How can you use device graphs to link users across different devices?

Imagine a silent concierge, following your digital trail – phone, laptop, tablet. This invisible map tracks your online habits, a web that whispers your desires. It learns, to send targeted ads based on fleeting thoughts. Advertisers crave this power – personalized messages, eerily relevant, plucked from your digital subconscious. But a shadow falls. Who controls this map of your online self? What's the future of device graphs? Can they revolutionize advertising without compromising privacy? A powerful tool, demanding a conversation about responsibility. We, the mapmakers of the digital world, will decide the future I believe.

We individuals often use multiple devices, such as smartphones, tablets, laptops, desktop computers, smart TVs, wearables, and other devices that are interconnected in terms of our usage patterns and our identity. Device graphs are data structures that map the relationships between various devices used by a single user, related user, or users in a household. Device graphs are powerful tool in the digital ecosystem that collects data like device IDs, browser cookies, app usage, IP addresses, and login information, enabling better user experience and more effective digital strategies.

Device graphs are key in linking users across devices. They use data like device IDs and online activities to map user profiles. By combining deterministic data (like login info) with probabilistic methods (like location tracking), they offer a nuanced view of user behavior. This understanding is crucial for targeted advertising, enhancing user experience and campaign effectiveness.

Device graphs, becoming increasingly important as cookie replacements, extend beyond basic identifiers by incorporating advanced data signals like app usage patterns and cross-device interactions to build comprehensive user profiles. They can be deterministic, using verified login data for precise user-device links, or probabilistic, employing sophisticated algorithms to infer connections based on behavior patterns. Leveraging machine learning, device graphs refine user mappings and improve accuracy. Integrating offline data, such as in-store purchases, can enrich device graphs, enabling a more holistic view of consumer behavior for targeted, multi-channel marketing strategies.

Device graphs are an essential mechanism in ad tech that seamlessly links users across various devices. Advertisers can effectively target their audiences across different platforms by harnessing identifiers such as cookies, mobile IDs, and IP addresses. This targeted approach ensures a consistent and impactful advertising experience regardless of the device. Long story short, Creating a fingerprint using a device graph as a variable will help advertisers target and track unique users across different platforms.

Device graphs, crucial in the post-cookie era, come in two types: deterministic and probabilistic. Deterministic graphs use concrete data, excelling in accuracy through user actions like multi-device logins, essential for precise marketing and security. However, they may miss part of a user's device ecosystem if not all devices are used consistently. Probabilistic graphs, on the other hand, infer connections using big data and machine learning, offering a broader view but with some uncertainty. This approach helps identify emerging trends but risks inaccuracies. Businesses must choose based on their need for precision or extensive coverage, considering their data analysis requirements.

Device graphs allow us to link users across different devices, creating a more holistic view of user behavior. This is achieved by mapping devices to a single user or household based on shared characteristics and behaviors. They are important because they provide a unified view of the customer journey, which is often fragmented across multiple devices. This enables more accurate targeting, personalized experiences, and comprehensive analytics. As for UCIM (Unified Customer Identity Management), device graphs are indeed a game-changer. They enhance UCIM’s ability to unify customer identities across devices and channels. This leads to a deeper understanding of customers, enabling more effective engagement strategies.

Device graphs are vital for several reasons: They enable seamless user experiences across devices, improve the effectiveness of advertising by ensuring coordinated messages across platforms, and allow for more accurate cross-device tracking and analytics. They are essential for detecting fraud by identifying abnormal device usage patterns. additionally, they provide deeper customer insights, enabling personalized content and services, and help businesses integrate data across platforms for a comprehensive user view. While device graphs raise privacy concerns, they also play a role in ensuring that user data is used appropriately across devices, complying with regulations such as GDPR or CCPA respecting user consent.

In my experience the importance of device graphs will only continue as offerings becoming more omnichannel and advertisers focus on finding the right person at the right time as opposed to focusing on the where. Having a holistic omnichannel view of attribution will become more of the expected norm in the industry and will drive even more value with the loss of 3rd party cookies.

Understanding your customer, and more specifically, understanding your customer journey will help you optimise your campaigns across all your marketing channels. And by 'optimise', I include human aspects such as showing consideration and respect. In a world where the AI bots are being deployed to increase the already mind numbing noise users experience, building a 'human', considerate brand will mean that you don't overwhelm your ideal prospects with a flood of the same message across channels because you cannot manage frequency capping or targeting across channels effectively. Well configured device graphs will empower this kind of effective campaign management and allow you to build a trustworthy brand positioning.

I possess direct experience collaborating with leading global brands in constructing device graphs. This involves aggregating first-party data such as cookies, mobile numbers, advertising IDs, hashed emails, and login information from diverse sources like web applications, mobile apps, public APIs like location, 2nd and 3rd party data providers, and partners. Subsequently, I employ a blend of deterministic and probabilistic approaches to connect user and device profiles, leveraging an array of signals and criteria for enhanced accuracy and efficiency.

Device graphs use data like IP addresses, device IDs, and behavioral patterns to stitch a user across devices, giving you line-of-sight into the single user view of their activities. By following these connections, you can now precisely attribute conversions back to where they really started—across devices. This provides more accurate cross-device attribution for increased accuracy when running your campaigns and ensuring all touchpoints are followed.

Segmenting Customers Using Device Graphs ? Collecting data on browsing history, app usage, TV viewing habits, and Identifying unique users through anonymous identifiers like cookies and IPs. ? Extracting behavioral and demographic features from data and Applying clustering algorithms to identify distinct customer segments. ? Developing detailed profiles for each segment. Segments and targetted strategies: ? Streaming Enthusiasts: Spend significant time streaming video content. Marketing Strategy: Video ads, and promotions during peak viewing times. ? Work-from-home professionals: High usage of desktops and laptops during work hours. Marketing Strategy: Work-related product promotions, LinkedIn ads

One thing I might find helpful in using device graphs to link users across different platforms and track conversions is to implement unified tracking and leverage device graph solutions. I agree with this approach because it provides a comprehensive view of user interactions and improves conversion attribution. For example, by integrating tracking technologies and using device graphs to connect data points across devices, I can analyze user journeys and optimize campaigns based on insights from their behavior. This method ensures accurate tracking of conversions and enhances the effectiveness of cross-channel marketing strategies.

Solid "blocking and tackling" of analytics data is the first step towards making useful device graphs. A lot of businesses haven't done a good job of setting up their tracking systems. The IT team that usually does the tracking implementation?are?seldom?given the business goals, so they do what they think is required, which if often a default configuration. When tracking systems are put in place, they rarely take into account the fact that different departments?have access to different sources of data. As the maxim goes - garbage in === garbage out. When you plan and carry out your tracking, make sure that it fits with your business management goals and includes all of your data sources.

When building a device graph, it's essential to establish a clear and consistent definition of what constitutes a data point. Ensuring that these data points are consistently defined and interpreted across all systems and platforms involved in your device graph is crucial for maintaining data integrity and accuracy. Inconsistent definitions can lead to data discrepancies, incorrect device matching, and ultimately, poor performance of your device graph and dependent campaigns.