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Measuring media effectiveness and making informed budget allocation decisions is one the key responsibilities of marketing executives. While it has always been a challenge, it has been getting more and more difficult over the past years as user-level data – often used for granular measurement with attribution modelling – has been less available due to a number of factors:
It is not unusual that brands today miss 40-50% of user data behaviour due to missing consents or technical changes on iOS or browser side. Should you then make major budget decisions based on your attribution model with so much data missing?
The answer to this lies in using a framework employing 3 methods together
Marketing Mix Modelling (MMM) is a time-proven yet complex way to measure the impact of media spend and other activities or events (such as discounts and pricing, changes in distribution, promotions, competitors’ activity or external events) on your business KPIs – most often sales or new customer acquisition.
There has been significant progress over the last 5 years in MMM techniques and this has made MMM much more accessible to a larger group of advertisers – but arriving to a reliable and useful production-level model still remains a demanding exercise requiring experience and expertise.
Experiments help measure the “ground truth” – true incrementality of media channels, specific campaigns or promotions or other changes and interventions – free delivery, change in pricing, changes in distribution etc.
There are multiple techniques and approaches and no silver bullet here – for example, some methods are great for big markets like the US but will not work in more fragmented geographies, finding the balance between what is practical and robust enough from a evaluation point of view is another complex topic.
But we have seen that practically without exception all the most successful advertisers use incrementality tests as a key part of their marketing measurement toolkit.
We can help with:
Attribution modelling used to be the primary tool digital marketers used to measure channel performance – it always had serious limitations (even with advanced algorithmic models) such as generally ignoring brand baseline, ignoring all offline world, underrepresenting long-term brand building channels or in general channels with sparse digital touchpoints or limited insights into channel diminishing returns and scalability etc.
In recent years a new major challenge appeared with privacy restrictions (both technical – iOS and web browsers, and regulatory) resulting in often 40-60% of user touchpoints not being tracked. This has massive implications for attribution models reliability and attribution vendors try to approximate the missing data but the accuracy of such approaches can be often questioned.
We can help with:
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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.