Attribution modelling is the process of understanding and assigning conversion credit to marketing touchpoints on a conversion path with the aim to achieve the following objectives:
Understand the customers’ purchase journey.
Determine the most effective marketing channels for investment at a particular point in time.
Find and fix attribution issues.
In order to understand the definition of attribution modelling, you would first need to understand the following terms really well:
Conversion
Conversion credit
Conversion credit distribution
Digital vs. non-digital marketing channels
Touchpoints
Conversion paths
Note: Attribution Modelling is full of jargon and if you do not understand the terminology, you will have a hard time understanding attribution reports and implementing attribution modelling.
Conversion is one of the goals or purposes for setting up a website or mobile app. A conversion is what you are trying to achieve through your website/app.
There can be one or many purposes for which you have set up your website or app.
These purposes can be something like:
Selling products
Generating leads
Branding
Selling advertising
Collecting donations
Fighting for a cause, etc.
If one of your website goals is to generate orders on your website, then ‘the number of ecommerce transactions’ could be defined as a conversion.
Similarly, if one of your website goals is to get sign-ups for your newsletter, then the ‘number of newsletter signups’ could be defined as a conversion.
In the context of attribution modelling, there are two categories of conversions:
Ecommerce conversions
Goal conversions.
What is the difference between ecommerce conversions and goal conversions?
An ecommerce conversion is a conversion that is directly tied to a transaction.
For example, a ‘purchase’ is an example of ecommerce conversion because it is directly tied to a transaction.
A goal conversion is a conversion that is not directly tied to a transaction.
For example, ‘newsletter signup’ is an example of goal conversion as it is not directly tied to a transaction.
What is a conversion credit (also known as conversion credit score) in Google Analytics attribution?
A conversion credit (also known as conversion credit score) is the amount of credit you or an algorithm gives to a touchpoint for completing a conversion.
What is conversion credit distribution in Google Analytics attribution?
A conversion credit distribution is the distribution of conversion credit to various touchpoints on a conversion path.
The major purpose of setting up a website is known as a macro conversion and other minor purposes are known as micro conversions.
For example, if your main purpose of setting up a website is to generate sales then the ‘number of ecommerce transactions’ can be your macro conversion.
The other minor purposes like ‘newsletters signup’, ‘downloading a brochure’, ‘providing customer support’, ‘requesting a follow-up’ etc. can be your micro conversions.
What are digital marketing channels?
The following are examples of digital marketing channels:
Why the focus is on digital marketing channels in attribution modelling?
In the case of attribution modelling, the marketing channels that we focus on are primarily digital marketing channels but can also include non-digital marketing channels as long as these channels are used to measure and optimise the ‘online performance’ of a business.
Here there is a strong focus on optimizing the ‘online performance’ of a business.
The non-digital marketing channels can also be used to optimize the ‘offline performance’ of a business. But that is marketing mix modelling and not attribution modelling.
What is a conversion path in attribution modelling?
A conversion path is a path a user took to complete a conversion on your website/app.
In the context of Google Analytics, a conversion path is a sequence of touchpoints (clicks, visits, impressions) with digital marketing channels during the 1 to 90 days period that leads to conversions.
Consider the following hypothetical conversion path of a user:
Here the user is exposed to six marketing channels before he made a purchase.
Google Analytics will report this conversion path as:
Important points to remember about conversion paths
A conversion path is made up of one or more touchpoints.
The conversion path is created for each conversion recorded by Google Analytics.
Now let’s revisit the definition of attribution modelling in Google Analytics:
Attribution modelling is the process of understanding and assigning conversion credit to marketing touchpoints on a conversion path with the aim to achieve the following objectives:
I hope now the definition of Google Analytics attribution modelling makes sense to you now.
Attribution Modelling is much more than Google Analytics
No one tool can solve all of your attribution issues. And Google Analytics is no exception.You need a whole set of tools at your disposal in order to understand your customer purchase journey and behaviour.
For example, Google Analytics is one of the best free tools for measuring website usage data. It is also the best tool for attribution modelling. It is not, however, the best tool for:
Google Analytics has its own sets of limitations which you should be aware of. Therefore, you should not limit yourself to just using Google Analytics and no other analytics tool or technology.
If you are a large enterprise then you should invest in more robust attribution modelling solutions.
If your budget allows, then create and use in-house custom attribution modelling solutions. With custom solutions, you get maximum flexibility in terms of creating attribution models and applying custom credit rules.
You can achieve very robust data integration capabilities. Above all, you get complete ownership of your data and solutions.
You do not need perfect data in order to implement attribution modelling
If you seek perfection then the majority of the time you will find yourself procrastinating. This is because things are perfect only once in a while.
Do not spend the majority of your time trying to collect perfect data so that you can take that perfect business decision. Otherwise, there is a high probability that at the end of the day you will not take any decision and action.
Taking timely actions is extremely important in today’s world of cut-throat competition. Imperfect action is always better than inaction. Moreover, no analytics tool is perfect.
You cannot expect 100% accurate data from any analytics tool out there and Google Analytics is no exception. So avoid being obsessed with collecting perfect data and be happy with good enough data.
Attribution Modelling is not the be-all and end-all of solving all the problems around ROI
This is because attribution modelling is not optimization. Optimization is what SEO, PPC and Conversion professionals do. Even with ideal budget allocation, you will not get optimum ROI across marketing channels if:
Your campaigns are being optimised poorly.
Your website is suffering from usability and credibility issues.
Your competitor is dominating your market.
False or unrealistic expectations from attribution modelling can disappoint decision-makers. It could make them not want to trust, use, and value the attribution data.
All of which are bad signs for getting budget allocation for attribution modelling efforts. That’s why setting up realistic expectations from the very start are very important.
Attribution modelling is not a one-time activity
The marketing channel or activity that helped you to generate a conversion today may not help you to generate the same conversion tomorrow. That’s why it is important to continuously explore the role of different marketing channels and campaigns in assisting conversions.
The performance of marketing channels and campaigns improve or deteriorate over time. So you need to continuously find and separate good and bad marketing channels, ads and campaigns.
You then need to either improve the performance of the bad campaigns or replace them with brand new campaigns.
That is why attribution modelling is not a one-time activity. It is not something that you can do once and then forget about it.
Focusing on a single marketing channel is not attribution
In a multi-channel marketing world, no single channel is solely responsible for conversions. Different marketing channels work together to create sales and other conversions.
That is why in attribution modelling we do not focus on measuring and optimising just one marketing channel. We do not create attribution strategies around a single channel.
Other Articles on Google Analytics Attribution Modelling
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