It is undeniable that consumers are constantly being tracked online: their interests, locations, spending habits, wishlists and so much more is channeled into some mega database by the omniscient, all-knowing internet. Or at least, that is what it feels like. But, what does this mean for consumer privacy? And, what is being done to protect personal data?
After a number of high-profile data breaches, data privacy and protection is a consumer-first movement: Consumers are skeptical when it comes to our ability to protect their data, and want more control over how it's collected and used. GumGum's latest webinar, What Data Privacy Really Means for Digital Advertising dives deep into the current data privacy crisis and the promising technologies that will shape the future of data privacy in digital advertising.
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Identifiers are on their way out
While government-led efforts to shore up data privacy like GDPR, HIPAA, GLBA, or CCPA get a lot of press, it is actually browsers that are moving quickly to eliminate identifiers. Cookies, a data collection tool the industry used widely but without the consent or understanding of consumers, have been under fire for a decade. Recently, major browsers including Safari and Firefox have put limits on cookies, with Google phasing out cookies altogether by 2022.
Of course, cookies aren't the only identifiers at stake. Apple's IDFA now requires apps to ask permission to track their location and other data. Just how many consumers will consent is unclear. But, what is clear, is that, with growing consumer mistrust, digital markets must find new and more transparent ways to collect personally identifiable information (PII).
Data privacy crisis spurs innovation
As the data privacy movement pushes forward, the industry is working on some promising technologies that may result in even more sophisticated capabilities than we have now:
This technology, which GumGum has been advancing for more than a decade, uses machine learning to understand the digital environment that a user is engaging with. The idea is that if a page contains keywords like “cars,” “driving,” and “luxury,” that might be a good place for a Lexus ad, because the person reading it may be in the market for a new car. Traditionally, this has been done using keywords, but GumGum's advanced contextual analysis tool, VerityTM uses deep learning to better understand unstructured data like text, images and video, making it an extremely sophisticated and cost-effective alternative to behavioral technology.
First party data
Essentially, publishers and platforms collect names, email addresses and other information as part of a registration process that verifies user identity. These offerings are more expensive but offer advertisers access to targetable, verified users, and campaigns they can measure and track.
Shared IDs have been promoted for some time as a way to reduce the audience loss attributed to cookie synching as well as technical complexity and page load times. If the industry could rally around a common framework for sign on, publishers, agencies and brands could use that cross-device, deterministic and persistent ID to serve and measure ad campaigns.
Google's attempt at a cookieless infrastructure rests on the success of its Sandbox initiative. It's proposed technology would ask Chrome users to fill out a one-time CAPTCHA then issue a “trust token” to ensure that every user is a real person. That identifier would then work with a set of APIs that essentially do the work of cookies with one big difference — all that information is pulled into the Chrome browser where the data is protected from other parties. The catch, of course, is that it puts a lot of control in the hands of Google only. And, it doesn't exist yet. It's just a grand plan.
To get a more in depth understanding of the upcoming data privacy protocols, tune into our webinar recording here.