Google's playbook provides privacy and measurement solutions for digital marketing.
The playbook addresses consumer privacy expectations and impending regulations.
It emphasizes building relationships with first-party data and using tools for accurate measurement.
Google provided essential details about its privacy and measurement solutions in its digital marketing playbook.
The digital marketing playbook is timely amidst the ongoing and ever-changing consumer privacy expectations and impending regulations such as the Montana TikTok ban and the AMERICA Act.
In the 31-page guide, Google outlines need-to-know updates for advertisers that will affect how performance is measured and how to connect with consumers in a meaningful way.
The playbook introduces how different key players, such as marketers, agencies, and executives, play a critical role in the future of advertising while keeping privacy at the top of mind.
Building Relationships With First-Party Data
The first section of Google’s playbook is dedicated to crafting a first-party data strategy.
Google outlines the importance of providing a meaningful and appropriate value exchange to strengthen customer relationships.
Part of establishing trust in capturing first-party data is putting the consumer in control of their information. This is where Apple’s App Tracking Transparency (ATT) policy comes in for iOS apps. Advertisers should review the ATT policy and determine the best action for consent in their iOS apps.
Another critical component of a first-party data strategy is integrating data sources and platforms, such as a CRM platform, into Google’s advertising and measurement tools, like Google Ads and Google Analytics.
Tools & Platforms For Accurate Measurement
In the 2nd chapter of the digital marketing playbook, Google outlines vital areas of learning:
Creating a solid tagging foundation
More accurate conversion measurement with first-party data and machine learning.
Connecting and integrating multiple data sources to Ads Data Hub
Privacy-focused app measurement
The transition to Google Analytics 4 for measurement
What the future of measurement looks like.
Unsurprisingly, marketing campaigns have been increasingly more difficult to track success.
Google’s solution? Adopt a solid sitewide tagging infrastructure.
Google provides numerous options for sitewide tagging, including:
The Google Tag
Google Tag Manager
Google Tag Manager 360
For enhanced privacy and security, server-side tagging is available for both versions of Google Tag Manager.
Another way Google has adapted to privacy changes is by introducing enhanced conversions for the web. This type of conversion tracking allows sitewide tags to collect first-party data (after a user consents), which is then sent to Google.
This is then where conversion modeling comes into play.
Per Google, conversion modeling will remain a key component of their measurement solutions.
Conversion modeling uses machine learning to capture and cross-reference the different signals for better performance.
Google stated in the playbook:
Wherever possible, we directly integrate conversion modeling into Google’s ads products,
so you’ll automatically find this modeled data in your conversions reporting column. This gives you insight into
conversions you otherwise would not have recorded, such as platform restrictions limiting the use of third-party
cookies or other identifiers.
Ads Data Hub for marketers uses BigQuery to aggregate first-party data and joins it with Google event-level ad campaign data. It also guarantees that personal user data is protected through privacy checks and is never available to advertisers.
Privacy-Focused App & GA4 Measurement
Following the rollout of Apple’s ATT policy, marketers should prioritize implementing on-device conversion measurement and Google Analytics for Firebase SDK for their apps.
On-device conversion measurement allows user interactions with app ads to match with app conversions, all without the user identification leaving a user’s device.
The Firebase SDK can be added to Android and iOS apps, allowing cross-platform measurement capabilities.
To meet privacy expectations, Google Analytics 4 has advanced machine learning to help bridge customer data gaps.
This includes conversion and behavioral modeling within the GA4 property. By default, the data-driven conversion model is automatically used. However, advertisers can change the default models.
Privacy Sandbox Updates
First introduced in 2019, the Privacy Sandbox continues to evolve.
Google’s tag solutions are built to integrate with the Privacy Sandbox Attribution Reporting API.
This means that the Reporting API will only report information in a way that doesn’t share consumer identity characteristics. Advertisers can expect more aggregate data around conversion tracking.
Using Platform Insights To Help Drive Growth
The last chapter in Google’s digital marketing playbook focuses on taking action utilizing the first two chapters.
How to engage first-party audiences at scale
Using AI to discover new audiences
Staying up-to-date on privacy changes
With first-party data, marketers can adopt Customer Match to reach users across Google properties like Search, Gmail, YouTube, and Shopping. First-party data works well with Google’s Smart Bidding models to optimize for ROI.
To expand reach outside of first-party data audiences, marketers can use Google audiences that aggregate a variety of signals using AI to reach target audiences. These signals include:
Interests based on web and app activity
Context during real-time auction bidding
The Topics API in the Privacy Sandbox now supports interest-based ads, meaning a user’s browser can shed light on user interests without tracking specific site activity.
Google’s digital marketing playbook summarizes many announcements made in the last 6-12 months.
Whether marketers have already introduced a privacy strategy or are just getting started, the guide is a great starting point.
Get the complete playbook from Google here.
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