Identity & Data Glossary of Terms

Deterministic identity graphs use known customer information such as email addresses or log-in data to match and recognize individuals on whatever device they may be using. Probabilistic algorithms make informed guesses with a probability of being correct. They do their work in the face of uncertainty and may generate more elaborate identifier graphs, but the connections may be incorrect. 

The main difference between deterministic and probabilistic identity graphs is that deterministic approaches have a higher threshold for inclusion than probabilistic algorithms. Deterministic approaches build up identifier graphs but they prioritize accuracy and recency of identity over scale. In environments where there may not be enough data to build a fully deterministic graph, probabilistic models based on deterministic links still outperform graphs that are not built on a deterministic, PII-based reference base².

However, it is useful to note that many universal ID companies describe their solution as deterministic, but in reality, still have probabilistic elements. 

Is one approach better?

1. A probabilistic identifier assigns unique identifiers using a probabilistic approach, which means there is a chance of collision or duplication, albeit a very low probability. Probabilistic identifiers often use randomization or a combination of attributes to generate the identifier, which makes it highly unlikely for two objects to have the same identifier, but not completely impossible. UUIDs (Universally Unique Identifiers) are an example of probabilistic identifiers.
2. The main difference between the two lies in their guarantee of uniqueness. Deterministic identifiers provide a strong guarantee of uniqueness as long as the rules or algorithm is followed correctly. They are predictable and reliable. In contrast, probabilistic identifiers provide a high probability of uniqueness, but there is a minute chance of collision. The probability of collision can be calculated based on the identifier's length and the number of objects being identified.
   
   

In summary, deterministic identifiers guarantee uniqueness based on predefined rules or algorithms, while probabilistic identifiers offer a high probability of uniqueness but with a small chance of collision. The choice between the two depends on the specific requirements of the application and the acceptable level of risk for identifier collisions.

"Offline data" refers to information or data that is collected from sources outside of the online realm or digital environment. In the context of online targeting for advertising, offline data typically refers to data that is gathered from offline sources such as physical stores, customer surveys, loyalty programs, or other non-digital interactions.

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

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

PII, or Personally Identifiable Information, refers to data that can be used to identify a specific individual..Personally Identifiable Information (PII) is data that can be used to identify a specific individual. It can be direct or indirect, public or sensitive, and may include data such as name, address, phone number, email, social security number, passport information, or IP address. PII is often a target for identity theft and requires secure protection. 

PII is used in both legitimate and illegitimate ways. A user's browsing history, cookies served by websites, and search history are often used to serve targeted advertisements². Sensitive personally identifiable information can include your full name, Social Security Number, driver’s license, financial information, and medical records. 

PII is crucial in various sectors, including marketing and advertising, for several reasons:

• Targeted Advertising: With PII, marketers can target advertisements to individuals based on their specific attributes, interests, or behaviors. For instance, if a retailer knows that a particular customer regularly buys dog food (obtained via purchase data, which is linked to the customer's PII), they can send targeted ads or coupons for dog-related products.
• Personalized Experience: PII allows companies to provide a more personalized experience to their customers. For example, by using a customer's name (a form of PII) in emails or showing products related to their past purchases, companies can enhance the customer's engagement with their brand.
• Customer Analytics and Insights: Businesses use PII to understand their customers better. For instance, marketers might analyze demographic information (such as age, gender, and location - all of which are PII) to identify trends and patterns, guiding future marketing strategies.
• Measurement and Attribution: Companies use PII to track the success of their marketing campaigns, tying specific responses back to specific customers. This can help determine the return on investment (ROI) of different marketing activities.

While the use of PII in advertising has significant advantages, it also comes with substantial responsibility and legal obligations. Misuse of PII can lead to violations of privacy laws, such as the General Data Protection Regulation (GDPR) in the European Union or the California Consumer Privacy Act (CCPA) in the United States. Companies must ensure that they have the necessary permissions to collect, store, and use PII and that they do so in a secure manner to prevent data breaches.

Universal Identifiers come in many flavors, but one key differentiating factor is whether the UID is consistent across websites (unsalted) or unique to the user on a specific website.

• Examples of Salted ID’s: RampID, ID5
• Examples of UnSalted ID’s: Panorama ID, Xandr ID. 

A first-party cookie is a small text file that is created and stored by a website or online publisher on a user's device when the user visits their website. It is a type of HTTP cookie that is associated with the domain of the website the user is visiting. First-party cookies are typically used to enhance the user experience and provide personalized services.

Here are some key points about first-party cookies for online publishers:

1. Purpose: First-party cookies are primarily used to remember information about the user and their interactions with the website. They enable the website to recognize the user across different pages and visits, providing a more seamless and personalized experience.
2. Information stored: First-party cookies can store various types of data, including login credentials, language preferences, shopping cart items, user settings, and other information relevant to the website's functionality or customization.
3.  Duration: First-party cookies can have different expiration periods, depending on the website's configuration. Some cookies may expire when the user closes their browser (session cookies), while others can be set to last for a specified time (persistent cookies) or until manually cleared by the user.
4. Data ownership: First-party cookies are created and controlled by the website or online publisher that the user is directly interacting with. The data stored in these cookies belong to the publisher, and they have control over how it is used and accessed.
5. Privacy considerations: While first-party cookies are generally considered less intrusive than third-party cookies, they can still raise privacy concerns. Websites must inform users about the use of cookies through their privacy policy and provide options for users to manage or disable cookies if desired.
6. Regulatory compliance: Online publishers need to comply with applicable data protection and privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union or the California Consumer Privacy Act (CCPA) in the United States when collecting and processing data through first-party cookies.

