Identity & Data Glossary of Terms

Deterministic Identity:

Deterministic identity graphs use explicit, verified data points such as email addresses, login information, or phone numbers to match and recognize individuals across devices and platforms accurately. These data points provide a high degree of certainty in user identification. Deterministic methods prioritize accuracy and recency, making them highly reliable but often limited in scale due to the requirement for direct user-provided information.

Probabilistic Identity:



Probabilistic identity graphs rely on algorithms to make educated guesses about user identities based on patterns and behaviors. These algorithms analyze data points like device types, IP addresses, and browsing habits to infer connections between devices and users. While probabilistic methods can create extensive identity graphs and scale efficiently, they risk inaccuracies as they deal with probabilities rather than certainties.

Key Differences:

Accuracy vs. Scale:

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• Deterministic methods are highly accurate because they use direct, verifiable data but may cover fewer users.
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• Probabilistic methods can cover a broader audience by inferring connections, though they may sometimes need to be more accurate.

Data Requirements:

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• Deterministic identity requires explicit user data like email addresses or log in details.
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• Probabilistic identity leverages indirect data like device characteristics and browsing patterns.

Use Cases:

• Deterministic identity is preferred when high accuracy is crucial, such as in personalized marketing and secure user authentication.
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• Probabilistic identity is useful for broad audience targeting and when deterministic data is unavailable.

Integration of Both Methods:

Many identity solutions integrate deterministic and probabilistic elements to balance accuracy and scale. For instance, a deterministic core might be expanded with probabilistic links to enhance the reach while maintaining a base level of accuracy.

Conclusion:

The choice between deterministic and probabilistic identity methods depends on the application's specific needs, the importance of accuracy versus scale, and the available data. Both approaches have advantages and can be effectively combined to optimize user identification and targeting strategies.

"Offline data" refers to information collected from sources outside the online or digital environment. In the context of online advertising targeting, offline data typically includes data gathered from physical stores, customer surveys, loyalty programs, and other nondigital interactions.



This offline data can encompass various types of information, such as demographic details, purchase history, geographic location, preferences, and other relevant customer attributes. Marketers and advertisers utilize offline data to enhance their understanding of consumer behavior, create more accurate customer profiles, and improve the effectiveness of targeted advertising campaigns.

Advertisers can develop a more comprehensive view of their target audience by combining offline data with online data—such as website browsing behavior, online purchases, or social media interactions. This integrated approach allows them to deliver more relevant and personalized advertisements to consumers based on their offline activities, interests, and preferences.

Personally Identifiable Information (PII) refers to any data that can be used to identify a specific individual. This information can include a variety of details that, either alone or in combination with other data, can uniquely distinguish one person from another. Common examples of PII include:

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• Full name
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• Social Security number
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• Email address
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• Home address
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• Phone number
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• Date of birth
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• Passport number
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• Driver's license number
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• Credit card information
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• Biometric data (such as fingerprints or facial recognition data)

In digital advertising and data management, PII is highly sensitive and subject to stringent regulations and privacy standards, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations protect individuals' privacy rights and ensure their data is handled responsibly.

When handling PII, companies must implement robust security measures to protect this information from unauthorized access, breaches, and misuse. Additionally, they must obtain explicit consent from individuals before collecting, storing, or using their PII for any purpose, including advertising and marketing activities.

Protecting PII is crucial for maintaining consumer trust and compliance with legal requirements. Failure to adequately protect PII can lead to significant legal penalties, reputational damage, and loss of customer trust.

