Measurement in a Cookieless World

Post-cookie and privacy-first world

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 and measure their campaigns…

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. Can you then rely on your attribution solution at all? Do you systematically use your first-party data to improve your marketing ROI? Brands that want to compete successfully in this new environment must develop capabilities in the following areas:

1/ Building a Strong First-Party Data Basis

The first step is to build a strong foundation in your 1st party data – what data to acquire and how, for what purpose, what business value it can bring, what is the appropriate technical infrastructure, governance and processes around it and then of course activating the data in your marketing efforts

We can specifically help you with:

2/ Measurement Methods Not Relying on Cookies

Many marketers have been used to rely on attribution modelling to measure the effectiveness of their digital marketing. This will no longer suffice – attribution modelling remains and will remain a useful tool for some tactical decisions as it provides the lowest levels of granularity but all the past issues with attribution modelling have been multiplied by the loss of user-level data due to recent changes – browser level, iOS, stricter privacy regulations etc.

Just using attribution models to evaluate your marketing will almost certainly lead to materially wrong conclusions and misallocated spending.

There are two gold standard techniques to measure marketing effectiveness that are independent of cookies and user-level data:

Lift Tests

The “ground truth” - measure the incrementality of media channels or even specific campaigns. 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 good enough from a statistics point of view is another complex topic. But we have seen that practically without exception all the most successful advertisers use the lift and incrementality tests as a key part of their marketing measurement toolkit.

Marketing Mix Modelling

A complex technique to measure the past effectiveness of marketing and optimize future performance. MMM used to be reserved for the largest advertisers but with recent developments in machine learning and statistical modelling, it is now much more widely available and cheaper.

Would you like to know more about MMM and find out what is your optimal spend for your marketing channels?

3/ Server-Side Tracking to Minimize Data Gaps and Maximize Control

The tracking and “data transportation” performed on the client side has always had issues but is becoming completely broken now with the advent of ITP, the end of 3rd party cookies etc. Fortunately, a new API-based layer of infrastructure (often called server-side tracking) is becoming available that allows brands to send data signals to platforms like Facebook, Google or TikTok in a more privacy-safe way.

This allows brands to:

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Predictive analytics in GA4

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:

  • Purchase probability: The probability that a user who was active in the last 28 days will log a specific conversion event within the next 7 days.
  • Churn probability: The probability that a user who was active on your app or site within the last 7 days will not be active within the next 7 days.
  • Predicted revenue: The revenue expected from all purchase conversions within the next 28 days from a user who was active in the last 28 days.

You can create audiences based on these values and leverage them in your advertising campaigns – for example, you could exclude users who have a high purchase probability as you may assume that these users will buy anyways and you don’t have to spend additional marketing dollars on them (test it) or try to win back an audience with higher churn probability with a special campaign or communication.

Data export to BigQuery

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

  • Joining GA4 data with other sources in your company – CRM, customer data, backend sales, and margin data.
  • Using GA4 data for advanced customer analytics for a better understanding of customer lifetime value or churn prediction.
  • Automate reporting in your BI tool.
  • Move the data to your on-premise or cloud data lake and data warehouse.

Example use cases might be

  • Joining GA4 data with other sources in your company – CRM, customer data, backend sales, and margin data.
  • Using GA4 data for advanced customer analytics for a better understanding of customer lifetime value or churn prediction.
  • Automate reporting in your BI tool.
  • Move the data to your on-premise or cloud data lake and data warehouse.

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!

Free data driven attribution in GA4

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.

World without Cookies and GA4

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.