In today’s hyper-connected digital ecosystem, consumers interact with brands through a variety of devices, including smartphones, tablets, laptops, connected TVs (CTVs), smartwatches, and even gaming consoles. These interactions are rarely linear or confined to a single platform. For marketers, this poses a considerable challenge: how can they recognise and understand the same user across multiple touchpoints to provide a seamless and personalised experience?

The answer lies in cross-device targeting powered by federated identity graphs—a transformative solution that marries data accuracy, privacy, and performance. This technology enables brands to construct comprehensive, privacy-compliant user profiles by linking disparate device interactions without centralising sensitive information. As a result, advertisers gain the ability to target users intelligently across their digital journey, ensuring relevance, reducing ad fatigue, and enhancing return on investment (ROI).

Understanding Identity Graphs

At the foundation of cross-device targeting lies the identity graph. An identity graph is essentially a sophisticated database that maps various identifiers associated with a single user. These identifiers may include:

• Email addresses
• Mobile advertising IDs (MAIDs)
• Device identifiers (like CTV IDs)
• Login information
• IP addresses
• Behavioural and transactional data

The primary goal of an identity graph is to construct a unified, persistent user profile that connects all the digital identifiers a person uses, enabling precise targeting and improved personalisation across platforms.

Deterministic vs. Probabilistic Matching

Identity graphs use two primary techniques to associate devices and behaviours with users:

1. Deterministic Matching

This involves the use of authenticated data—information that users provide through explicit actions such as logging into an app or opening an email. Deterministic links are highly accurate, typically achieving over 90% accuracy, as they rely on verified user actions.

2. Probabilistic Matching

Probabilistic models use inferred signals to associate devices and behaviours with users. These signals may include shared IP addresses, time-of-day browsing habits, or patterns of geographic location. Although less precise than deterministic matching, modern probabilistic techniques, powered by machine learning, can achieve confidence levels of upwards of 73% in accurately linking data.

Most modern identity solutions employ a hybrid approach, combining deterministic and probabilistic methods to maximise both accuracy and scalability.

What Are Federated Identity Graphs?

A federated identity graph is an evolution of the traditional identity graph. Rather than relying on a single, centralised data source, it aggregates and links multiple identity graphs across organisations and systems, creating a more comprehensive and distributed view of the user. It allows multiple data providers to maintain control of their information while contributing to a shared identity framework.

Federated Architecture Explained

Federated identity graphs work through a decentralised, privacy-preserving architecture, where each participant processes data locally and only shares encrypted metadata. This approach has several critical advantages:

• No central storage of sensitive data, reducing the risk of breaches
• Compliant with global data privacy laws such as GDPR and CCPA
• Combines first-party, second-party, and third-party data from multiple entities
• Supports real-time identity resolution across platforms and devices

By utilising federated learning and secure computation techniques (like differential privacy), these systems enable scalable, compliant identity resolution without compromising user privacy.

The Power of Cross-Device Targeting

Cross-device targeting allows brands to recognise users across multiple devices and deliver personalised, context-aware advertising experiences. Federated identity graphs enhance this capability by providing a deeper, more accurate view of the user’s digital behaviour.

Use Cases and Benefits

1. Audience Recognition Across Devices

Marketers can identify users as they switch from smartphones to tablets to smart TVs, enabling consistent messaging and retargeting strategies.

2. Optimised Campaign Performance

With clearer insights into the user journey, advertisers can deliver sequential messaging, ensuring each touchpoint moves the user closer to conversion.

3. Enhanced Attribution Modelling

Cross-device tracking enables multitouch attribution, providing a more accurate assessment of how various channels and devices contribute to a conversion.

Stat Insight: Studies show that 68% of digital conversions involve interactions across three or more devices.

4. Ad Frequency Management

By managing impressions at the individual or household level, marketers can avoid overexposing users to the same ad, improving engagement and reducing waste.

Technical Components of Federated Cross-Device Targeting

Effective cross-device targeting through federated identity graphs relies on several core technical components:

1. Device Graph Architecture

A federated device graph is constructed from signals collected from various devices. Microsoft’s cross-device identity solution, for instance, combines over 60 cookieless data feeds and more than 25 identity solutions into a privacy-compliant device graph. These systems use:

• Private Google Kubernetes Engine (GKE) clusters
• Confidential computing nodes
• Differential privacy protocols

This architecture ensures that while user data is leveraged for targeting, it is never stored or processed in a way that compromises privacy.

