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Glossary of post-cookie data key words and phrases for marketers

An overview of post-cookie phrases ranging from Conversions APIs to retail media networks, as well as the Privacy Sandbox and other services.

Just when you thought you understood ad tech terms like programmatic and DSPs, the cookie’s demise and the development of alternative IDs to assist brands in targeting consumers on the web are ushering in a slew of new acronyms. As marketers and their agencies navigate the post-cookie advertising landscape, they must become acquainted with terms such as PETs, SDAs, and CAPIs. Do you have any idea what these are?

Here are some of the most commonly used words that you may be too afraid to ask what they mean.

Zero-party data: This term was coined by Forrester in 2018, and while it fills a niche in the data marketplace, not everyone is sold on the concept of zero-party data. Some advertisers may even argue that zero-party data is overhyped. In some ways, zero-party data is similar to first-party data in that it is collected directly from the customer by a brand or publisher. However, the interest in zero-party data and the number of brands adopting it reflects industry trends. Brands are looking for new data sources that come with consumer consent. There are services that use zero-party data to reward and even pay consumers for sharing information about their interests and purchases.

Conversions API (CAPI): Conversions APIs connect an advertiser’s data to a publisher, such as Facebook, so that they can determine when ads lead to sales (online and offline) and downloads. Meta, Google, TikTok, Snap, and Twitter are all developing conversion APIs, which are becoming increasingly common in mobile marketing. An advertiser uses conversions APIs to share event-level data—such as data collected from website clicks, signups, and sales—directly with a platform’s servers in order to quantify the impact of the ads. CAPIs also assist advertisers in identifying audience segments to target with advertisements.

Data clean room: The ad tech industry is divided on what constitutes a data clean room. This is due to the fact that there are various types of data clean rooms. At their core, data clean rooms are intended to use highly sensitive consumer data in a secure manner. Data clean room services assist brands in managing what data they are permitted to use and for what purposes, as well as with whom they are permitted to share that data.

According to the IAB, the other type of data clean room is a partner data clean room. According to InfoSum, this is a “decentralised multi-party clean room.” In this configuration, two or more parties store data in separate silos, but they can run computations without ever technically sharing data. InfoSum uses the example of a consumer product brand and a retailer studying what customers buy and why in data clean room environments. The CPG brand could use that data to target ads to clean room audiences. Following a campaign, the CPG brand, retailer, and possibly another party, such as a publisher, could work together in a partner data clean room environment to analyse the advertising results and measure sales and ROI.

Match rate: In internet advertising, this concept is as old as the cookie. The match rate is the frequency with which a brand’s customer data, in the form of cookies or other identifiers, overlaps with the audience that can be targeted via a demand-side ad platform. For example, a brand may provide data on “customerXYZ123” and search for that user on an ad network. With the demise of the cookie, matching has become increasingly complex, and brands are using clean rooms to analyse where their customer base overlaps with the audiences of major publishers and platforms.

Privacy-enhancing technologies (PETs): This term was coined by Meta, which owns Facebook, Instagram, and WhatsApp, but it has since been adopted by the IAB. PETs are new ways of interacting with data that aren’t tied to a specific consumer. For example, Meta is developing “private lift measurement,” which combines encrypted data about a group of people who were exposed to an ad with an aggregated set of data about sales outcomes. The calculation provides marketers with the average sales lift from an ad campaign without revealing any personal information.

Google’s post-cookie ad tech experiments are known as the Privacy Sandbox, and the company has stated that it will remove them from Chrome web browsers by the end of 2024. Google has begun testing a Topics API—application programming interface—which is one of the pillars of Privacy Sandbox to replace cookies in Chrome. Instead of using invasive cookies to track web users’ behaviour, advertisers can use the Topics API to gain very broad insights into them. Topics API collects only a few examples of consumer interests based on the types of sites that person visited in the previous three weeks. Advertisers can target advertisements based on contextual data points.

Retail Media Networks: These are ad networks based on first-party data collected by retailers on their customers. Examples include Walmart, Dick’s Sporting Goods, CVS, Best Buy, Lowe’s, and Michaels. Retailers see an opportunity to provide brands that sell goods in their stores with a way to use shopping data to improve the efficiency of online ad purchases. Best Buy, for example, recently partnered with Criteo, a programmatic ad platform, to assist brands in purchasing ads on third-party websites. On its demand-side platform, Walmart collaborates with The Trade Desk. The retail media boom is coinciding with the data clean room craze, because major retailers are looking for secure ways to share transaction data about shoppers with brands, such as clean rooms.

SKAdNetwork: Also known colloquially as SKAD, this is the measurement platform released by Apple in 2018 in advance of the App Tracking Transparency policies that went into effect last year. With iOS 14.5, Apple made it so that app developers could not access the Apple Identifier for Advertisers unless the consumer granted them permission. Developers such as Meta, TikTok, Snap, and Twitter could not easily track when an ad viewed on their apps resulted in a successful consumer event, such as a sale or download, without that ID. That’s where SKAdNetwork comes in: it’s a platform that shares less granular data with marketers while maintaining the user’s anonymity. SKAdNetwork has reached its fourth generation.

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