Navigating a Privacy-Focused Ecosystem with DV360

2022-12-05 | Fact sheet | Insights

Strategies to live up to users’ privacy expectations

In response to a growing demand for privacy, new regulations have been adopted and cookies have been restricted by browsers. The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are two regulations that govern the collection and use of data in the European Economic Area and the United States. In addition, technical changes, such as increased browser restrictions, are impacting traditional data collection methods, such as third-party cookies and device identifiers. Therefore, advertising must evolve as the industry becomes increasingly complex. This is the only way to maintain a bond between users, marketers, and publishers, and ensure universal access to high-quality information on the Internet.

This article describes four strategies on how to use DV360 to navigate a privacy-focused ecosystem. To provide tangible guidance on a privacy-safe approach to media activation, these strategies are translated into different examples for features.

Invest in durable measurement


A first-party tagging solution can preserve your measurement in times of decreasing amounts of third-party cookies. First-party tagging solutions set cookies in the advertiser’s domain. These are less prone to changes that impact third-party cookies. DV360's future product solutions are also expected to rely more and more on data derived from first-party tagging solutions. Floodlight activities should be implemented using the global site tag or Google Tag Manager using the Conversion Linker. By opting in to Enhanced Attribution, you will preserve post-click conversions for Floodlights by improving attribution signals across the Google Marketing Platform. A DCLID value is automatically appended to the landing page URL when a user clicks on an ad.

Conversion Modeling

As third-party cookies and further identifiers decline, other attribution methods are becoming more relevant. Here's where Conversion Modeling comes in. This is a method for estimating online conversions. The best estimate of actual campaign performance comes from combining measured data with conversion modeling. Google uses its available data to surface lost conversions that couldn't be measured traditionally. Lost conversions are unattributed conversions. In order to fill in the measurement gaps, Google uses machine learning to predict their attribution. In DV360, conversion modeling is by default enabled for attribution models.

Consent Mode

Furthermore, if you haven't already, you should start using Consent Mode. To obtain visitor consent, Consent Mode integrates with your Consent Management Platform (CMP) or custom implementation. Consent Mode receives your users' consent choices from your cookie banner or widget and dynamically adapts the behavior of Google Analytics, Ads, and third-party tags that create or read cookies. With Consent Mode, your Google tags are adjusted according to the consent decision of your users, so Google can model for conversion gaps to recover conversion data. Based on this, it can be indicated whether consent has been granted for Google Analytics and Google ads cookies. Google's tags are dynamically adapted, only utilizing cookies for the specified purposes when consent has been given by the user. This article explains Consent Mode in more detail. You can use Consent Mode with Floodlights for conversion tracking and your data segments.

Enchanced Conversions

By augmenting your first-party data with Enhanced Conversions (EC), you can further increase the basis for conversion modeling. The conversion tag captures a field you specify, such as an email address, when Google signed-in users view your ad and convert on your website. This is then matched against Google hashed users’ data and a conversion is reported in your account. This way, a more accurate conversion measurement by increasing the observed data and improving the overall quality of conversion modeling is achieved. You must obtain consent from your end users for the passing and use of any PII you send to Google through EC with Floodlight.

Through a linked GA4 account, Enhanced Conversions can also be used.

Frequency Management

With Frequency Management another solution is offered to DV360 clients that supports all digital identity scenarios: signed-in users, signed-out users, and limited-signal users including publisher information. For Google Ad Manager Inventory, Google provides modeled frequency where tracking the actual frequency is limited. This way, the effects of potential overexposure of users to campaigns caused by tracking limitations are decreased and advertisers' campaign budget is re-invested in reaching further users in their target audiences (also on anonymous inventory, including impressions served to Safari 12+). Ultimately, this improves efficiency and campaign reach and is also done using machine learning.

Another solution that supports advertisers with mixed media campaigns in managing campaign exposure of users is the Frequency Management Value Quantification tool. You can find this setting within the Combined view at campaign level. It analysis the number of additional users reached becaused of media consolidation and frequency management under a single campaign.

With respect to user privacy at all times, these capabilities reduce media waste and improve reach, especially when used with a media unification activation strategy on DV360 across all formats, inventory types, and media buying options.

Target Frequency

Traditionally, Frequency Caps have been used to control the exposure of users to campaigns. However, frequency capping is meant to prevent too many contacts but does not ensure that people who have been reached with a campaign receive a follow up message with the same or other creative to create a sustainable effect. Individual contact fizzle out their effect. This is why Google recently introduced the feature “Target Frequency” which is an optimization solution that drives higher ad viewership. You can input your desired frequency and DV360 will optimize towards this goal while using all available and modeled information and respecting user privacy. This is available for bumper ads, skippable as well as non-skippable YouTube video ads.

Use more automation and machine learning

Reaching users and measuring performance has shifted from relying on user IDs and cookies to leveraging first-party data, browser APIs, and contextual signals. Due to decreasing user and event-level data, the effectiveness of a digital strategy is more dependent on algorithms and machine learning then ever. With budgets continuously migrating from TV and radio to digital, enabling consolidated buying and serving across channels and formats through media unification - regardless of identity signals available - is becoming increasingly important as a means of future-proofing your digital marketing efforts.

