Google has provided key details about its privacy and measurement solutions in its digital marketing playbook.
The digital marketing playbook is timely given the ongoing and ever-changing consumer expectations for privacy and upcoming regulations like the Montana TikTok ban and the AMERICA Act.
In the 31-page guide, Google outlines important updates for advertisers that will impact how performance is measured and how they can engage with consumers in meaningful ways.
The playbook introduces how various key players such as marketers, agencies and executives play a crucial role in the future of advertising while keeping privacy in mind.
Building relationships with first-party data
The first section of Google’s playbook is dedicated to developing a first-party data strategy.
Google emphasizes the importance of providing a meaningful and appropriate exchange of value to strengthen customer relationships.
Part of building trust when collecting first-party data is putting the consumer in control of their information. This is where Apple’s App Tracking Transparency (ATT) policy for iOS apps comes into play. Advertisers should review the ATT policy and determine the best consent measure in their iOS apps.
Another important part of a first-party data strategy is the integration of data sources and platforms, such as a CRM platform, with Google’s advertising and measurement tools, such as Google Ads and Google Analytics.
Tools and platforms for accurate measurements
In the second chapter of the Digital Marketing Playbook, Google outlines key learning areas:
- Establishing a solid tagging foundation
- More accurate conversion measurement with first-party data and machine learning.
- Connect and integrate multiple data sources with Ads Data Hub
- Privacy oriented app metering
- Transitioning to Google Analytics 4 for measurement
- What the future of measurement looks like.
Not surprisingly, it’s getting harder and harder to track the success of marketing campaigns.
Google’s solution? Establish a robust, site-wide tagging infrastructure.
Google offers numerous options for site-wide tagging, including:
- The Google tag
- Google Tag Manager
- Google Tag Manager 360
For more 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 advanced conversions to the web. This type of conversion tracking allows site-wide tags to collect first-party data (after consent from a user), which is then sent to Google.
Google matches the hashed data with registered Google accounts in order to attribute corresponding conversions to search and YouTube ads.
This is where conversion modeling comes into play.
According to Google, conversion modeling will continue to be a key component of their measurement solutions.
Conversion modeling uses machine learning to capture and cross-reference the different signals for better performance.
Google explained in the playbook:
Wherever possible, we integrate conversion modeling directly into Google’s ad products.
Therefore, you will automatically find this modeled data in your conversion reporting column. This gives you insight into
Conversions you wouldn’t otherwise have tracked, such as B. Platform restrictions restricting the use of third-party providers
Cookies or other identifiers.
Ads Data Hub for Marketers uses BigQuery to aggregate first-party data and combine it with event-level Google ad campaign data. It also guarantees that personal user data is protected by privacy checks and is never exposed to advertisers.
Privacy oriented app and GA4 measurement
Following the implementation of Apple’s ATT policy, marketers should prioritize the implementation of on-device conversion measurement and the Google Analytics for Firebase SDK for their apps.
On-device conversion measurement allows user interactions with app ads to be matched with app conversions without user identification leaving a user’s device.
The Firebase SDK can be added to Android and iOS apps, enabling cross-platform measurement capabilities.
To meet privacy expectations, Google Analytics 4 features advanced machine learning to fill gaps in customer data.
This includes conversion and behavior modeling within the GA4 property. By default, the data-driven conversion model is used automatically. However, advertisers can change the default models.
Privacy sandbox updates
First introduced in 2019, the Privacy Sandbox is constantly evolving.
Google’s tagging solutions are designed to integrate with the Privacy Sandbox Attribution Reporting API.
This means that the reporting API will only report information in a way that does not reveal consumer identity characteristics. Advertisers can expect more aggregated data around conversion tracking.
Use platform insights to drive growth
The final chapter in Google’s digital marketing playbook focuses on taking action, leveraging the first two chapters.
- How to engage first-party audiences at scale
- Discover new target groups with AI
- Stay up to date on privacy changes
With first-party data, marketers can use Customer Match to reach users across Google products like Search, Gmail, YouTube, and Shopping. First-party data combines well with Google’s Smart Bidding models to optimize ROI.
To extend reach outside of first-party data audiences, marketers can use Google Audiences, which uses AI to aggregate a variety of signals to reach audiences. These signals include:
- Demographic Information
- 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, which means a user’s browser can provide insight into user interests without tracking specific site activity.
Google’s digital marketing playbook summarizes many announcements over the past 6-12 months.
Whether marketers have already implemented a privacy strategy or are just starting out, the guide is a good place to start.
Get Google’s full playbook here.
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