Just when you thought you figured out ad tech terms such as programmatic and DSPs, the demise of the cookie and the creation of alternative IDs to help brands target consumers on the web are ushering in a slew of new acronyms. As marketers and their agencies look to navigate the post-cookie advertising landscape, they need to become familiar with terms like PETs, SDAs and CAPIs. Don't know what these are?
Here’s a look at some of the most used words that you might be too afraid to ask what they mean. And check back for updates to this list.
Conversions API (CAPI): Conversions APIs, or application programming interfaces, connect an advertiser’s data to a publisher, such as Facebook, so they can determine when ads lead to sales (online and off) and downloads. Conversions APIs are becoming ubiquitous within mobile marketing with Meta, Google, TikTok, Snap and Twitter all developing them. The conversion platforms replace the utility that cookies and IDs used to provide. Cookies and IDs were easy ways to detect if an ad hit the right target and to track consumers to measure when ads affected sales. Through conversions APIs, an advertiser shares event-level information—such as data collected from clicks on their websites, signups, and sales—directly with a platform's servers, in order to quantify the impact of the ads. CAPIs also help advertisers find audience segments to target ads.
Data clean room: There is some confusion in the ad tech industry about what constitutes a data clean room. That’s because there are different types of data clean rooms. At their core, though, data clean rooms are meant to make use of highly sensitive consumer data in a privacy-safe manner. The data can be encrypted or anonymized to hide personally identifiable information. The data also is permission-based, since the consumer theoretically consents, or does not consent, for certain parties to use their information. Data clean room services help brands manage what data they are allowed to use and for what purposes, and with whom they are allowed to share that data.
The IAB describes two varieties of data clean rooms, and one type is operated by walled gardens such as Google Ads Data Hub and Amazon Marketing Cloud. Brands match their data on consumers with data from the major platforms. The data matching helps measure how many people saw ads, what types of consumers saw the ads, and how much sales were generated from the ads. The clean room computing helps target ads, measure performance and plan future campaigns. There are some drawbacks to publisher-controlled clean rooms: The advertiser needs to trust Google, Amazon and others with their data. And the data is only useful in those environments, so whatever a brand learns only applies within those platforms.
Partner data clean room: The other type of data clean room is a partner data clean room, according to IAB. InfoSum would call this a “decentralized multi-party clean room.” In this setup, two parties, or more, store data in their own silos, but they can run computations without ever technically sharing data. InfoSum uses the example of a consumer product brand and a retailer using data clean room environments to study what customers buy and why. The CPG brand could use that data to target ads to audiences developed in the clean room. After a campaign, the CPG brand, retailer, and even another party, like a publisher, could collaborate in a partner data clean room environment to study the advertising results and measure sales and predict future consumer behavior.
Match rate: This concept is as old as the cookie in internet advertising. The match rate is how often a brand’s customer data, in the form of cookies or other identifiers, overlaps with the audience available for targeting through a demand-side ad platform. For instance, a brand comes with data on “customerXYZ123” and looks for that user on an ad network. With the death of cookie, matching is becoming ever more complex, and clean rooms are being used by brands to analyze where their customer base overlaps with the audiences of major publishers and platforms.
Privacy-enhancing technologies (PETs): This is a term first embraced by Meta, which owns Facebook, Instagram and WhatsApp, but it has since been picked up in IAB’s lexicon. Basically, PETs are new ways of playing with data that don’t tie it back to an individual consumer. Some basic characteristics of privacy-enhanced technologies include using aggregated sets of data, so it hides identities within cohorts (groups), and encrypting data that can’t be de-anonymized. For instance, Meta is working on what it calls “private lift measurement,” which uses encrypted data about a group of people who were exposed to an ad, and it matches that with an aggregated set of data about sales outcomes. The calculation gives marketers the average sales lift from an ad campaign without sharing data on individuals.