This post is the second in our two-part series about making the case for first-party data. Check out Part 1 here.
Why are marketers clamoring to find clean, reliable first-party data for their ad targeting? There are three primary reasons, and they are game changers:
When publishers collect information on their own customers, the sky is the limit. Every click, every movement, every purchase can be tracked. Online and offline behaviors are recorded, analyzed, and categorized, woven together to tell a comprehensive story about each person who interacts with that company’s sites and apps.
When publishers require users to log in with a single sign-on across their devices, the customer’s data story becomes even tighter. That person’s identity is linked to each device they are logged in on, which creates a unified device profile – a cross-device targeting map for that user. This type of first-party information – called deterministic data – is necessary to accurately identify and target the same person across their smartphone, tablet, and desktop, thereby greatly increasing the chances for a conversion.
Facebook is a master at utilizing deterministic data for ad targeting, and they are empowering advertisers with that data like never before. Aside from the connected ad experience on their platform, Facebook partners with publishers to target and retarget consumers across various properties, on multiple screens. It’s the Holy Grail of ad targeting, providing tangible connected value to the advertiser and increased monetization for the publisher. Expect to see more of this from Twitter’s continued integration with MoPub, advertising consortiums like Pangaea, and large programmatic platforms like AppNexus.
In addition to its cross-device targeting value, deterministic tracking is opt-in, so it’s reliable and privacy-safe. The same can’t be said for all third-party providers: some third-party data companies build profiles around users without their consent, like DSPs that listen and collect information without actually bidding, or user acquisition companies that grab other developers’ data to build models for their own use. The proliferation of data companies means that it’s getting harder to regulate questionable data collection methods, and the industry has yet to really crack down on these types of operations. Now more than ever, CMOs need to be ever-diligent about screening potential partners to ensure that they are collecting data in an ethical, opt-in environment.
On the other side of the cross-device matching story is probabilistic data. This type of data uses predictive algorithms to determine the likelihood that a specific device ID is tied to a user, typically resulting in match rates hovering around 40%-70% accurate at best. Claims of higher probabilistic matching accuracy are oftentimes based on data sets pulled from a small sample of information – or on probabilistic data layered with deterministic data – making the probabilistic results look much better than they are. In either case, those inflated accuracy rates are highly unlikely to hold up in a large-scale environment, so it’s best practice for advertisers to make sure that data partners are separating their probabilistic and deterministic models when testing accuracy.
Many people argue that first-party data lacks scalability. No matter how big their databases, companies have finite consumer data and therefore finite audience prospects, right?
Not quite. A company’s data can be scaled, and it doesn’t even have to be that big to get started. When an advertiser starts with first-party data, they can be confident that those profiles are scrubbed and legitimate. They are segmented into the very best targeting prospects – the cream of the crop – and you know those customers are going to convert.
Enter lookalike modeling. Creating lookalike audiences based on high-quality, first-party data helps advertisers build incredible scale without affecting the integrity of their carefully curated insights. They can employ lookalikes from their own CRM databases or DMPs, or allow a publisher to create lookalikes on their behalf. (Caveat: Advertisers should approach this method carefully to make sure the publisher gives them the audience they are asking for and not leaving out valuable data attributes.) Growing an audience based on a successful seed audience is the best way to ensure that the scaled pool of prospects will perform well.
Similarly, third-party data finds its niche with audience layering. While employing solely third-party information in an ad campaign is less effective, large amounts of clean first-party data overlaid with specific third-party information can be a powerful combination. Layering on third-party insights can deepen data’s relevance and greatly increase the number of usable targeting prospects.
Pick Your Partners Wisely
A successful ad targeting strategy begins with deep, deterministic, first-party data, but tapping into this valuable resource isn’t as easy as it seems. Ideological challenges aside, today’s data environment requires that CMOs and marketers be diligent about asking data providers the right questions to ensure they are getting user information that’s accurate and ethical.
If advertisers take the time to evaluate their partnerships, force transparency into data discussions, and be more selective with the partners they choose, the results will be staggering. In a good way.
Contact us to learn more about how we use first-party data to make your ad targeting better.