Overall, first-party cookies play an essential role in delivering personalized experiences and maintaining user preferences on websites. They allow online publishers to offer tailored content, remember user settings, and provide efficient functionality, all while adhering to privacy regulations and user consent.

Third-party data refers to information collected by entities other than the online publishers themselves. In the context of online publishers, third-party data typically refers to data collected by external sources, such as data brokers, advertising networks, or other platforms, which is then shared with the online publishers for various purposes.

This data is often obtained through various means, including tracking user activities across different websites and platforms using cookies or similar tracking technologies. Third-party data can include demographic information, browsing behavior, purchase history, social media activity, and other relevant data points that provide insights into users' interests, preferences, and online behavior.

Online publishers can leverage third-party data to enhance their understanding of their audience, target their advertising campaigns more effectively, personalize content recommendations, and optimize their overall user experience. By accessing third-party data, publishers can gain a broader view of user behavior and interests beyond the data they collect directly from their own website or app.

It is important to note that the collection and usage of third-party data are subject to privacy regulations and policies. With the increasing focus on user privacy and data protection, there have been significant changes in the industry, such as the introduction of regulations like the General Data Protection Regulation (GDPR) and the evolving practices around data privacy, which have impacted the availability and usage of third-party data by online publishers.

A third-party cookie refers to a small text file that is created by a website that is different from the one the user is currently visiting. It is primarily used for tracking and collecting user data across multiple websites for online advertising purposes. 

When a user visits a website, the website may include elements, such as advertisements or social media buttons, from other domains. These third-party domains can set cookies on the user's browser to track their browsing activities across various websites. The information collected by these cookies can include user preferences, browsing history, demographics, and other behavioral data.

Third-party cookies are commonly used by advertising networks, analytics providers, and marketing companies to deliver personalized ads, measure ad performance, and target specific audiences. By tracking user behavior across multiple sites, advertisers can create profiles or segments of users with similar interests and display relevant ads based on their browsing habits.

However, due to privacy concerns and user consent, the use of third-party cookies has faced increasing scrutiny. Web browsers and regulatory bodies have implemented measures to limit or block third-party cookies, promoting more privacy-centric alternatives like first-party cookies, contextual targeting, and other technologies to deliver personalized content without relying on extensive user tracking.

Contextual targeting for online advertising refers to a method used by advertisers to deliver targeted advertisements based on the context or content of a web page. Instead of relying on user-specific data, such as demographics or browsing history, contextual targeting focuses on analyzing the keywords, themes, and overall context of the page being viewed by a user in real-time.

Here's how contextual targeting typically works:

1. Content analysis: Advanced algorithms and natural language processing (NLP) techniques analyze the text, images, and other relevant elements on a webpage to determine its topic, tone, and overall context.
2. Keyword matching: The algorithm identifies keywords and phrases that are relevant to the advertising campaign or the target audience.
3.  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 and likely to be of interest to the users viewing that page.

Contextual targeting offers several advantages for online advertisers:

1. Relevance: By serving ads that align with the content of a web page, contextual targeting increases the chances of reaching users who are already engaged with or interested in a particular topic or theme.
2. Brand safety: Advertisers can choose to avoid specific types of content or websites that may not align with their brand image, ensuring their ads are placed in suitable and safe environments.
3. Privacy-friendly: Unlike other targeting methods that rely on individual user data, contextual targeting focuses on the content itself, thereby reducing concerns related to privacy and data protection.
4.  Real-time adaptability: As contextual targeting operates in real-time, advertisements can be dynamically matched to changing content, allowing for more up-to-date and relevant ad placements.

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

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: These segments are 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 the return on investment (ROI) of their advertising efforts. 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, which is an industry organization that develops standards and guidelines for online advertising. The IAB Taxonomy refers to a hierarchical classification system developed by the Interactive Advertising Bureau 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 in organizing and structuring digital advertising inventory and also facilitates targeting and delivery of 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, which can improve 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.

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

Think of it like a virtual auction where multiple advertisers can bid for the chance to show their ads on a website or app. This creates healthy competition and ensures that publishers can make more money 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 the creation of great content or the improvement of 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 a technology called header bidding, which allows advertisers to compete for ad space, resulting in better revenue for publishers.

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

Think of it like a virtual auction where multiple advertisers can bid for the chance to show their ads on a website or app. This creates healthy competition and ensures that publishers can make more money 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 the creation of great content or the improvement of 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 a technology called header bidding, which allows advertisers to compete for ad space, resulting in better revenue for publishers.

An RTD prebid module is a software component 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 used by publishers 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. By 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 a software component 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 with the purpose of identifying and linking 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 third-party cookie and a hashed email.

A third-party cookie is a small text file that is stored on a user's device by a website they visit, but it is managed by a different domain 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, meaning that it 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, along with any other relevant user data or preferences, is stored in a secure database or data management platform (DMP).
3. Linking: As the user interacts with different platforms or websites that utilize third-party cookies, the cookie data is collected and associated with the corresponding hashed email in the database. This linkage allows for the aggregation of user behavior and interests across multiple touchpoints.
4. Analysis and Targeting: The linked data can be used for analysis, segmentation, and targeting purposes in advertising and marketing campaigns. By understanding the user's behavior and interests, advertisers can deliver more relevant and personalized content or advertisements.

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 the process of 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.

The goal of audience curation is to ensure that the advertising message reaches the most relevant and receptive audience, maximizing the effectiveness of the campaign. 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, ensuring that the right message is delivered to the right people at the right time, leading to improved campaign performance and better overall results.