A first-party cookie is a small text file that a website or online publisher creates and stores on a user's device when they visit their website. It's an HTTP cookie associated with the domain of the website being visited. Here are some key points about first-party cookies:

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• Purpose: It is primarily used to remember user information and interactions with the website, enhancing the user experience and providing personalized services.
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• Information Stored: This can store various data types, including login credentials, language preferences, shopping cart items, user settings, and other relevant data for website functionality or customization.
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• Duration: This may have different expiration periods, such as expiring when the browser is closed (session cookies) or lasting for a specified time (persistent cookies) until manually cleared by the user.
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• Data Ownership: This is created and controlled by the website or online publisher, giving them ownership and control over the stored data and its usage.
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• Privacy Considerations: While less intrusive than third-party cookies, privacy concerns remain. Websites must inform users about cookie usage through privacy policies and provide options to manage or disable cookies.
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• Regulatory Compliance: Compliance with data protection regulations like GDPR and CCPA is essential when collecting and processing data through first-party cookies.

First-party cookies are crucial in delivering personalized experiences, maintaining user preferences, and adhering to privacy regulations.

A third-party cookie is a small text file created by a website other than the one the user is visiting. It's primarily used to track and collect user data across multiple websites for online advertising purposes. Here are some key points about third-party cookies:

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• Purpose: Used to track user behavior across websites for advertising, targeting, and performance measurement.
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• Information Collected: Includes user preferences, browsing history, demographics, and behavioral data.
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• Usage: It is commonly used by advertising networks, analytics providers, and marketing companies to create personalized ads and target audiences.
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• Privacy Concerns: Third-party cookies face scrutiny due to privacy concerns and user consent issues, leading to measures to limit or block them.
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• Alternatives: Promotes privacy-centric alternatives like first-party cookies, contextual targeting, and other technologies for personalized content delivery without extensive user tracking.

Third-party data refers to information collected by entities other than online publishers themselves. Here are some key points about third-party data:

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• Source: Collected by external sources like data brokers, advertising networks, or platforms, then shared with publishers for various purposes.
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• Data Types: Includes demographic information, browsing behavior, purchase history, social media activity, and other relevant data points for audience insights and targeting.
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• Usage: Helps publishers understand their audience, target ads effectively, personalize content, and optimize user experiences.
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• Regulation: Subject to privacy regulations like GDPR, impacting the availability and usage of third-party data.
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Third-party data provides publishers with valuable insights but requires compliance with privacy regulations and evolving industry practices.

Contextual advertising in online marketing is a strategy advertisers use to deliver targeted ads based on the context or content of a webpage. Instead of relying on specific user data like demographics or browsing history, contextual targeting focuses on analyzing the keywords, themes, and overall context of the page viewed by a user in real time.



Here's an overview of how contextual targeting typically operates:

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• Content Analysis: Sophisticated algorithms and natural language processing (NLP) techniques examine the text, images, and other relevant elements on a webpage to understand its topic, tone, and context.
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• Keyword Matching: The algorithm identifies keywords and phrases pertinent to the advertising campaign or the target audience.
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• Ad Placement: Based on the content analysis and keyword matching, the system selects and displays advertisements that are most relevant to the page's context, aiming to capture the interest of users visiting that page.

Contextual targeting offers several advantages for online advertisers:

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• Relevance: By presenting ads aligned with the content of a webpage, contextual targeting increases the likelihood of reaching users already engaged with or interested in a particular topic or theme.
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• Brand Safety: Advertisers can avoid specific content types or websites that do not align with their brand image, ensuring their ads are placed in suitable and secure environments.
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• Privacy-Friendly: Unlike other targeting methods, which rely on individual user data, contextual targeting focuses on the content itself, minimizing concerns related to privacy and data protection.
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• Real-Time Adaptability: Because contextual targeting operates in real time, ads can be dynamically matched to evolving content, enabling more timely and relevant ad placements.

Overall, contextual targeting empowers advertisers to deliver more precise and relevant ads to users based on the context of the web pages they visit, thereby enhancing the effectiveness of online advertising campaigns.

Audience Segment: In online advertising, an "audience segment" refers to a specific group of individuals who share similar characteristics or interests and are targeted for advertising campaigns. These segments are created based on various data points, such as demographics, behavior, interests, location, or any other relevant attributes.

By dividing the larger audience into smaller segments, advertisers can tailor their marketing messages and deliver more personalized and relevant ads to specific groups. Audience segments enable advertisers to focus their efforts on reaching the most relevant users, increasing the effectiveness and efficiency of their advertising campaigns.

