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In today’s multichannel world, there are mountains of data which provide insights into how users have interacted with your business and their path to conversion (or non-conversion). It is important to understand performance with multichannel marketing, which can be achieved through attribution modelling. Attribution refers to assigning credit to something (a channel, touchpoint, etc.) for the role it played in the final conversion. An attribution model is a rule, or set of rules, that assigns this credit correctly to the right channel or touchpoint.
This blog will focus on AdWords attribution and then move on to Google Analytics attribution. The key difference between the two is that AdWords attribution is limited to the channels available within the platform, while Google Analytics can be used for a plethora of channels, enabling you to understand fully how all your marketing efforts impact the user journey.
Historically, AdWords has provided only last-click attribution or five rules-based models. These are explored in greater detail below.
This is the standard model used by most advertisers. It gives all the credit for the conversion to the last-clicked ad. The major issue with this model is that in today’s multichannel world, users are likely to interact with your brand across multiple touchpoints and channels.
For example, a user may click on an initial non-branded keyword, then a display remarketing ad and then finally convert through a branded keyword. Clearly, credit is owed to the first two touchpoints – in this case, it could be argued that the first non-branded keyword was the most important.
To ensure efficient optimisation, areas of your account which are having the most impact need to receive the budgets they deserve. Using last-click attribution can pull a curtain over these efforts, which could lead you to suffocate areas which actually play a crucial role in the overall conversion. To combat this, there are other models available; they are presented below.
This gives all the credit to the first touchpoint. Since attracting users’ attention is getting harder and harder, this model seems useful in that it credits the first brand interaction. However, similar to last click, this channel rewards only one aspect of your account and could prevent certain ads from getting the credit due.
Linear attribution assigns equal credit to every touchpoint in the path to conversion, so if there are 10 touches, each will receive 10% of the credit. This model is the first step to multichannel attribution and will allow you to start optimising towards your customers’ journey rather than individual touchpoints.
Whilst this model is a great first step, it has limitations – notably that every channel receives the same credit, so it will be hard to understand which touchpoints on the journey really have the greatest impact.
As the name suggests, time decay will give the most recent conversion points the most credit. Again, this is another great step towards multichannel attribution. Within Google AdWords, it works by giving attribution credit a seven-day half-life, which means that a conversion that happened eight days ago would get half as much credit as one which happened one day ago.
This model is suitable for businesses with multiple touchpoints and a short sales cycle. However, due to the time decay, it would not be best suited to longer cycles as it would fail to assign sufficient credit to the first interaction, which can be the most crucial for some.
The position-based model assigns 40% of the credit to both the first and last touchpoints and spreads the remaining 20% evenly between all others (disclosure: this model is a personal favourite of mine). Using this model will allow you to ensure that all touchpoints are rewarded, but also emphasises the first and last points, which I believe can be the most essential.
The first touchpoint is essential to introduce users to your brand and generate the initial interest, and the last touchpoint is essential to ensure you can be ready to close on the conversion (even for branded keywords, to prevent competitors taking users when they are at the point of conversion). One downside to position-based attribution is that you can begin optimising towards the first and last touchpoints without factoring in anything in between, which may not be suitable for your business’s objectives and the strategies you have in place.
Data-driven attribution is the latest model available and is based on an algorithmic approach to attribution. This model utilises machine learning to evaluate all the conversion and non-conversion paths your users take, their interactions with ads and a huge array of other factors such as ad creatives and keyword effectiveness to understand where credit should be assigned across your account.
The main focus of the model is to understand how people behave and then become customers of your business. Utilising this attribution model will allow you to harness the power of Google’s machine learning and also take the guesswork out of what rules-based model works best for your business.
Bing Ads uses last-click attribution by default, so even if a user clicks through multiple ads on the Bing search network before converting, the final keyword will receive all the credit. This can be problematic, especially if you have brand and non-brand keywords, as it will be hard to understand whether non-branded ones are having a meaningful impact.
Google Analytics attribution works in the same way as AdWords, but on a larger scale, and is able to encompass all of your channels. Using Google Analytics is definitely recommended to understand how your channels perform together and the journey your users take across organic search, email, paid social… the list goes on.
The one key difference between Google Analytics and AdWords is the default attribution model. Google Analytics uses last non-direct click, which ensures that the credit is assigned to the channel a user went through, as many users may bookmark a site then come directly to purchase at a later point.
It is important to use AdWords attribution when optimising the channels within your account, but looking at the overall picture in Google Analytics can lead to some discrepancies in the data. For example, if a user clicks a paid search ad and then converts through organic search a few days later, the credit will be assigned to your campaigns in AdWords and organic search in Google Analytics.
The best next step would be to read into the positives and negatives of each attribution model carefully and then decide which one is the best fit for your business (more on this below). Following this, test the different attribution models in both AdWords and Google Analytics and see how this impacts your existing data (both AdWords and Google Analytics allow you to compare different models).
It is important with any attribution model to understand how many touchpoints are in play for your client and their business goals and then use that to determine what model will work best. My recommendation would be to use data-driven attribution wherever possible. However, if your account is smaller than the requirement, a rules-based model that values all touchpoints to some extent would work best (meaning time decay, position-based or linear, depending on what you want to achieve).
For more information on attribution and to find out how you can use data to drive your business forwards, contact our team today.
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