“You can have data without information, but you cannot have information without data.”
Daniel Keys Moran
What is the best approach how to create an infrastructure of marketing data that gets you reliable answers to your crucial business questions? If you are unsure about the incremental
MMM has now become the #1 marketing tactic tool advertisers have adopted. Get to know more about Marketing Mix Modelling solutions for CMOs, marketing managers, and other executives with marketing
There is no doubt about it – we are moving into a “post-cookie” world which will have a profound impact on marketers and the ways they use data to optimize
Marketing Mix Modelling and Attribution Modelling have the same basic goal: measure the business impact of marketing channels and find out how to allocate marketing budget between channels in order
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One of the new and exciting features of GA4 is called “predictive metrics” – with these you can learn about your customers and their shopping behavior. These are added automatically to your data and are based on Google’s machine learning expertise.
GA4 currently includes the following predictive metrics:
Google BigQuery is a cost-effective and highly-scalable cloud data warehouse optimized for high performance on very large data sets. With GA4 you can export all your event-level data to BigQuery for additional analytics or data science initiatives.
Example use cases might be
Example use cases might be
You will need to have a Google Cloud account set up and maintained for this purpose. That is why we are here and can solve all the infrastructure for you and with you!
Since 1/2022 GA4 has made a data-driven attribution model available to all users – unlike in Universal Analytics where the DDA model was only available to GA360 customers.
DDA is an algorithmic attribution model that quantifies the value of each touchpoint in the user journey (such as campaign click) and it does so by smart modelling behind the scenes without human bias that is always present in rule-based models.
The DDA model in GA4 is also better than the one on GA360 as it takes into account up to 50 touchpoints in the user journey vs. the GA360 only took 4 touchpoints.
It is also possible to set the default attribution model to DDA (or another model) from the old Last Non-Direct Click. And then all your reports and data exported to BigQuery will use the newly selected model as default.
DDA in Google Analytics 4 excludes almost all direct visits from receiving credit – you may or may not like that, but as it’s been always the case with attribution we recommend validating any model using marketing experiments.
GA4 is built for a world where more and more users opt out of cookie consent and other methods for data collection.
Google uses machine learning modeling to fill in the data gaps – some of these are already in the current GA4 others will be deployed in the future.
For example, modelling conversions allows GA4 to properly attribute conversions without user identification – this is crucial for optimized advertising campaigns and automated bidding. This covers situations such as some browsers limiting the time window for first-party cookies, conversions for unconsented users, Apple’s App Tracking Transparency (ATT) impacts, cross-device user behaviour, and others.