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Understanding and connecting with your audience across multiple touch points is crucial for marketing success in today’s varied digital. This process, known as identity resolution, has evolved significantly over the years. Let’s explore the four stages of identity resolution, each building upon the last to provide marketers with increasingly powerful tools for audience targeting and personalization.
Identity resolution is the process of connecting multiple identifiers across devices and touch points to build a cohesive, unified view of individual customers. As consumers interact with brands through various channels – from websites and mobile apps to social media and in-store visits – the ability to tie these interactions together becomes essential for delivering seamless, personalized experiences.
The journey of identity resolution begins with cookie-based tracking, a fundamental yet limited approach that has been a staple of digital marketing for years.
When a user visits a website, a small text file called a cookie is stored on their device. This cookie contains a unique identifier, allowing the website to recognize the user on subsequent visits. Marketers can use this information to track user behavior, preferences, and basic demographic data within a single browser environment.
While cookie-based tracking laid the groundwork for digital marketing personalization, it has significant drawbacks. Cookies are browser-specific, meaning they can’t track users across different browsers or devices. They also have a limited lifespan and can be easily deleted by users, creating gaps in user profiles. Additionally, with increasing privacy regulations and browser restrictions on third-party cookies, this method is becoming less reliable for long-term identity resolution.
As cookie-based tracking evolved, marketers began adopting deterministic matching techniques for more accurate identity resolution. Deterministic matching relies on known, unique identifiers to link user interactions across devices and platforms. These identifiers typically include data like email addresses, phone numbers, account logins and social media profiles.
For example, when a user logs into a retail website on their laptop and later uses the same login on their smartphone app, deterministic matching can confidently link these interactions to the same individual.
Deterministic matching offers several benefits:
Higher accuracy: Since it’s based on authenticated user information, the match rate is typically very high.
Cross-device tracking: It can connect user behavior across multiple devices and platforms.
Persistence: Unlike cookies, deterministic identifiers don’t expire or get deleted easily.
However, deterministic matching is limited by scale, as it relies on users actively providing their information through logins or registrations.
To address the scale limitations of deterministic matching, marketers turned to probabilistic matching techniques.
Probabilistic matching uses statistical algorithms to infer connections between devices and users based on various signals, such as IP addresses, device types and characteristics, browsing patterns and behaviors, location data, and time of day usage. By analyzing these signals, probabilistic matching can create likely connections between different devices and interactions, even without explicit user authentication.
The primary advantage of probabilistic matching is its ability to dramatically increase the scale of identity resolution. It can make educated guesses about user identity across a much larger pool of interactions and devices. However, this comes at the cost of reduced accuracy compared to deterministic methods. Marketers must carefully balance the trade-off between reach and precision when employing probabilistic techniques.
The latest stage in identity resolution leverages artificial intelligence and machine learning to enhance both accuracy and scale.AI-powered identity resolution systems can process vast amounts of data from multiple sources, including first-party customer data, third-party data providers, real-time behavioral signals and contextual information.
Machine learning algorithms can identify complex patterns and relationships within this data, creating more nuanced and accurate user profiles.
One of the most significant advancements in this stage is the ability to perform identity resolution in real-time. As users interact with various touchpoints, AI systems can instantly update and refine their profiles, allowing for immediate personalization and targeting.
This real-time capability enables marketers to:
As we progress through these four stages of identity resolution, it’s clear that the future lies in increasingly sophisticated, AI-driven approaches. However, it’s important to note that each stage still has its place in the marketing toolkit. Many organizations will find themselves using a combination of these methods, depending on their specific needs, technical capabilities, and data availability.
Looking ahead, we can expect further advancements in identity resolution technology, with a growing emphasis on:
By understanding these four stages of identity resolution, marketers can better assess their current capabilities and chart a course for improvement. Whether you’re still relying on basic cookie tracking or pushing the boundaries of AI-driven solutions, there’s always room to enhance your approach to creating a unified view of your customers. When you’re ready to level-up your approach to identity resolution, reach out. Aqfer’s Marketing Data Platform is built to power future-ready identity resolution – this is just one of many powerful marketing data capabilities.