How to Leverage AI and First-Party Data for Privacy-First Google Ads?

The digital advertising landscape is changing significantly. Privacy rules like GDPR and CCPA, along with browser changes such as the end of third - party cookies, are making businesses rethink their Google Ads strategies. As shown in Google DeepMind's Cybersecurity Framework released on April 2, 2025, AI and technological progress can disrupt and boost the digital ecosystem. Marketers face the challenge of running effective ad campaigns while respecting user privacy. The solution lies in using privacy - focused tools, strengthening first - party data strategies, collaborating across functions, using AI for optimization, and adapting to regulatory changes. This guide combines insights from industry leaders like Victorian Plumbing, LPP S.A., and Openbank to offer practical steps for this new era of privacy - first advertising.

I. Embrace Privacy-Centric Measurement Tools

The deprecation of third-party cookies has created a gap in conversion tracking, leaving marketers scrambling for alternatives. Traditional methods relied heavily on user-level data, but privacy regulations now demand a shift toward aggregated, anonymized insights. Victorian Plumbing faced this challenge head-on when a significant portion of their audience opted out of tracking. Their solution? Implementing Consent Mode and Enhanced Conversions, which use AI to model lost data while respecting user privacy choices.

Consent Mode functions by modifying the behavior of Google tags according to user consent. If a visitor declines cookies, it sends non-identifiable "ping" data to fill gaps in conversion reporting. Enhanced Conversions go further by hashing first-party data (e.g., email addresses) to match ad interactions without exposing personal details. For Victorian Plumbing, this approach recovered 12% of lost conversions and accurately attributed millions in revenue.

The key to success lies in seamless integration. Start by auditing your current tracking setup in Google Tag Manager, then enable Consent Mode and Enhanced Conversions in Google Ads. Test incrementally—Openbank, for instance, ran A/B tests to validate lead-scoring models before full deployment. Privacy-safe measurement isn’t just about compliance; it’s about reclaiming visibility into campaign performance.

II. Strengthen First-Party Data Strategies

Relying on third-party data is no longer sustainable. Reserved (LPP S.A.) demonstrated this by leveraging Customer Match to segment audiences, transforming first-party data (e.g., purchase histories) into actionable Google Ads lists. Their advertising campaigns were focused on reaching high-intent shoppers, resulting in a 15% growth in Search conversions and YouTube conversions that were 25% higher.

The true potential of first-party data is unlocked when integrated with AI. Openbank’s predictive lead-scoring model analyzed anonymized mortgage application details (e.g., loan terms, property values) to prioritize high-value leads. This fed into value-based bidding, slashing cost-per-lead by 26% while boosting mortgage signings by 29%.

For example, Topkee’s services can streamline this process on Google Ads:

Data Collection: Utilize tools like TTO to automate conversion tracking and consolidate first-party data from forms, loyalty programs, or simulations.
Activation: Store and unify insights with Google Analytics 4 (GA4), enabling cross-channel analysis.
Optimization: Deploy Smart Bidding (e.g., Target ROAS) to let AI adjust bids in real time based on first-party signals.

The goal is a closed-loop system where data powers AI, and AI maximizes ROI—mirroring Topkee’s approach with clients through tailored solutions like attribution remarketing and TM settings for granular audience segmentation on Google Advertisement.

III. Foster Cross-Functional Collaboration

Privacy compliance often stalls when legal and marketing teams work in silos. Victorian Plumbing avoided this by involving their Data Protection Officer (DPO) early in tool evaluations. Together, they assessed Consent Mode’s GDPR compliance, ensuring tags aligned with user preferences without sacrificing performance.

WPP’s global DPO, Gareth Burkhill-Howarth, emphasizes that privacy is now a company-wide priority, not just a legal checkbox. Regular workshops between marketing, legal, and tech teams can align goals—for example, designing clear consent banners that balance UX and compliance. Openbank’s cross-functional team even co-developed their lead-scoring model, ensuring it met regulatory standards while driving conversions.

Practical steps:

Document data flows to identify compliance risks (e.g., data deletion protocols).
Educate teams about Privacy Sandbox APIs and the changing regulatory landscape.
Use plain language in consent requests; vague wording increases opt-outs.

Collaboration turns privacy from a hurdle into a competitive edge.

IV. Leverage AI for Privacy-Safe Optimization

AI bridges the gap when data accessibility is constrained by privacy regulations. Google’s Smart Bidding within Google Ads exemplifies this by combining first-party data with modeled insights to automate bid adjustments dynamically. For instance, Openbank’s predictive algorithms analyzed anonymized behavioral signals (e.g., time spent on mortgage pages) to preemptively identify high-intent leads, improving targeting precision without compromising privacy. Performance Max campaigns on Google Ads further amplify this capability, using AI to distribute budgets across channels based on real-time engagement signals like ad interactions or offline conversions.

However, the effectiveness of AI relies on the quality and granularity of the data. Reserved enhanced their Smart Bidding performance in Google Advertisement by integrating Customer Match lists, enabling Google’s algorithms to extrapolate lookalike audiences from verified first-party profiles. Similarly, tracking micro-conversions (e.g., PDF downloads, video views) in GA4 is critical for training AI models.

Emerging tools like the Privacy Sandbox’s Topics API (which groups users by interest cohorts instead of individual tracking) signal the industry’s shift toward privacy-compliant targeting. Early adoption of such technologies, coupled with Topkee’s attribution remarketing solutions (e.g., segmenting users by engagement depth via TM settings), can future-proof campaigns on Google Advertisement while maintaining performance.

V. Proactive Adaptation to Regulatory Changes

Regulations such as GDPR and CCPA mark just the beginning of privacy compliance requirements. Although Chrome has delayed the deprecation of cookies, this does not eliminate the need for businesses to prepare. Browser updates and regional laws, like Austria's strict YouTube tracking regulations, necessitate agility in adaptation.

Reserved put in place a worldwide strategy encompassing quarterly reviews of its data operations and testing tools like Enhanced Conversions on a market-by-market basis.

Conclusion

The future success of Google Ads depends on achieving a balance between privacy and performance. By implementing Consent Mode, investing in first-party data collection, and aligning teams around privacy objectives, businesses can prosper in a cookieless environment. Those who leverage AI effectively and adapt proactively will distinguish themselves from competitors. It is advisable to start with small steps, such as enabling a single privacy tool or organizing a cross-team workshop, and then scale efforts as insights are gained.

For assistance, businesses can contact Topkee's professional consultants to obtain the most effective marketing strategies.