What Are Deterministic and Probabilistic IDs?

For many years, third-party cookies have been essential tools for tracking user behavior across multiple websites, facilitating effective advertisement delivery. 

However, due to rising privacy concerns and regulatory changes, the utilization of third-party cookies has been decreasing. Google initially announced plans to remove third-party cookies from Chrome by the end of 2024. However, by July 22, 2024, Google reversed this decision, opting instead to provide users with more control over their web browsing data. 

Despite this reversal, the traditional reliance on third-party cookies has become increasingly untenable for publishers, prompting a shift towards more sophisticated identification techniques, such as deterministic and probabilistic IDs.

Let’s understand these technologies better.

Deterministic Identification: Precision through Explicit Data

Deterministic identification relies on explicit user-provided information, such as email addresses or phone numbers, to create precise user profiles. 

This method offers high accuracy due to the direct linkage to individual users. For example, when a user logs into a website using their email, the platform can accurately track their interactions across sessions. 

The effectiveness of this approach is contingent upon users' willingness to provide personal information, which can limit its scalability.

These identifiers are typically derived from:

• User Logins and Registration Data: Sites and services can be certain that activities made with the same account and when users sign up on multiple devices are linked. For instance, the services such as Facebook, Google as well as Twitter fold ID inference to assure constant identification.
• Device-Specific Identifiers: Mobile platforms provide identifiers like Apple’s Identifier for Advertisers (IDFA) or Google’s Android ID to maintain continuity of users across multiple applications and devices.

Through the use of deterministic identifiers, publishers can effectively target everyone and provide unique content and advertisements. Nevertheless, the approach strains the requirement to obtain and store personally identifiable information (PII), which is a critical violation of privacy concerns. Compliance with data protection law, particularly the GDPR is necessary when dealing with such data.

Probabilistic Identifiers

Probabilistic identification aggregates various data points—such as device type, browser version, and IP address—to infer user identity. 

The approach reduces precision but enables tracking of users who refrain from providing explicit identifiers. 

The probabilistic ID approach involves formulating certain key values and then progressing with probability-based patterns and other device parameters.

Key factors include: 

• Browser and Device Information: Details like browser used, operating system, and device, all add up to forming a unique identity for the user.

• Network Data: Data like IP address, time zone, and similar allows to guess the user identity while they are using different gadgets and switching between different sessions.

Probabilistic methods are not as accurate as deterministic ones, the user tracking occurs without using PII. This method intertwines more naturally with privacy regulations since the method leans less on the user’s data. Nonetheless, probabilistic identification can be subject to inaccuracy and usually requires sophisticated algorithms and substantial data to achieve reliable results.

Technical Considerations for Web Publishers

The combination of deterministic and probabilistic identification methods provides a comprehensive approach to user tracking in a post-cookie world. 

Deterministic methods offer precision, ensuring that user data is accurate and reliable. Probabilistic methods, on the other hand, provide scale, allowing publishers to reach a wider audience by inferring identities based on behavioral patterns. 

This hybrid approach enables publishers to maintain effective targeting capabilities while adhering to evolving privacy standards.

Web publishers should consider:

• Data Integration: Data needs to be collected from websites and other traffic sources, mobile applications, and third-party platforms, etc. This could also be done by the use of good data management platforms (DMPs), ensuring data consistency and integrity.
• Algorithm Development: Developing and maintaining algorithms capable of accurately analyzing and matching data points for probabilistic identification requires significant technical expertise. Machine learning models are often employed to enhance matching accuracy over time.
• Privacy Compliance: Implementing mechanisms to anonymize data and obtain user consent is critical. Some measures include hashing PII, making it easier for individuals to have a clear option to opt in, or opt-out – these meet privacy requirements as well as gain customer confidence. 

The Practical Applications of Deterministic and Probabilistic IDs

Aiming to enhance user targeting and strategies? Then understanding the practical applications of deterministic and probabilistic identifiers is key!

Below are use cases illustrating how each method can be employed effectively:

1. Deterministic Identifiers

Use Case: Personalized Content Delivery for Logged-In Users

A premium news publisher requires users to create accounts and log in to access articles. By utilizing deterministic identifiers, such as hashed email addresses, the publisher can recognize users across different devices whenever they log in. 

This enables the delivery of personalized content recommendations and targeted advertisements based on the user's reading history and preferences. The high accuracy of deterministic IDs ensures that the personalization is relevant and enhances user engagement. 

2. Probabilistic Identifiers

Use Case: Broad Audience Targeting Without Login Requirements

A lifestyle blog does not require users to log in, resulting in limited access to deterministic data. To serve relevant advertisements, the publisher employs probabilistic identifiers by analyzing data points such as IP addresses, device types, and browsing behaviors. 

By aggregating this information, the publisher can infer user-profiles and deliver ads that align with the interests of anonymous visitors. While less precise than deterministic methods, this approach allows the publisher to reach a wider audience without mandating user authentication. 

3. Combined Approach

Use Case: Enhancing Reach and Accuracy

An e-commerce platform integrates both deterministic and probabilistic identification methods. For users who are logged in, deterministic IDs facilitate precise tracking and personalized marketing efforts. For visitors who browse without logging in, probabilistic identifiers help in approximating user profiles based on available data signals. 

This combined strategy enables the platform to maintain a balance between accuracy and reach, optimizing advertising campaigns across its entire user base. 

4. Cross-Device Attribution

Use Case: Tracking User Journeys Across Devices

A streaming service aims to understand how users interact with its platform on various devices. By employing deterministic IDs, the service can track a user's activity on a smart TV, smartphone, and laptop, provided the user is logged in on each device. 

This comprehensive view allows the service to attribute content preferences and viewing habits accurately, facilitating better content recommendations and targeted advertising. 

5. Privacy-Compliant Advertising

Use Case: Adhering to Data Protection Regulations

A publisher operating in regions with strict data protection laws, such as the GDPR in Europe or the US, must ensure user privacy while delivering targeted ads. 

By leveraging probabilistic IDs, the publisher can infer user interests without relying on PII. This approach helps in maintaining compliance with privacy regulations while still enabling effective audience segmentation for advertisers. 

By applying these identification methods thoughtfully, publishers can enhance user experience, improve effectiveness, and navigate the complexities of data privacy regulations.