Within the next 12-15 months, third-party cookies will retire across digital marketing channels.
Savvy advertisers know they need to begin developing a game plan for the cookieless future, but what will happen to those who don’t adapt to these changes?
Above all, marketers will suffer from signal loss, which will negatively impact how we measure campaign performance, optimize campaigns over time, create audiences for ad distribution and drive growth within our digital channels.
The industry sea change with the lion’s share of attention is the retirement of third-party cookies in Google Chrome.
Sure, other browsers, including Microsoft Edge, Apple Safari and Mozilla Firefox, have previously restricted third-party cookies. Chrome is more monumental simply because of its market share.
SimilarWeb recently released a study that showed Chrome was the world’s most popular browser with 62% of web traffic.
To recap from my previous article, Google Chrome will retire third-party tracking cookies around Q3 2023. That is an approximate timeframe for this monumental change, but it gives us a target to make sure that our digital marketing campaigns will be ready.
This might sound like the distant future, but many of the measurement solutions needed to replace the functionality of third-party cookies could require significant time and effort from development teams.
This type of support usually requires a few cycles to be prioritized on project roadmaps.
Getting started in the next couple of months will be beneficial in the long run.
Look at it this way: your future self will thank you for being thoughtful and proactive!
What happens when marketers do not build new measurement frameworks?
For over two decades, marketers have utilized third-party publisher cookies to track their media performance. This method isn’t perfect, but it’s been a standard practice that’s set to evolve in a major way during the next 12-15 months.
From a digital marketing perspective, one of the most significant impacts is the loss of conversion measurement. This loss of performance data includes sales, sign-ups, purchases, revenue and other engagement metrics since those actions are likely to be restricted.
If marketers do not evolve their measurement practices, their accounts will rely on algorithmically-driven modeled conversions.
Successfully enabling automation within PPC is critical to driving positive results.
One of the most potent algorithmic elements is smart bidding. Algorithms that drive cost-per-acquisition (CPA) and return-on-ad-spend (ROAS) bidding need strong data signals to optimize performance.
The data that feeds these algorithms must be reliable so that accounts are optimized toward the most valuable actions and this conversion data needs to have enough volume to drive machine learning.
Data loss means bid algorithms will not function properly, which will result in decreased PPC performance. Let’s try to avoid this!
More conversions will be algorithmically modeled as a result of signal loss
There is too much at stake (i.e., money) for ad platforms such as Google and Microsoft to leave marketers without another option to gain back lost data.
When marketers forge new measurement frameworks via Enhanced Conversions (EC), Google Analytics 4 or Offline Conversion Tracking, those are considered Observed Conversions.
This mix of first-party data and user-matched data (EC) is generated by registered actions taken by our website visitors.
Try to collect as much observed conversion data as possible.
The alternative is Modeled Conversions in Google and Smart Goals in Microsoft Ads. According to Google, Modeled Conversions is:
“When Google surfaces modeled conversions in Google Ads, we are predicting attributed conversions. In most cases, Google will receive ad interactions and online conversions but is missing the linkage between the two. The modeling we perform is modeling whether a Google ad interaction led to the online conversion, not whether a conversion happened or not.”
Even after these large-scale privacy shifts, Google will continue to acquire mountains of data per user: search history, browsing history, and any other online activity when someone is logged into their Google Account, especially when those signed-in users are on a Google property.
Google will not be able to install a tracking pixel for that user specifically, but they should have enough data to algorithmically predict which media interactions lead to a conversion for an advertiser.
Microsoft Ads is working on a version of conversion modeling. This product is called Smart Goals.
According to Microsoft:
“Smart Goals use Microsoft Advertising machine learning models to identify the best sessions on your website. If you have the UET tag set up correctly, the smart goal will examine all your website sessions and determine which of those sessions can be considered a ‘conversion.’ Smart goals use multiple signals to identify conversions. Some of the signals that are used include session duration, pages per session, location, device and browser.”
In essence, they are similar to Google’s modeled conversions. They both rely on machine learning at scale to understand user behavior and potential reactions to paid media exposure.
Marketers need to provide numerous additional signals to make any modeled conversions as accurate as possible.
With the loss of user-level data, modeled conversions will be part of the measurement landscape going into 2023.
This brings us back to creating a strong framework for supplying as much Observed Conversion data within the platforms, which will help inform the Modeled Conversion algorithms.
Read the rest of the article on Search Engine Land here.
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