Identification strategies for advanced personalization: cookies, probabilistic methods, and Universal ID

Introduction

In the constantly evolving field of digital advertising, the use of identifiers, or “IDs,” represents a crucial facet of advertising personalization and targeting.

These identifiers take two main forms:

• on the one hand, they can be data that allowsidentify a user, like an email address.
• and on the other hand, they are used in targeting solutions specific.

This duality of approach offers advertisers a range of tools to interact with their audience in a more targeted and personalized way, while navigating a complex landscape of privacy and regulatory compliance.

In this context, we will explore various targeting methods using IDs, from the use of first-party cookies to improve the user experience on a specific website, to cross-domain first-party cookies that extend this personalization to multiple domains.
We'll also look at probabilistic methods that use behavioral data to create user profiles, as well as the concept of a universal ID, which aims to provide a consistent view of the user across channels and devices.

Each of these approaches has unique advantages and disadvantages in terms of targeting accuracy, controlling advertising pressure, and challenges in terms of user consent and platform interoperability. This exploration provides a comprehensive overview of current ID targeting strategies and their potential impact on user experience and online advertising.

How ID targeting works

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ID targeting is a sophisticated method of online advertising, where various types of identifiers are used to provide a personalized and targeted advertising experience. This process involves several techniques and technologies, each with its own specificities.

The use of identifiers, abbreviated under the term”ID“, refers to two distinct concepts:

• On the one hand, the category of data that makes it possible to identify a user, such as an email address,
• And on the other hand, the category of solutions exploiting this data for specific targeting.

First-party cookie:

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First-party cookies is data stored locally by the user's browser, issued by the website he visits. These cookies allow the site to recognize the user during subsequent visits, thus facilitating the personalization of the experience by remembering elements such as language preferences, display settings, or items added to the basket.

These first-party cookies are a common method of collecting pseudonymized information. These small text files are generated and stored by the user's browser when they visit a particular site and allow the publisher to keep personalization and session data. These cookies facilitate the recognition of each Internet user during future visits, thus promoting the personalization of content according to their preferences and browsing behaviors.

For example, let's say a user customizes dark theme settings on a news media site. Thanks to first-party cookies, the site will save these preferences. When the user returns to the site later, the dark theme will be automatically applied based on the data stored in the first-party cookies, providing a personalized and consistent experience.

First party cross domain cookies:

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First-party cookies in Cross domain are files generated by the website visited, but designed to work on different domain names belonging to the same publisher or the same company. They facilitate the continuity of the user experience from one domain to another, allowing the publisher to better understand the complete user journey through its various online properties.

The evolution of first-party cookies towards cross-domain use offers a continuity of user experience, even across different domain names, whether they are owned by the same publisher or not. This approach, such as that proposed by First in France, aims to extend the scope of the data collected, thus improving the understanding of user behavior throughout their online journey.

For example, a media group with multiple sites may use first-party cookies to track a user's behavior across their online properties. This makes it possible to better understand the interests of the user and to adjust advertising content accordingly, even if the user navigates from one site to another.

Probabilistic methods:

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Probabilistic methods rely on statistical models to infer information about a user, often from data that is not directly linked to their identity. Fingerprinting, for example, analyzes characteristics such as screen resolution, browser used, and other unique parameters to create a “fingerprint” of the user. By observing the behavioral patterns associated with this footprint, advertisers can anticipate user preferences.

For example, if a user frequently consults movie reviews, the ads could be focused on movie releases.

Universal ID:

THEID universal aims to create consistency in user identification across multiple platforms and channels. For example, an advertising identifier linked to the terminal (device ID) makes it possible to track a user across his various devices. An initiative like Trustpid, developed by telephone operators, uses shared, hashed and encrypted identifiers to generate unique identifiers. This offers a more holistic view of the user, allowing for finer personalization of content.

Certainly, in addition to Trustpid, there are several other key players and initiatives in the field of universal ID, each contributing to the creation of a more integrated and consistent advertising environment. Some notable examples include:

Trustpid:‍

• Trustpid is an initiative that focuses on creating universal identifiers for users, based on the collaboration of telephone operators. This solution uses shared, hashed, and encrypted identifiers to create unique user profiles. It allows transversal identification on different devices and platforms, while ensuring the protection of personal data. Trustpid thus offers advertisers a holistic view of the user, facilitating accurate and privacy-friendly targeting.

The Trade Desk with Unified ID 2.0:

• Unified ID 2.0, developed by The Trade Desk, is a collaborative initiative to create an open and interoperable universal identifier for Internet users. This system uses consensual login information to generate a unique identifier, offering a transparent and privacy-friendly alternative to third-party cookies.

LiveRamp with IdentityLink:‍

• LiveRamp offers IdentityLink, a platform that connects consumer data across devices and channels. This solution helps brands create a unified user profile, facilitating accurate and personalized ad targeting.

ID5:‍

• ID5 is designed to offer an alternative identification solution in an environment without third party cookies. This platform provides a unique identifier for each user, allowing for effective targeting and measurement in the programmatic advertising ecosystem.

Net ID:‍

• NetID, widely adopted in Germany, is a universal ID solution developed by the European NetID Foundation. It aims to provide consistent identification of users across different sites and online services, while complying with strict EU data protection standards.

Zeotap with ID+:

• Zeotap offers ID+, an initiative to create a universal identifier for advertising targeting. This solution focuses on consolidating first-party data to provide a unified view of consumers, while respecting their privacy.

These universal ID players and initiatives play a crucial role in the evolution of the advertising industry, offering solutions that meet the personalization needs of advertisers while respecting users' privacy concerns. Everyone brings their own technology and approach to meet the challenges of targeting in a post-cookie world.

➕ The advantages:

• ‍Targeting accuracy: ID-based solutions offer greater precision, allowing for finer personalization of content based on individual preferences.‍
• Control of advertising pressure: By having better control over the frequency and intensity of ads, publishers avoid over-solicitation, thus improving the user experience.

➖ Disadvantages: 

• ‍No interoperability: The fragmentation of ID solutions can hinder the exchange of information between platforms, limiting the overall vision of the user.‍
• Difficulty obtaining user consent: Gathering information through IDs can encounter challenges related to obtaining user consent, highlighting concerns about privacy and transparency in the use of data. These challenges are amplified in an environment where users are increasingly aware of the protection of their privacy.

Conclusion

The ID targeting represents a diverse and sophisticated approach to personalizing the user experience. From first-party cookies that ensure continuity on a site, to probabilistic methods that offer personalization based on trends, to the effectiveness of the universal ID in offering a consistent view across different channels, these strategies open up new perspectives for publishers.

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In addition to this article, find the alternatives to ID targeting:

🔗 Understanding contextual targeting

🔗 Understand how cohort targeting works

🔗 The prospects of third-party cookie targeting

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