The Impact of Signal Deprecation on Attribution and Personalization

Signal loss is no longer something we can ignore. Over the past few years, we’ve seen it emerge and usher in an era of new industry standards.

Deloitte Digital research revealed that companies across a range of industries risk losing at least $91 million in revenues per year due to the deprecation of third-party ad IDs. And this is for each company!

With these effects swaying all parts of the ecosystem, it’s clear that privacy and tracking changes are formidable forces that marketers need to reckon with. But how do these changes show up in brand marketers’ day-to-day work?

How Signal Loss Has Affected Attribution and Personalization

As I teased in my previous post, the deprecation of third-party cookies and mobile ad IDs (MAIDS) has had — and will continue to have — a profound impact on two things marketers care about: (1) personalization and (2) measurement.

Below, I dig a bit deeper into how powerful  personalization and attribution tactics were before these sweeping changes , and how the ecosystem is coping now.

Personalization

How it worked before

If you recall my first blog, which mentioned how the third-party cookie provided the common infrastructure for the entire internet, the third-party cookie and other third-party signals provided immense value for personalizing advertising for consumers.

Connecting the entire ecosystem end-to-end, third-party signals helped marketers to better understand every touchpoint they had with their audiences. Because you knew which ads converted, you could better segment your audience, recognize patterns in the copy and creative customers resonated with, and design highly customized ads that pushed higher LTV prospects to convert. This optimization process worked so well that 75% of marketers still rely on third-party cookies worldwide.

2024 and beyond

With the privacy rules and browser changes of the past few years, marketers now struggle to connect their data - as well as to activate it - across all of the platforms they need to reach their audiences. Once Chrome strikes the final blow to the third-party cookie in 2024, unprepared marketers will need new ways to personalize and target their ads, as well as to avoid wasting their budgets on poorly performing ads.

To preserve their budgets and continue to personalize the ad experience, they need to start thinking about how else they can enable signal.  At LiveRamp, we see the best way to do this as authenticated IDs. We’ve developed an authenticated infrastructure that spans from 18,000 publisher domains, to walled gardens, mobile in-app, CTV and more. We enable the ecosystem to reach authenticated consumers and transact on them via LiveRamp’s RampID identifier. Other authenticated IDs, like UID 2.0, have emerged as well. We see these as the strongest solutions because they embrace the ecosystem’s - and consumers’ - pushes for privacy, and require that users affirmatively share their email or other identifier with companies.

Another strategy is to prioritize environments where you have strong signals. It’s not a coincidence that RampID works well in CTV, which is a largely logged-in environment where consumers provide their information as they sign up for services. It’s worth noting that every part of the ecosystem will have to overcome signal loss, and CTV is no different as companies still leveraging the IP address contend with losing this as an identifier. Just as with the rest of the ecosystem, authenticated identity offers a strong solution here, and marketers can leverage their first-party data to reach real consumers via CTV.

‍Second-party data is another option. Brands can develop deep partnerships with other companies that aren’t competitive but target a similar market, making use of their first-party data to get ahead, and can not only personalize experiences for consumers, but also find new, valuable audiences, using data collaboration to leverage their data in a privacy-conscious way.

An easy win here is to reevaluate your suppression strategy. It seems like a no-brainer — don’t market to the people who already bought a certain product, are looking for a job at your company, etc. Yet recent case studies by mobile advertisers have cited that up to 40% of ad budgets can go toward users who already have the same app installed on their devices. 

Funneling your ad dollars away from irrelevant audiences can save substantial budget. Across the board, 37% of marketers have reported a substantial increase in campaign performance when implementing suppression strategies. Tools like WasteNot make it an easy lift — that is, if you have accurate first-party data.

Attribution

How it worked before

Historically, marketers and social platforms used pixels to track conversions. If you worked at Nike, for example, you might work with Meta to show a particular audience segment an ad for Pegasus shoes. Whenever a user who saw the ad went to Nike.com and bought Pegasus shoes, Meta’s conversion pixel would track that, and Meta got credit for bringing in that purchase.

Of course, the model wasn't perfect — you could argue someone already had the Pegasus shoes in mind or they saw a different ad on TV or in a magazine. But overall, pixels worked fairly well. Back in 2018, the Meta ad pixel could ID users on every browser except Safari. Further, Meta was able to use mobile advertising IDs to power attribution in-app. A great example: you bought the Nike shoes via their app instead of via their website.

Having that identification was critical because it allowed you to optimize your ad budget based on performance and identify which users had already completed certain actions, like completing a purchase. When you knew how each ad was performing on each ad platform, you could pour more dollars into the good campaigns and remove dollars from the bad ones. What’s more, conversion pixels allowed you to identify which users had already completed purchases, so you didn’t spend money serving them more ads for items they already purchased and instead served them ads for the next best offer.

2024 and beyond

Continuing with conversion pixels as a microcosm of the market, today, a user might see that same Meta ad for Nike shoes, but if they visit the site using Safari, Microsoft Edge, or Firefox (and now 1% of Chrome), the pixel will fail, blinding Meta and the advertiser from proper attribution or identification of existing purchases. 

So, how can marketers get around that? The key is to approach conversions a little differently. 

When someone buys Nike shoes, they want a receipt and they need to input shipping information. That means almost every conversion has an email address associated with it.  Looking at other types of conversions, like newsletter sign-ups, app activations, and marketing asset downloads, other consumers may be providing emails, as well as other signals that help to identify them to marketers.

Once marketers have identifiers, they can start to leverage a range of new tools to better understand conversions. One tool - which all of the major social platforms have leaned in on - is conversion APIs. Conversion APIs rely on brands’ data and connect directly to the platforms measuring the conversions, thereby overcoming signal loss on the browser front. 

Social platforms like Meta have shown that implementing Conversion APIs can increase conversion rates by 23% or more - a meaningful gain.

As Signals Disappear, You Need a Way to Stitch Data and Experience Together

With the acceleration of signal loss, your first-party data becomes incredibly important. Your CRM is the only thing you have full access to and control over.

Only then can you connect your first-party data to new, innovative signals for intent. In my next article, I’ll share ways to build a better first-party data strategy, enrich your data, and use suppression to your advantage.