Meta, the owner of Facebook and Instagram, updated its ads platform, introducing more automation into campaigns, as the company continues to adjust to privacy and data restrictions in online marketing.
On Tuesday, Goksu Nebol-Perlman, Meta’s VP of product marketing, ads and business products, announced the new automation and machine learning tools within Facebook’s ads platform. A newly branded ad service called Meta Advantage consolidates the automated ad products, Nebol-Perlman said. Automation helps advertisers set app-install ad campaigns and create “lookalike” audiences, by relying more on Meta’s artificial intelligence. Lookalike audiences are people who resemble—based on their internet behavior and other factors—existing customers of brands, and “lookalikes” are ripe for targeting with ads.
Last year, Apple implemented rules that limit how much apps like Facebook and Instagram can track users on web browsers and mobile devices. The app-tracking changes made it more difficult to create “lookalike” audiences and to measure ads. It became more difficult to quantify the effectiveness of app-install ads, for example. In the past year, Meta has made a series of updates to its ad platform in response to Apple and notified marketers of nagging issues with some of its ad services. In September, Meta outlined how it was not accurately reporting “conversions,” or the rate at which ads lead to concrete results such as app downloads and sales. In February, Meta said it was making progress at tracking conversions.
“This move is in line with the industry’s desire for a more fully integrated tech stack that gathers all actionable data on one platform for more automated and efficient mobile growth," said Simon “Bobby” Dussart, CEO of Adjust, a mobile marketing analytics platform, in an email to Ad Age, referring to Meta’s new automation.
Meta has more than 10 million advertisers, and many of them are sophisticated online marketers that are used to running their own ad campaigns. As data becomes harder to collect and connect online, advertisers have to lean on machine learning models and aggregated data sources, which don’t identify individual users.