What is cross-device tracking?
The definition of cross-device tracking
Cross-device tracking refers to the set of technologies and methods used to track users across multiple devices such as mobile phones, desktop computers, laptops, tablets, and connected TVs (CTV).
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Cross-device tracking is a subset of user tracking. While user tracking measures behavior on a single device, cross-device tracking links the same user between devices. This makes it possible to understand complete (when consented) customer journeys, spanning platforms.
Why is cross-device tracking important?
People use many devices throughout the day, often switching between phones, desktops, tablets, CTVs, consoles, and more. For example, a customer might research a product on a desktop during work, see an ad for it later on their CTV, and then complete the purchase in a mobile app that evening. Without cross-device tracking, these actions would appear as separate users, leading to attribution errors such as double-counting and missing connections between devices.
Cross-device tracking links these interactions into a unified user profile, providing the visibility needed for cross-channel measurement. This enables accurate attribution, supports personalization by allowing marketers to target the same user with relevant messaging, and improves resource use by showing which touchpoints contribute to conversions. It also gives marketers a complete view of the customer journey, allowing them to optimize campaigns and make informed decisions about channel mix and investment.
Deterministic vs. probabilistic tracking
To connect users between devices, marketers typically rely on two main approaches: deterministic tracking and probabilistic tracking. Each has distinct advantages and trade-offs in accuracy and scale.
Deterministic tracking
Deterministic tracking uses explicit identifiers such as logins, email addresses, account IDs, or device advertising IDs (IDFA/GAID) to recognize the same user on different devices. For example, if someone logs into the same account on both a laptop and a mobile app, the platform can link these devices with strong confidence—provided the user has consented to sharing these identifiers Because the match is based on first-party data, deterministic tracking is highly accurate. Its limitation is that it only works when users sign in or provide a consistent identifier. Anonymous users, or those who never log in, cannot be tracked deterministically.
Probabilistic tracking
Probabilistic tracking uses device signals—such as IP address, operating system, browser version, location data, and usage patterns—combined with statistical models to infer whether multiple devices belong to the same user. It applies to both logged-in and anonymous users, so it captures more activity than deterministic methods, but with lower accuracy. Probabilistic systems may match unrelated devices (for example, those sharing the same Wi-Fi network) or fail to link devices when privacy settings or dynamic identifiers obscure signals. Because it relies on inference rather than definitive identifiers, accuracy varies by implementation and data quality.
What is cross-device attribution?
Cross-device attribution explains how credit for a conversion is assigned to multiple devices and touchpoints a user interacts with. While cross-device tracking collects activity data, attribution analyzes that data to determine which interactions influenced the outcome.
For example, a user may click a mobile ad, research the product later on a desktop, and complete the purchase in a mobile app. In this case, attribution recognizes the role of each interaction rather than giving all credit to the final action.
Here is how cross-device attribution works:
- Measurement: Interactions on various devices are recorded, such as ad clicks, website visits, app sessions, and purchases.
- Integration: Data from different devices is merged into a single profile that represents a single user journey.
- Modeling: Attribution models (e.g., first-touch, last-touch, linear, multi-touch attribution, or time-decay) distribute credit for the conversion to relevant devices and touchpoints.
- Optimization: Marketers analyze this cross-platform measurement to optimize budgets and strategies.
Cross-device attribution avoids the limitations of single-touch reporting and helps marketers identify which ads, devices, and channels contribute most to conversions. This enables more accurate campaign evaluation and better resource allocation.
Cross-device tracking challenges
While cross-device tracking has clear benefits, there are challenges that affect measurement quality and scope.
Privacy and regulations
Frameworks such as GDPR, CCPA, and Apple’s App Tracking Transparency (ATT) have restricted access to identifiers like cookies and IDFA, rightfully prioritizing user consent and decisions around how and when data is shared. The move toward a cookieless future further reduces available data for linking devices, increasing reliance on privacy-compliant, consent-based approaches and first-party data.
Consent fragmentation
Users grant or deny consent inconsistently across platforms. This inconsistency disrupts continuity in cross-device measurement and can create gaps in user journeys, even with tracking in place.
Data fragmentation
User activity is spread over different systems. Without a universal identifier, these datasets remain siloed, making it hard to build a unified profile.
Technical complexity
Connecting fragmented datasets can require significant engineering effort. Implementing SDKs, tags, and APIs in various environments and reconciling them on the backend can lead to integration issues, duplicate records, and inconsistent results.
Browser and platform-level blocking
Browsers and operating systems now enforce stricter cookie restrictions and app-level policies. These measures actively prevent some identifiers from being used, reducing the signals available for cross-device measurement and challenging probabilistic models.
Despite these challenges, solutions are emerging, including server-side tracking, probabilistic models adapted to reduced identifiers, and privacy frameworks that secure user consent. Addressing these issues requires both technical infrastructure and compliance strategies to maintain reliable cross-device measurement.
Cross-device measurement and Adjust
Adjust Measure provides attribution across devices, platforms, and channels, allowing marketers to analyze user journeys end-to-end. It supports mobile measurement for iOS and Android, web-to-web and web-to-app campaigns, CTV with CTV-to-mobile and CTV-to-CTV attribution, and PC and console on both single and cross-device bases. This coverage enables marketers to evaluate how touchpoints in different environments contribute to conversions.
Adjust also delivers holistic reporting by bringing campaign and revenue data together in one place. This helps teams identify which users and channels drive results and reduces data fragmentation. Our privacy-first design ensures that measurement remains accurate and compliant as frameworks continue to evolve.
For a practical example, see the Yelp case study. For additional guidance on attribution models, you can also explore our guide to multi-touch attribution.
Request a demo today to see how Adjust can help you with cross-device measurement and growing your app business.
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