If there’s one thing growth teams are—it is resilient. Think about it. We navigated through a barrage of changes through the course of 2021, from Apple's IDFA changes, to Facebook’s deprecation of the AMM program, and so many more twists and turns along the way. It may have been a little chaotic each time, but the point is we collectively made it. Which is good because there is more to come!
That’s right—let us not forget that Google plans to stop supporting the third-party cookie in late 2023. This will directly affect how marketers can reach audiences on the open web, track the right kind of data, and gauge the success of growth campaigns. So now what? Increase spending to reach similar goals of previous years? Invest more on email marketing software?
Well, let’s just say there’s no need to panic, cause it’s not all that bad. And we all have some time to prep for what’s to come.
Here’s what you need to know before Google phases out its support of third party cookies.
Google isn’t banning all cookies
That’s right. First party cookies will still be in the picture. Also, Google won’t stop tracking people entirely.
You will still be able to see what a user did while visiting your website, see how often they visit it, and gain other basic analytics that can help you develop or automate an effective marketing strategy around them. However, you can't see data related to your visitor's behavior on other websites that aren't affiliated with your domain.
The fact that Google won’t stop tracking people entirely kind of opens the door to a few gray areas, as the company is investing in alternatives such as its own Privacy Sandbox. They have also seen successful advertising results from FloC, a technology that tracks groups of people rather than individuals.
"Our latest tests of FLoC show one way to effectively take third-party cookies out of the advertising equation and instead hide individuals within large crowds of people with common interests," Google's recent announcement explained.
You and your growth team might want to consider exploring how to run some tests on FloC for your brand.
Third party cookie alternative categories
There are a few categories of third-party alternatives that are worth being a part of your brand’s toolkit. Browser solutions, such as Privacy Sandbox and FLoC, which I previously mentioned, are worth considering. Granted, they are both still works in progress. Even so, they can have you covered in terms of audience segmentation, retargeting, measurement and incrementality. With such solutions, marketing teams would still be able to deliver relevant advertising without using individual identifiers.
You can also look into alternative ID’s, which are also known as universal ID’s. These can help you identify users on the open web in a way that easily complies with privacy regulations. In most cases, the identifiers themselves are based on deterministic data such as anonymized personally identifiable information that a user provides anyway.
If you aren’t already, you might also want to consider contextual advertising. These can come in handy for DTC’s as these ads provide value for users that are already in that mindset (like an ad for face cream when reading about combination skin). Or if you’re thinking about retargeting campaigns, there is also Google Turtledove and Google FLEDGE, which both prove to be helpful without third-party cookies.
Digiday states that although Google’s deadline extension gives advertisers a breather, it is still expected to continue testing cookie-free alternatives for tracking, targeting and measuring ads.
Now’s the time to review data strategies within your organization
It’s time to kick third-party data for good and focus on developing relationships with your customers. This is because marketers have been addicted to using third-party data for years. However, it wasn’t because third-party data was the most effective way to reach their target customers— it was because that data was just so easy for them to access and execute short-term campaigns. As such, marketers need to break their third-party data addictions and instead turn to review the data strategies within their organizations.
If you know me, you know where I’m going with this. I’m talking about putting the spotlight more on zero-party, and first-party data. The good stuff that’s exclusive to your brand.
As mentioned in a previous blog post, “zero-party data is data that is intentionally and proactively shared directly by the consumer, which you can use to determine what your customers intend to do or buy in the future.” This form of data can also help your team get a better grasp of the LTV of your different customers, which would ultimately help with acquiring additional customers that demonstrate similar LTV.
I’m a big believer in all zero and first-party data being good data—BUT the impact of these can only pan out if you have an amazing data team set in place. The most robust data structure will support advanced uses of data, such as data modeling and predictive modeling. In turn, that will make the data structure able to support the growth team in a manner that facilitates the transformation to a personalized approach. A good chunk of your brand’s data capabilities need to cater to your growth strategy, and as such, you would need to ensure all the boxes are ticked to enable data as a lever for growth.
Turn to a predictive UA solution to assist with LTV-based marketing
There’s no time like the present to explore the latest tools and tech that can assist with growth campaigns for the long-term.
A predictive UA solution will be able to step in and assist in multiple ways, down to the user-level. It will help growth teams with audience targeting, remarketing, and loyalty-focused marketing. We went over the latter in one of our previous posts, in which we discussed how loyalty focused marketing is far more strategic than hit and runs.
Of course, you don’t need me to explain the importance of the lifetime value of customers for DTC brands, and how it’s ideal to target those with high LTV. But what about when there are two different and completely unrelated customers that purchased a product—but how can ad networks determine which customer would have a better LTV over the other? This is where a predictive UA solution would come into play.
I know, I know. There are thousands of martech solutions out there. How can an already-busy team narrow down on what the best predictive solution would be, to meet their unique needs? Well, this will call for a couple of internal meetings, where you would need to ask yourselves a few questions. These questions include what your media spend is, and how much you expect it to grow. You would also need to ensure that the solution is made for your current business model and martech stack, as well as whether the solution is plug-and-play (to eliminate the frequent need for internal engineering resources.) Here’s a list of a few other questions we recommend you go over.
All in all, any kind of change in the growth landscape can be considered exciting in some aspects, but will always be somewhat of a nuisance for marketing teams as strategies become shuffled each time. The solution for this significant pain point would be an effortless future-proofed ability to continue reaching desired audiences, regardless of those pesky changes. Predictive UA offers just that—serving as the ultimate bridge between the current landscape, and the cookieless world.