• Advanced Contextual

Dynamic vs. Static Data - Two Critical Building Blocks


Contextual Provides the Critical Component of Intent in the Post-Cookie World. Does your brand or publication understand the differences between static data and dynamic data? As we built the Advanced Contextual platform, we realized these two classes of user data are extremely relevant. Learn about the importance of combining both data types to better retain and/or acquire your ideal customers and subscribers here.

Our mission at Advanced Contextual is simple. We believe individual privacy is important and doesn’t have to compromise good business. We provide publishers and brands with tools to better understand their audiences through content consumption and dynamic intent data. We do this in a way that preserves both scale and precision, not tied to any single identifier.

As we built the Advanced Contextual platform, we had to develop a thought process around a series of things. One foundational aspect of our platform caused us to realize there are two classes of user data relevant to publishers and brands; static and dynamic. Static and dynamic here are used literally and one isn’t better than the other.


Static data doesn’t change very often. We see examples of this like physical address, size of household, DMA where household resides, and other individual or household identifiers which are stable and dependable. This data is used to establish identity in the post-cookie/CCPA world. Identity has to be driven by static and dependable data as we move to a deterministic (individual or cohort) world as the cookie goes away.

Dynamic data illuminates a verified identity based on current activities and interests. This data can take many forms as long as it’s permissioned and managed properly. Current location is dynamic, time of day is dynamic, and content consumption patterns are dynamic.

The owners of static data which resolve to an individual or household need additional dynamic data to understand what that person or household’s current interests are. Having an understanding of current interests allows publishers and brands to tailor their customer experience to better engage their users, track them and then attribute activity that contributed to a conversion. The traditional way of inferring that via web browsing behavior is going away and quickly.

How, then, can publishers and brands continue to obtain and leverage dynamic data?

First order point is they already have some dynamic data about their static and stable identities. A brand knows what is purchased and when by someone. A publisher with subscribers knows what they read on that publication. It’s valuable. But it’s also a narrow view of people.

Here’s an example.

An insurance company knows what products a person buys from them over time and they know what’s being insured. What they might want to know is which of their insurance customers are reading articles about ways to save money on insurance. If they knew which subset of customers were researching insurance, they might try to better retain those who are looking to switch providers before they cancel. In this instance the brand would leverage their knowledge of customers (static 1P data), then inform it with a dynamic layer of insight around which of their customers are reading or watching content about insurance. Static data identifies the person and dynamic data gives a signal on the brand should act.

This, we think, is the point. Platforms that provide dynamic data to brands and publishers must be able to easily customize the signal for the customer, deliver it with multiple levels of contextual specificity (e.g. topic, keyword, entity), and do so in a way that preserves both scale and precision. Combining this with verifiable, static data that identifies a person enables publishers and brands to better retain or acquire customers and subscribers. It’s a foundational aspect of our platform and approach.


Contact us below to learn more.



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