Marketers should strive to give users helpful, in-the-moment controls that empower them to choose the ad experience that’s right for them.

The key trust levers to gain user’s trust are security, usefulness, control, and transparency.

To strengthen user trust, I recommend focusing on transparency, security, control, and usefulness.

Users benefit from the advertiser identity verification program by being able to see who’s responsible for the ads they are shown.

Google continuously invests in the security of its consumer-facing products to protect personal information and build user trust.

The Chrome and Android Privacy Sandbox aims to limit cross-site and cross-app user tracking.

The three use cases for the Privacy Sandbox are re-engaging audiences, performance measurement, and interest-based advertising.

FLEDGE supports re-engagement with users based on their actions on a website or app.

Optimized targeting finds new, high-performing audiences by building off existing targeting inputs, including the client’s first-party data, and being powered by privacy-forward machine learning models.

Optimized targeting builds off existing targeting inputs, including your client’s first-party data, to find new, high-performing Google audiences.

Two key benefits of machine learning in the changing privacy landscape are helping marketers understand performance when the path between ad interactions and conversions isn’t visible and helping marketers reach qualified audiences, even when some signals are limited.

When a subset of conversions can’t be tied to ad interactions, a marketer should use conversion modeling to provide a more complete picture of their advertising performance.

Since 2017, the ads ecosystem has seen an increased need for machine learning due to increasing user expectations, privacy regulation, and technology changes impacting campaign measurability.

Google machine learning modeling is different from other privacy-forward proposals because Google’s signed-in base of users allows their models to function independently of cookies and other identifiers.

Smart Bidding drives as many conversions as possible to maximize your client’s ROI or budget target.

Smart Bidding uses first-party data to optimize toward business goals.

If a client is looking to optimize for higher-value conversions, they should start by using readily available values or proxy values, such as average order value.

One way an eCommerce client can use Smart Bidding is by using business data, such as profit margin, to optimize for profit instead of revenue.

Smart Bidding drives performance in a privacy-safe way by using a client’s account conversion data and billions of signal combinations to set precise bids in each auction.

When an advertiser adds a user to their Customer Match list, the user’s data is kept confidential and secure.

The process used to keep Customer Match data private involves encrypting and hashing data.

Customer Match transforms first-party data into targetable audiences.

An example of Customer Match driving personalized, high-performance campaigns is seeking out your most loyal customers and generating more sales by reaching them with personalized offers.

Google Analytics 4 offers advertisers data controls that help businesses keep up with ever-evolving industry regulations.

The new data deletion feature in Google Analytics 4 offers businesses the option to remove an individual user’s data or specific fields within a property.

A main benefit of linking Google Analytics and Google Ads is that you can use event data for app install and conversion tracking.

To test the effectiveness of a new privacy-centric media strategy, recommend using a Holdback treatment group while keeping the control group the same.

A heavy-up geo-experiment strategy involves increasing the budget for the treatment group while keeping the budget of the control group the same.

To improve their marketing strategy with privacy-centric solutions, your client should speak to Google’s privacy specialist for customized recommendations and use the Build, Measure, Activate Framework.

To convince your client that third-party cookies will be deprecated, explain that Chrome is committed to withdrawing support for them, and most browsers already restrict and block them.

Someone would use Google Analytics for Firebase instead of Google Analytics 4 because it is app-focused and streamlines the deep-linking process, which is beneficial for app developers.

Smart Bidding can continue to be effective after the deprecation of third-party cookies because it combines clients’ conversion data with Google’s machine learning, ensuring durability.

Ad tech companies, including Google Ads product offerings, will use the Chrome and Android Privacy Sandbox APIs by integrating Privacy Sandbox signals along with other signals.

A condition required for using enhanced conversions is having Google Ads Conversion tracking as your conversion source.

While communicating about the Chrome and Android Privacy Sandbox, use the statements: Privacy Sandbox technologies are developed through an open and transparent process, and they are expected to evolve to continue improving privacy and utility.

Three durable solutions most clients can implement today to enhance their marketing strategy are enhanced conversions, Google Analytics 4, and Consent Mode.