2. Decentralised Data Processing

Decentralisation means the raw data never leaves the source system. Instead, insights and models are shared using federated learning protocols, which allow systems to learn collectively while respecting data sovereignty.

Example: Google’s Federated Learning of Cohorts (FLoC) and its more advanced Privacy Sandbox initiatives utilise this model to support advertising while protecting user data.

3. Matching Systems

Method	Data Sources	Estimated Accuracy
Deterministic	Email logins, app subscriptions	91%
Probabilistic	IP addresses, Wi-Fi networks, location data	73%
Hybrid	Microsoft Graph + Tapad ML models	82%

These systems are constantly updated in real-time to reflect changing user behaviour, device usage, and contextual signals.

Privacy and Compliance in a Federated World

In today’s regulatory environment, privacy is non-negotiable. Federated identity graphs are inherently designed to address the most stringent global privacy standards.

GDPR and Beyond

In the European Union, where GDPR dictates the collection and use of personal data, federated systems shine by:

• Using only first-party and pseudonymised data
• Employing data minimisation principles
• Ensuring user consent and transparency

Data Handling Practices

Federated identity systems prioritise privacy by design:

• Metadata Encryption: Only anonymised, aggregated insights are shared.
• Confidential Nodes: Isolated processing units ensure individual-level data is never exposed.
• Real-time Controls: Dynamic consent management systems allow users to opt in or out easily.

Case Study: In 17 EU countries, companies like Experian have implemented GDPR-compliant federated identity graphs to power programmatic advertising campaigns, achieving a 42% increase in click-through rates (CTR) while remaining fully compliant.

Marketing Applications and Impact

When deployed correctly, federated identity graphs can transform digital marketing strategies at scale. Brands that leverage this approach have achieved measurable success across various performance metrics, including click-through rates, reduced media waste, and improved ROI.

1. Campaign Optimisation Across Devices

Federated graphs enable sequential, context-aware messaging across a user’s journey. For example, a consumer may see an awareness-focused video ad on CTV, followed by a product detail carousel on their smartphone, and finally a purchase incentive on their desktop. The sequencing of these messages is adjusted in real-time based on device usage and interaction signals.

Benefits:

• Aligns messaging with real-time engagement across devices
• Supports full-funnel engagement, from awareness to conversion
• Maximises efficiency by delivering the right message on the right device at the right time

Stat Insight: Brands using federated identity graphs report 23–37% reductions in wasted ad spend due to household-level targeting, which avoids duplication across shared devices.

2. Enhanced Attribution Accuracy

Federated identity graphs empower multitouch attribution (MTA) by connecting interactions across devices. Unlike last-click models that credit only the final touchpoint, MTA provides a holistic view of how various interactions influence a user’s path to conversion.

Use Case:

A user watches a YouTube product review on their smart TV, checks the product website on their mobile device, and completes the purchase on their laptop. Federated graphs link these touchpoints and attribute value appropriately to each stage.

Benefits:

• Provides more accurate ROI calculations
• Identifies high-performing channels and devices
• Informs budget allocation based on actual performance

Research shows that MTA models powered by federated identity graphs deliver 2.4x higher ROI compared to traditional last-click models.

3. Advanced Frequency Management

One of the most overlooked yet valuable advantages of federated identity graphs is the ability to cap frequency at the person or household level. Traditional device-based limits often result in repetitive ads being shown to the same individual on different devices. Federated graphs solve this by recognising the user regardless of device.

Benefits:

• Enhances user experience by reducing ad fatigue
• Maintains brand favorability
• Improves campaign efficiency by reallocating impressions

Regional Deployment and Considerations

Implementation of federated identity graphs varies depending on regional laws, market maturity, and available data partnerships. Leading markets have adopted different strategies to comply with data privacy regulations and optimise reach.

United States

• Coverage: Nationwide
• Primary Data Providers: Microsoft Graph, Tapad, Experian
• Tech Stack: Uses hybrid identity resolution models combining deterministic and probabilistic data

In the U.S., a vast amount of first-party and authenticated data is accessible through consumer logins and partnerships. This allows for a more robust deterministic layer in identity resolution.

Example: Brands leveraging Experian’s federated graph in the U.S. report significant improvements in conversion attribution and campaign precision.

