First-party data-based technologies available for user grouping

“First-party data” is one of the most commonly used phrases regarding cookieless, and not without good reason. Users are generally well aware that, when they visit certain websites, their activity is tracked by the website owner, or its trusted partners, for the purpose of personalizing the shopping, browsing, and advertising experience on the website. Even if they’re not―a cookie consent window can easily clarify that for them. That’s why leveraging first-party data for personalized marketing is well-perceived. However, third-party cookies allow external parties to benefit from that information.

One of the key drivers of Google’s decision to depreciate third-party cookies was the problem of transparency around who collects the users’ data and for what purpose. There are a number of ways to collect first-party data, which can then be used for the privacy-preserving grouping of users. These tools won’t need any third-party cookies to satisfy their use-cases in the cookieless future.

Contextual analysis

The announcement of third-party cookie deprecation by Google immediately placed more emphasis on the roots of personalized digital advertising―a websites’ context. This method takes the tracking of user behavior out of the equation. It assumes simply that if a user visited a certain URL address, they are interested in the content that sits on that site. Contextual information might be provided by either the publisher owning the website (it can be delivered to the buyers by using Deal IDs or Seller-Defined Audiences’ signals) or third-party vendors, which leverage contextual engines to analyze websites.

A publisher or its trusted partner can observe and gather a set of information about the contexts of the websites a user visits. Such a collection of contextual signals might help build insightful user profiles, which could be leveraged to personalize ads displayed to a given user in the first-party context or contribute to more precise Protected Audience API segmentation.

Topics API

Google’s Privacy Sandbox contains an API, which maps and categorizes website domains into one of around 350 topics from its taxonomy (the amount of topics is expected to be increased in the future, according to recent statements by Chrome). Topics API assigns a user with five different topics based on their recent Chrome browsing history with randomized elements attached, with which they can be targeted.

Importantly, topics from Topics API can only be read by those callers that have already witnessed users from the same category elsewhere. Therefore, while publishers will only be able to benefit from the topics assigned to the visitors on their own inventory, third-party vendors (such as demand side platforms) are integrated with many different websites. This allows third-party vendors to witness topics across different contexts, thus enabling the possibility of receiving them later on. This leads to an advantage in the form of having extra information about users collected in a privacy-preserving manner. That’s why publishers can benefit from befriending an adtech partner by having its proprietary first-party user data enriched with Topics API information from outside its inventory.

User behavior in the publisher domain

Another extremely valuable input for privacy-friendly user grouping is the record of user behavior across a publisher’s inventory. Such data might include information about visitor time spent on the website, where they clicked, or to which parts of the website they devoted the most attention (and which parts were quickly scrolled through). Such a record of behaviors could be captured by either the publisher itself or its integrated technological partner. 

Capturing first-party data in this way, creating audiences and passing them with assistance of third-party cookies-agnostic tools (such as Publisher’s Partitioned First-Party IDs, Secure Signals, or Seller-Defined Audiences) has become one of the key publisher strategies for the cookieless future. However, one should keep in mind that if that approach is selected without third-party vendor partnership; it narrows down learning opportunities about the users to the publisher’s own inventory.

Additional data from publishers

Last but not least, there are other ways for publishers to attain more first-party data from their users that might be leveraged for user grouping. These might include:

• Information gathered through surveys or questionnaires. This type of voluntary data collection is sometimes referred to as “zero-party data” and is considered to be hard to get but very valuable.
• Personal data collected through physical newspaper sales. This derives from customer subscription plans (delivered via physical mail) or retail partner loyalty card records, and then connected with the user’s data required for registering and then logging in to the website.
• Other data, which might be present in a publisher’s CRM, and come from other sources.

All of the abovementioned ways can combine to form a powerful substitution of soon-to-be-gone third-party cookies, especially when they’re collected and put in use with the assistance of an experienced vendor.