Google Audiences

Google Audiences are designed with robustness in mind and deliver in any environment by utilizing the best combination of signals. Whenever user signals are available, Google uses them along with contextual signals to power machine learning and create Google Audiences. In the absence of user signals, for example when someone opts out or cookies are constrained, Google uses group behaviour signals instead, and combines them with the context of the page where the ad appears as input. This is how algorithms are able to determine relevance and create a variety of Google Audiences. Recently, Google Audiences entered a new phase of the interest-based advertising evolution aiming to understand the impact of the latest developments in the market. This includes an update to the simulation of Chrome’s publicly available Topics API.

In Google Audiences, insights about user activity on Google owned and partner properties are used to understand user preferences and target based on those preferences. You can choose from out-of-the-box In-Market or Affinity segments or create your own Custom Intent (Display, Video) Audience, Custom Intent (YouTube) Audience, or Custom Affinity Audience or implement strategies around Live Events or Demo targeting to reach your awareness and consideration goals.

Auto and Custom Bidding

You can also optimize your campaigns using machine learning in DV360 with Auto and Custom Bidding. In an automated bid strategy, your line item's (your tactics within a campaign) are optimized based on the likelihood that:
  • As a result of an impression, a chosen outcome will occur (a click, conversion, view, etc.).
  • Impression prices are optimal (compared to the market).
Inventory signals, audience lists, and creative effectiveness are some variables used by automated bid strategies to predict outcome and define bids based on the outcome prediction.

Custom Bidding incorporates your specific metrics on top of Auto Bidding. For example, you can use your margin data to optimize your campaigns towards customer profitability. In our article "Increase Campaign Efficiency with Custom Bidding", we discuss this in more detail. Experiments and Lift can help you deploy A/B tests to find the best way to deliver campaigns by testing different optimization options against each other.

YouTube Floodlight Tracking

The Floodlight attribution methodology is now available in Display & Video 360 for powering bids for conversion-optimized YouTube campaigns as well. With this launch, YouTube and non-YT line items now use the same optimization and attribution methodology. This ensures a media-unified approach to attribution reducing potential uncertainty in reporting and optimization. Due to differences in cookie and device ID settings, in some situations Display & Video 360 uses here machine learning and historical data to model conversions, too.

Optimized Targeting

DV360 also offers Optimized Targeting which is the evolution of the audience expansion solutions. This feature allows your campaign to explore customers who have the highest chance of showing interest (conversions/ clicks/ views) beyond your manually-selected audience segments. Google uses Machine Learning to identify new customers based on their characteristics and web browsing habits.

Execute a privacy-safe first-party data strategy

Due to the third-party cookie changes, implementing a privacy-safe first-party data strategy becomes increasingly important as it can help boost customer loyalty and lifetime value.

Customer Match

You can re-engage your customers with Customer Match in a privacy-safe manner. First-party audiences can be uploaded to match against signed-in Google users based on email addresses, phone numbers, user identifiers, and mobile device identifiers. A one-way hashing mechanism is used on the marketer's side to hash uploads. Using Customer Match, you can reach offline customers or deploy an activation strategy based on customer lifetime value analysis and predictions to deliver tailored messages. A Customer Match list can store the following attributes of its members:
  • The advertiser-defined lifetime value for the user, in microseconds.
  • An integer between 1 and 10 (inclusive) that represents the lifetime value bucket of the user.

Customer Match lists should be updated frequently to maximize their impact.


PAIR (Publisher Advertiser Identity Reconciliation) is another solution that has recently been introduced. By using advanced data encryption methods, you can match your first-party data with the ones of publishers in a secure and privacy-safe way without relying on third-party cookies. The publisher can freely chose a Supply Side Platform and you can activate the marketing use cases based on the matched data in DV360. Advertisers and publishers retain full control of their audience data with this feature since it doesn't allow data pooling.

Leverage the integration with GA4

In an era when cookies are becoming scarcer, GA4 offers solutions that can cater to these developments and adapt as the ecosystem changes. At the same time, it offers a direct integration with DV360 available to both, GA4 and GA4 360 clients to support them with Predictive Audiences, Channel Attribution, and optimization opportunities.

Predictive Audiences

Predictive audiences are audiences with at least one condition based on a predictive metric. By applying machine-learning to your dataset, Google Analytics automatically enriches your data to predict your users' future behavior. Predictive metrics provide insights into your customers based on structured event data. For example, you could create an audience for 'likely 7-day purchasers' that includes users who are expected to purchase within the next 7 days. Those who use the free GA4 version can create up to 100 audiences, while users of GA4 360 can build up to 400 audiences that can be used with any linked product account.

Cross-Channel Report & Optimization

As a result of integrating your GA4 account with your DV360 partner, you will also be able to see DV360 as a traffic source in GA4's cross-channel report for campaign optimization, as well as use DV360 campaign and cost data to optimize programmatic campaigns in a more user-centric way. Moreover, GA4 events can be used for Custom Bidding and real-time optimization.


As you navigate the changing advertising ecosystem, DV360 provides many features that support your privacy-preserving activation strategy. Deploying them while tailoring them to your business characteristics and needs not only solves for the increased user demand on transparency, choice, and control but can also create a competitive advantage for your business when understood well and used to the fullest of their potential.

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