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GDPR -The benefits of using Digiseg audience data for marketing

Over the past decade, the digital ad-tech ecosystem has worked diligently to help marketers reach and engage their users as they went about their digital lives. The opportunity was huge — and so were the investments in the sector (in 2011, there were only 150 unique companies on the Lumascape, in 2018 there are 7,000!).
GDPR with start in the background

Published July 18, 2019

GDPR with start in the background

GDPR -The benefits of using Digiseg audience data for marketing

Over the past decade, the digital ad-tech ecosystem has worked diligently to help marketers reach and engage their users as they went about their digital lives. The opportunity was huge — and so were the investments in the sector (in 2011, there were only 150 unique companies on the Lumascape, in 2018 there are 7,000!).

But there was a problem

Nearly all targeting was based on cookies and personal data, and consumers began complaining. The EU listened to those complaints and responded by enacting GDPR. In the process, they upended the third-party behavioral data market. The industry has been scrambling for ways to allow marketers to continue using third-party data without running afoul of regulators.
For instance, they’ve created systems that hash personal data so that it is fully anonymized.But for many legal departments within brands, this approach isn’t enough. In fact, cookie use, in general, makes them uneasy. Lawyers for brands now pore over contracts, delaying the launch of campaigns and making the process far more onerous. The writing is on the wall: the digital advertising ecosystem needs to find an alternative to third-party data for marketing initiatives. This is where public data comes in.

What is Public Data?

Public data is gathered and maintained by National Statistics Offices. It is highly reliable and stable (read: marketers can trust it).
This data covers long-term characteristics of people: children in households, household income, homeownership vs. renters, number of cars in the household, size of the home. What’s interesting and useful about this data is the way it focuses on the overall needs of a consumer. For instance, a consumer who lives in a large stand-alone home has very different needs than one who rents a small apartment, as does one who has three school-aged children at home versus an empty nester.
It is also the most comprehensive data available, covering 80% of the population throughout Europe and the US, and performs equally well, equally well for desktop, in-app, mobile and video. Finally, and most importantly, this data is inherently GDPR compliant. Why? Because the Statistics Offices already aggregate data and place strict discretion criteria that must be met before it can be released to companies like Digiseg. Because this data never ever uses cookies or personal it is, and always will be, fully GDPR compliant.

Digiseg Audience Data is Built on Public Data

Long before GDPR was drafted, Digiseg set out to craft audiences based on public data for a very good reason: we wanted to offer marketers data options that allowed them to reach more of their target audiences based on stable and reliable criteria. We saw the limitations of third-party data and realized marketers needed more options. Third-party data — based on clicks, site visitors and interactions — only reaches 20% of the population, and it’s very unstable. If a person clicks on an auto ad, is it safe to assume he or she is still in the market for a new car three weeks (or even three days) later? Marketers never had real transparency into how and why consumers are classified, and that was a problem we wanted to address. Additionally, marketers can use our data in conjunction with their own first-party data to get a better understanding of who their audiences are, develop more relevant messaging and to target more accurately.


Difference between Digiseg- and third-party data.
Public Data (Digiseg Audience Data) Third-Party Data
No Cookies Cookie-based
Covers 100% of the population Covers less than 20% of the population
Segmentation based on stable characteristics: Number of children, household income, education levels, etc. Segmentation based on clicks, site visits, interactions
High level of stability Highly unstable
62.5% viewable 50% viewable
0.60% CTR .09% CTR

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