Terms That DSPs Like to Use- What are They and Why Important?

If you're a marketing or senior executive, and you've demo'd ad platforms like I always do, you hear some terms that you think you know? Or maybe not? But here are some definitions and why media buyers like me think about them.

Identity Graph:

An Identity Graph, in the context of display advertising, is a data structure that maps various identifiers and data points related to individual users across multiple channels, devices, and platforms. The primary purpose of an Identity Graph is to create a unified and consistent view of consumers, enabling marketers to effectively target, personalize, and measure their advertising campaigns across various digital touchpoints.

Identity Graphs combine data from a variety of sources, including:

1. First-party data: Collected directly from users by the advertiser or publisher, such as email addresses, phone numbers, or website browsing behavior.
2. Second-party data: Data shared between partners, usually with some agreement in place.
3. Third-party data: Collected by data brokers or aggregators, which can include demographic information, interests, and behaviors.

By connecting these data points, an Identity Graph can help advertisers:

1. Achieve a more comprehensive understanding of their audience, based on a broader range of data points and touchpoints.
2. Deliver personalized and relevant content and ads to users, enhancing the overall user experience and ad effectiveness.
3. Improve cross-device targeting, which enables advertisers to reach users on multiple devices and platforms, increasing the likelihood of engagement.
4. Enhance attribution and measurement, allowing advertisers to better understand the performance of their campaigns and optimize their marketing strategies accordingly.

Media buyers lke myself can use Identity Graphs to optimize ad campaigns and drive better lead performance in several ways. By leveraging the unified view of consumers provided by the Identity Graph, media buyers can gain insights into user behaviors, preferences, and interests across multiple channels, devices, and platforms. This enables them to make more informed decisions when planning and executing advertising campaigns. Here are some ways media buyers can use Identity Graphs to optimize campaigns and improve lead performance:

1. Audience segmentation and targeting: Identity Graphs help media buyers identify and segment their target audience based on various attributes, such as demographics, interests, and behaviors. This allows them to deliver highly relevant ads to the right users, improving engagement and conversion rates.
2. Cross-device and cross-channel targeting: With a better understanding of users' online habits and device usage, media buyers can deliver consistent messaging and experiences across multiple touchpoints. This enhances the overall user experience and increases the likelihood of conversions.
3. Personalization: By leveraging the rich data provided by Identity Graphs, media buyers can create and serve personalized ads tailored to individual user preferences and behaviors. Personalized ads generally resonate better with users, leading to higher engagement and conversion rates.
4. Frequency capping and ad sequencing: Identity Graphs can help media buyers control the number of times users are exposed to their ads and ensure that they deliver the right sequence of messages. This prevents ad fatigue and ensures that users are not overwhelmed by repetitive or irrelevant content.
5. Improved attribution and measurement: By connecting user data across channels and devices, Identity Graphs enable media buyers to more accurately measure the effectiveness of their campaigns. This allows them to identify which channels, ad formats, and creatives are driving the best results and optimize their campaigns accordingly.
6. Lookalike modeling: Media buyers can use Identity Graphs to find and target users with similar attributes to their existing customers or high-performing leads. This helps them expand their reach and find new, high-quality prospects.
7. Retargeting and remarketing: Identity Graphs allow media buyers to re-engage users who have previously interacted with their ads, website, or app. By delivering tailored ads based on past behaviors, media buyers can increase the chances of converting these users into leads or customers.

Overall, using Identity Graphs enables media buyers to make data-driven decisions, personalize their advertising efforts, and improve the efficiency and effectiveness of their campaigns, ultimately driving better lead performance.

Here's two more terms that are used frequently, but to explain in detail:

Deterministic data:

Deterministic data refers to information collected through direct, one-to-one connections, typically based on personally identifiable information (PII) or unique identifiers, such as email addresses, usernames, or device IDs. Deterministic data offers a high level of accuracy and confidence in user identification and targeting.

In advertising, deterministic data is primarily used for cross-device and cross-channel tracking, targeting, and attribution. For example, if a user logs into the same app on multiple devices using the same email address, deterministic data can accurately link the user's activities across those devices. This enables advertisers to deliver a consistent and personalized experience and measure campaign performance more accurately.

However, deterministic data collection can be limited by privacy regulations, such as GDPR and CCPA, which require user consent for collecting and processing personal information.

Probabilistic data:

Probabilistic data, on the other hand, is derived from statistical algorithms and machine learning models that analyze and make predictions based on patterns and correlations in large, anonymized data sets. Probabilistic data does not rely on unique identifiers or PII, which makes it less precise in user identification and targeting compared to deterministic data.

In advertising, probabilistic data is often used for audience segmentation, targeting, and attribution when deterministic data is unavailable or insufficient. Probabilistic methods analyze data points, such as device type, browser, IP address, location, and browsing behavior, to make educated guesses about user identity and preferences.

While probabilistic data can help advertisers reach a larger audience and maintain user privacy, its accuracy is lower than deterministic data, and it may lead to less precise targeting and attribution.

In summary, deterministic data offers higher accuracy and confidence in user identification and targeting due to its reliance on unique identifiers and PII, while probabilistic data uses statistical methods and large data sets to make predictions and maintain user privacy. Advertisers often use a combination of both deterministic and probabilistic data to optimize their campaigns and achieve the best results while respecting user privacy and complying with relevant regulations.

Hope this helps! Next time a DSP salesperson demos a platform and mentions these terms, you got this...