Google has rolled out the data driven attribution to most accounts now. It requires quite a large amount of data to work though.
Using data driven models simplify optimisations. When the models work they make it easier to analyse campaigns based on the amount of revenue or conversions they have contributed with. This makes it easy to optimise based on one KPI instead of looking at click-troughs, clicks etc.
Data driven attribution uses machine learning and allocates conversion credits to the different marketing touch-points along the customer journeys.
The two different data driven attribution models
There are two widely used algorithmic attribution models:
– Shapley value, this is the one google uses
– Markov model
More on the difference between these here: https://windsor.ai/shapley-value-vs-markov-model-in-marketing-attribution/
The markov model can work on less data and is faster computationally so its easier to go down to keyword and content level in the analysis when there are less conversions.
Data driven attribution takes into account all marketing touchpoints in a customer journey. So it is multi-touch attribution.
Compared to last-touch or last click attribution it makes it possible to move into early funnel marketing channels to acquire more customers.
Example of multi touch attribution
Here is the example Google provides on how the data driven attribution works:
You own a tour company in New York City, and you use conversion tracking to track when customers purchase tickets on your website. In particular, you have one conversion action to track purchases of a bike tour in Brooklyn. Customers often click a few of your ads before deciding to purchase a ticket.
Your data-driven attribution model finds that customers who click your “Bike tour New York” ad first, and then later click “Bike tour Brooklyn waterfront,” are more likely to purchase a ticket than users who only click on “Bike tour Brooklyn waterfront.” So the model redistributes credit in favor of the “Bike tour New York” ad and its associated keywords, ad groups, and campaigns.
Now, when you look at your reports, you have more complete information about which ads are most valuable to your business.
More on this here: https://support.google.com/google-ads/answer/6394265?hl=en
In this example the earlier touch-points also get conversion credits with multi-touch attribution, for example the first touch-points with Adexchange and Light Reaction.
Data requirements for Google data driven attribution
To get started with google data driven attribution the account needs 600 conversions in a 30 day period and 15 000 clicks. To continue using the model a level of 400 conversions has to be maintained. If this level is not maintained then the model will be switched to linear model.
Windsor.ai attribution can work with less data and we can go to keyword level much easier.
We recommend to switch to multitouch attribution or data driven attribution because it makes the analysis so much easier. It becomes possible to look at one KPI and make decisions based on that.
Especially with complex customer journeys and programmatic platforms it can sometimes be directly misleading to look at clicks, click-trough rates etc. So it is much better to really look the contribution of the conversions and put that into perspective to the costs to get a data driven ROAS or a data driven CPA.