European Union

• Coverage: 17 countries, including Germany, France, and Spain
• Data Approach: Relies heavily on GDPR-compliant first-party data
• Privacy Practices: Enforces data minimisation and user consent

Due to stringent regulations, European deployments of federated identity graphs emphasise privacy-preserving models, including differential privacy, pseudonymisation, and real-time consent mechanisms.

Case Study: In the EU, privacy-compliant identity graph solutions enabled brands like MiQ to achieve 42% higher CTRs in programmatic ad campaigns by targeting households using secure, federated insights.

Integration with Machine Learning and AI

Federated identity graphs become exponentially more powerful when integrated with artificial intelligence (AI) and machine learning (ML) systems. These technologies refine probabilistic modelling, allowing for smarter inference and personalisation at scale.

Applications of AI in Federated Identity

1. Behavioural Pattern Analysis

Machine learning algorithms identify patterns in time-of-day usage, app behaviours, and device switching to infer relationships with increasing accuracy.

2. Predictive Modelling

AI can predict future user behaviour, like likelihood to convert or churn, by analysing device usage history across linked profiles.

3. Real-time Decisioning

Dynamic optimisation engines can adjust bids, creative, and delivery strategies in real-time based on cross-device signals.

Microsoft Advertising utilises ML-driven identity resolution from partners like Tapad to enhance probabilistic modelling and boost targeting precision.

Cookieless Future: Federated Identity as the Solution

The deprecation of third-party cookies across major browsers has disrupted traditional user tracking methods. In response, federated identity graphs offer a future-proof alternative by eliminating reliance on centralised, cookie-based identifiers.

Why Federated Identity Graphs Lead the Future

Cookieless by Design

These systems leverage deterministic and device-based signals (like MAIDs and CTV IDs) instead of browser cookies.

Privacy-Centric Infrastructure

Differential privacy, encrypted metadata sharing, and decentralised architecture ensure compliance without sacrificing scale.

Universal Compatibility

Works across environments (web, app, OTT, IoT) and platforms (mobile OS, desktop browsers, CTV operating systems).

Stat: Federated identity graph-based campaigns outperform cookie-dependent strategies by up to 42% in CTR, according to programmatic media benchmarks.

Real-World Success Stories

1. MiQ’s Programmatic Campaigns (EU)

Challenge: Navigating GDPR while maintaining cross-device reach

Solution: Integrated Experian’s federated identity graph into demand-side platforms

Results:

• 42% increase in CTR
• 28% reduction in cost-per-acquisition (CPA)
• Improved conversion tracking across 3+ devices

2. Retail Brand with Microsoft Graph (US)

Challenge: Fragmented device journeys across a household of 4+ users

Solution: Utilised deterministic and probabilistic data from Microsoft’s federated graph

Results:

• 37% reduction in wasted ad impressions
• Enhanced multitouch attribution showing contributions of CTV to final purchase
• 2.1x lift in conversion rate compared to non-graph targeting

Challenges and Considerations

Despite its advantages, federated identity graph technology comes with its own set of challenges:

1. Data Quality and Fragmentation

The accuracy of the graph is dependent on the quality and freshness of the input data. Regular validation is essential.

2. Complex Implementation

Integrating a federated identity solution requires significant coordination between data partners, compliance officers, and tech teams.

3. Regulatory Risk

With laws like GDPR and CCPA evolving, federated systems must continually adapt their privacy frameworks.

Future Outlook: What’s Next?

The adoption of federated identity graphs is poised to accelerate. As privacy regulations evolve and third-party tracking becomes obsolete, more advertisers will pivot toward these decentralised, intelligent, privacy-compliant systems.

Key Trends to Watch

• Growth of Identity-as-a-Service (IDaaS): More providers offering turnkey identity graph integrations

• AI-powered personalisation: Improved real-time targeting and user journey prediction

• Expansion to new verticals: Beyond advertising, industries like fintech, telehealth, and e-learning will explore federated identity for secure, personalised experiences

Final Thoughts

Cross-device targeting powered by federated identity graphs is not just a technological innovation—it is a strategic imperative in the privacy-first, multi-device era. It allows advertisers to deliver seamless, personalised, and effective campaigns across a fragmented device landscape while honouring the growing demand for data privacy and compliance.

By integrating deterministic and probabilistic methods within a decentralised framework, federated identity graphs solve one of the most pressing challenges in digital marketing today: identifying and engaging users meaningfully without compromising their trust.

For forward-thinking marketers, federated identity graphs provide a clear path to precision, performance, and privacy —all at once.