To unite all non-keyword related Google advertising into one automated cross-product, use Performance Max Campaigns, which combine automation technologies across bidding, targeting, creatives, and attribution to drive growth in conversions and value.

An effective way to broaden your advertising strategy in a privacy-safe way is by advertising to users similar to your current high-LTV customers.

Smart Bidding is a durable, privacy-safe solution because it uses automated bid strategies and machine learning to optimize for conversions or conversion value in every auction.

Optimized targeting is a good fit for a cookieless world because it relies on using first-party data, Google audiences, and machine learning instead of third-party data.

To optimize profit rather than revenue, Smart Bidding can help your client by using business data instead of the transaction value to optimize for better returns.

To help your client reach consumers who will drive the highest conversion revenue, recommend using historical purchase data and optimizing toward lifetime value (LTV) with Smart Bidding.

Optimized targeting uses machine learning to predict the individuals most likely to convert and reaches them for you.

A recommended way to build a privacy-safe, full-funnel marketing strategy is to first build a comprehensive strategy and then pare it down by removing the most inefficient tactics.

Conversion modeling benefits a client’s business by ensuring measurement remains privacy-safe while fueling more efficient campaign optimization and bidding.

To compensate for conversion data loss, a short-term solution is to use the Google Analytics 4 Assisted Conversions Report to adjust the weights of their attribution model.

One benefit of testing cookieless geo-experiments is that it doesn’t rely on cross-site tracking for data.

The tool within Google Analytics 4 that enables advertisers to model user journeys in the absence of observed data is Consent Mode.

An example of a key benefit of conversion modeling is that it optimizes campaigns and bidding strategies, leading to better campaign results.

The benefit that isn’t provided by linking Google Analytics 4 and Google Analytics for Firebase is serving the right app ads on desktop and in YouTube Living Room.

The false statement is that conversion modeling can’t provide a complete picture of your performance when the path between ad interactions and conversions isn’t visible.

Using machine learning to model and plug gaps in data when users opt out of collection results in a more accurate picture of website and app performance, while also remaining privacy safe.

Google Analytics 4 addresses evolving measurement standards by allowing businesses to see unified user journeys across websites and apps and using Google’s machine learning technology to surface and predict new insights.

Google Analytics for Firebase offers three features: using first-party data while respecting user privacy, control over user data collection, retention, and removal, and event-centric, in-app behavioral analytics.

The key benefit of implementing Consent Mode is that it allows adjusting how the Google tag behaves based on the user’s cookie consent choices.

If your client isn’t sure how Consent Mode can benefit their business, tell them that Consent Mode adjusts the tags’ behavior based on users’ consent choices.

To encourage as many site visitors as possible to share their email, it is recommended to use a single sign-on (SSO) tool like Google or Facebook Connect, which can increase the email sharing rate by 20%-40%.

Google’s user privacy solutions help deliver value to advertisers by building user trust in brands, which influences users’ willingness to share their data and purchase from the brand.

The two Google tagging infrastructures that help clients build durable first-party data are site-wide tagging and Consent Mode.

If a customer wants to invest in a durable first-party data strategy, tell them to offer high discounts in exchange for consumers’ emails to build up a first-party database.

To prepare, clients should use robust first-party data collection solutions, adopt products that strengthen data signals sent to Google, and stay updated on the latest measurement best practices.

An example of applying user privacy solutions as a positive business opportunity is by offering a transparent and fair value exchange, which makes users more likely to share their information.

Traditional methods of tracking are becoming less reliable due to increasing privacy regulations and technological changes that limit the use of user data for measurement.

The Chrome and Android Privacy Sandbox API that aims to measure campaign performance without third-party cookies is Attribution Reporting.

The Chrome and Android Privacy Sandbox supports two key advertising use cases: interest-based advertising and re-engaging audiences.

To kick off their privacy strategy, your client should mitigate data loss by measuring data accurately through enhanced conversions. This step ensures compliance with privacy changes and maintains reliable data collection.