Here are a few examples of audience segments commonly used in online advertising:

1. Demographic segments: These segments categorize users based on demographic information such as age, gender, income, education level, or marital status.
2. Behavioral Segments: Based on user behavior and actions, such as websites visited, search queries, content consumed, purchase history, or engagement with specific ads or campaigns.
3. Interest segments: These segments group users based on their interests and preferences, which can be determined from their online activities, social media engagement, or interactions with specific content categories.
4. Geographic segments: These segments target users based on their location, which can be as broad as a country or as specific as a neighborhood or zip code.

Advertisers use audience segments to optimize their ad targeting strategies, allocate their budgets effectively, and maximize their advertising efforts' return on investment (ROI). By reaching the right audience with the right message at the right time, advertisers can increase the likelihood of engagement, conversions, and, ultimately, business success.

IAB stands for Interactive Advertising Bureau, an industry organization developing standards and guidelines for online advertising. The Interactive Advertising Bureau's IAB Taxonomy is a hierarchical classification system developed to categorize and organize digital advertising content.



The IAB Taxonomy provides a standardized framework for classifying and organizing digital advertising content, making it easier for advertisers, publishers, and ad tech platforms to understand and work with different types of online advertising. It helps organize and structure digital advertising inventory and also facilitates targeting and delivering ads to specific audiences.

The taxonomy includes a hierarchical structure with multiple levels of categories and subcategories. It covers various aspects of digital advertising, including ad formats, industry sectors, content types, and targeting methods. Each category within the taxonomy represents a specific aspect or attribute of digital advertising.

By using the IAB Taxonomy, advertisers and publishers can ensure that their ads are properly classified and aligned with relevant content. This can improve the targeting, relevance, and effectiveness of advertising campaigns. Additionally, it enables better data analysis and reporting, as the taxonomy provides a consistent and standardized way of categorizing digital advertising content across different platforms and systems.

A prebid wrapper, also known as a header bidding wrapper or wrapper tag, is code implemented on a website to facilitate the execution of header bidding.

Header bidding is an advertising technology that allows multiple ad exchanges to compete for ad impressions simultaneously before the webpage loads. Traditionally, publishers used a waterfall model, where ad networks bid one after another, resulting in suboptimal revenue and slower page load times. With header bidding, publishers can receive bids from multiple demand partners simultaneously and choose the highest bid in real-time.

A prebid wrapper is a container for all the header bidding codes from various demand partners or ad exchanges. It is placed in the header section of a webpage and makes requests to different ad exchanges, allowing them to participate in the bidding process. The wrapper collects bids from these exchanges and passes them to the publisher's ad server, selecting the winning bid to serve the ad.

The advantage of using a prebid wrapper is that it simplifies the implementation and management of header bidding. Rather than individually integrating the code of each demand partner, the publisher only needs to implement a single wrapper tag. The wrapper communicates with the exchanges, collects bids, and passes them to the ad server. Additionally, wrappers often provide features like bid caching, timeout management, and analytics, further enhancing the efficiency and effectiveness of header bidding.



Overall, a prebid wrapper streamlines the header bidding process, improves competition among demand partners, and helps publishers maximize their advertising revenue.

Prebid.org is a project that supports digital advertising by offering free tools and resources to publishers and advertisers. It focuses on header bidding, a technology that allows websites and apps to get the best value for their ad space.

Think of it as a virtual auction where multiple advertisers bid to show their ads on a website or app. This creates healthy competition and ensures publishers can profit more from their advertising inventory.

Prebid.org provides software solutions that publishers can use to implement header bidding on their websites or apps. These tools help publishers take control of their advertising and maximize their revenue potential.

By using Prebid.org's tools, publishers can increase their chances of getting higher bids for their ad space. This means more revenue for them, which can support creating great content or improving their services.

