Solutions abound for replacing third-party cookies' role in the advertising ecosystem - so many putative next-best-things that it might be easy to get lost in all the noise. As part of our Deep Dive on Data, Ryan Webb of Drum Network members Adapt Worldwide runs us through the pretenders to the cookie throne.
The phasing out of third-party cookies may have been pushed out to 2023, but it would be irresponsible for publishers and marketers to follow suit and put a pin in their future data plans.
Change is coming. It will arrive faster than you think. And it will be monumental.
What that change will look like is far from certain, but one thing is: the solution won’t be as ubiquitous as the third-party cookie. That means it’s crucial that marketers fully understand their options.
But just because advertisers can no longer track every user interaction online does not mean you can’t be smart and undertake brilliantly effective advertising campaigns. We can start preparing for the uncertain by acting upon the certain.
A significant part of Facebook’s ongoing battle with Apple over tracking restrictions is for advertisers to use their “server-to-server” conversion tracking solution, known as Conversions API (“CAPI”).
In a nutshell, by connecting its users with advertisers’ customers, Facebook has created a situation where there’s no need for a “browser- or device-based” tracking connector – a cookie. Instead, the source and destination servers can talk to each other directly.
Google now also enables this by using server-side tagging within Google Tag Manager. The principle is the same - no need for cookies within a browser, we can join a source and destination together in the cloud by matching up “logged-in” users.
The alternative to storing user data on your first-party server is to keep everything on-device. Questions about privacy largely arise when user data is shared across the web, so this is an equally valid solution as server-to-server.
Google are developing two solutions. First, FLoC. (Federated Learning of Cohorts). This approach “hides” individual users in a crowd (or cohort) of people with similar interests and uses on-device processing to keep individual browsing habits private.
Second, FLEDGE in which interest groups (possibly determined by FLoC) are stored in-browser and assigned to arbitrary, benign metadata. The metadata will then act as a signal to the ad platforms for bid management or targeting in real-time.
In both cases functionality such as targeting and bid management should be possible by using on-device, clustered signals to deliver real-time experiences for users.
Other user ID solutions
The advertising industry has been exploring alternative ways to track and connect users with their behavioral attributes for several years.
Exploring these solutions will continue, even though Google has said they will not be investing in them. Instead, Google has said that it will allow publishers to use their own solutions for targeting users where the publisher has a direct relationship (and they have said they won’t block other solutions).
Preparing for uncertain change
The exact nature of the incoming change may be uncertain, but we can use what we do know to make decisions and preparations that will be of certain benefit in the not-too-distant future.
First-party data is already essential to ad platforms. Think of all the logged-in users that Google, Facebook, Apple, and Amazon have. What’s about to change is how valuable first-party data is to advertisers.
First, piggybacking off platform first-party data is now essential (if it wasn’t already). That means taking advantage of Google’s “customer match” and Facebook’s “custom audiences”.
Second, now’s the time to start building your own banks of first-party data. As cookies phase out, the more first-party data you have, the more you can take advantage of not just these existing mechanisms, but the inevitable new first-party mechanisms that will follow.
Bridging the data gap
As cookies phase out, users will become even more protective of their data. But, by leveraging AI and machine learning we now have tools to use the data we do have to bridge that gap.
The latest version of Google Analytics (GA4) is a great example. Using already-existing data, Google is building ever-more-powerful modeling to ensure the reporting built on a smaller set of data is robust and accurate.
Dynamic creative will need to make better use of the other data signals available to them (location, context, weather, time of day).
In the past, we have relied too heavily on data tied specifically to individuals. Data that’s readily available to cohorts will still enable excellent dynamic creative execution.
Chatbots and conversational tools are sure to become incredibly useful. With these user-initiated tools, advertisers can take people on a journey by encouraging them to interact with the ad creative itself.