The project also fosters collaboration and knowledge sharing among industry participants, promoting fairness and transparency in the digital advertising ecosystem. It helps advertisers and publishers work together more effectively to create a better online advertising experience for users.



In summary, Prebid.org is a project that offers free tools and resources to help websites and apps earn more money from their advertising. It does this by implementing header bidding, which allows advertisers to compete for ad space, resulting in better revenue for publishers.

An RTD prebid module is software used in programmatic advertising to improve the bidding process. It works within real-time bidding systems, where advertisers and publishers participate in auctions to buy and sell ad space.



The module focuses on using real-time data to make bidding decisions. It is part of a prebid framework publishers use to manage their advertising inventory.

The main job of an RTD prebid module is to collect and analyze real-time data about the ad inventory being auctioned. This data includes user behavior, demographics, device information, and other relevant details. Using this data, advertisers can make smarter bidding choices, leading to better targeting and campaign performance.

The RTD prebid module connects with data providers, demand side platforms, and ad exchanges to exchange data and bidding information. It accesses real-time data streams, applies algorithms and rules, and generates optimized bid responses based on available inventory and advertiser preferences.

In summary, an RTD prebid module is software that uses real-time data to improve the efficiency and effectiveness of bidding in programmatic advertising. It helps advertisers make informed decisions and achieve better results in their campaigns.

Linkage data refers to the process of connecting or associating different pieces of information from various sources, typically to identify and link user profiles or data points across multiple platforms or systems. In the context of digital marketing and advertising, linkage data often involves the linking of a thirdparty cookie and a hashed email.

A third-party cookie is a small text file stored on a user's device by a website they visit, but a different domain manages it than the one being visited. Third-party cookies are commonly used for tracking and targeting purposes, allowing advertisers and marketers to collect information about a user's browsing behavior and interests across multiple websites

On the other hand, a hashed email refers to an email address that has been converted into a unique string of characters using a cryptographic hashing algorithm. Hashing is a one-way process that cannot be reversed to obtain the original email address. Hashed emails are often used as a privacy measure to pseudonymize or anonymize personal information.



When linking a third-party cookie and a hashed email, the process typically involves the following steps:

1. Collection: A user's email address is collected through a specific interaction or opt-in process on a website or application. The email address is then hashed using a hashing algorithm, generating a unique string of characters.
2. Storage: The hashed email and any other relevant user data or preferences are stored in a secure database or data management platform (DMP).
3. Linking: As the user interacts with different platforms or websites that utilize thirdparty cookies, the cookie data is collected and associated with the corresponding hashed email in the database. This linkage allows for aggregating user behavior and interests across multiple touch points.
4. Analysis and Targeting: The linked data can be used for analysis, segmentation, and targeting purposes in advertising and marketing campaigns. Advertisers can deliver more relevant and personalized content or advertisements by understanding the user's behavior and interests.
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It's important to note that with increasing privacy concerns and regulatory changes, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), the use of third-party cookies and hashed emails for linking user data is subject to compliance with applicable laws and regulations, as well as obtaining proper user consent and providing transparency regarding data usage and privacy practices.

Audience curation refers to identifying, segmenting, and selecting a specific target audience for a digital advertising campaign. It involves analyzing various data points, such as demographics, interests, behaviors, and past interactions, to create a highly defined audience profile.

Audience curation aims to ensure that the advertising message reaches the most relevant and receptive audience, maximizing the campaign's effectiveness. By curating the audience, advertisers can deliver personalized and tailored content that resonates with the specific characteristics and preferences of the target audience, increasing the likelihood of engagement, conversions, and, ultimately, a higher return on investment (ROI).

Audience curation often involves using various tools and technologies, such as data management platforms (DMPs), customer relationship management (CRM) systems, and third-party data providers. These resources help advertisers collect, analyze, and leverage data to create audience segments or personas that align with their campaign objectives and messaging strategies.

Overall, audience curation plays a crucial role in optimizing digital advertising efforts. It ensures that the right message is delivered to the right people at the right time, leading to improved campaign performance and better overall results.