Why I Recommend the Featured Ads

The long-awaited Featured Ads feature arrived in SocialPeta in November 2021. To understand why the feature mattered, it is useful to first look at one of the important data types used in ad intelligence at the time: original ads published as social-media feed posts.

A Statista report released on July 29, 2021 cited Facebook as having 10 million active advertisers using its social-networking platforms to promote products and services in the third quarter of 2020, compared with 7 million advertisers in the first quarter of that year. The article also noted that Facebook generated more than $84.2 billion in advertising revenue in 2020, compared with roughly $1.7 billion from other revenue.

At the time, those figures reflected how established and competitive Facebook feed advertising had become. Advertisers increasingly wanted to study competing ads together with useful performance signals so they could improve their own advertising strategies.

Engagement on an original post provides one such signal because likes, comments, shares, and related interaction values can continue changing while an advertisement remains active. SocialPeta's Featured Ads feature selected original-post ads showing strong growth, based on the increase in their combined engagement values, and surfaced those ads for further analysis.

According to SocialPeta's November 2021 update, its system had accumulated the capability to collect approximately 50,000–100,000 original-post ads per day. SocialPeta then tracked advertisements associated with the original posts it obtained and continued adjusting its tracking algorithms to improve the advertising data available to customers.

Facebook Ad Library provides an official public database for researching ads running across Meta's platforms. The distinction highlighted in SocialPeta's 2021 article was that the Ad Library view shown at the time focused primarily on the ad itself rather than exposing the engagement indicators SocialPeta associated with original posts.

As illustrated by the original screenshots, SocialPeta could display data such as likes, comments, and shares for an advertiser's original-post creative. That additional context was the basis for selecting Featured Ads: rather than reviewing every creative equally, advertisers could focus on ads whose engagement was showing stronger growth.

The original article described SocialPeta's original-post ad collection as a self-built crawling system that was continuously adjusted as advertising-platform strategies changed. SocialPeta positioned the scale of this original-post collection as an important advantage for ad research at the time.

When this article was published in November 2021, SocialPeta stated that its broader advertising data supported five channels: Facebook, YouTube, Instagram, Twitter, and TikTok.

The Featured Ads selection itself was limited to Facebook at that point. The original product update said work on the other channels was still in progress.

This section describes the product's historical November 2021 capabilities and should not be interpreted as a statement of current SocialPeta platform coverage.

The November 2021 SocialPeta update introduced Featured Ads alongside an upgraded creative-saving workflow. Users could create their own labels when saving an ad creative or select labels recommended by the system.

After saving creatives, users could review them from the Saved Ads area. The update also described support for viewing creatives saved by a team.

SocialPeta said it would provide dynamic information for saved creatives within the first 90 days of an advertisement. If the system detected an advertisement more than once, its status would be recorded as "discovered"; otherwise, it would be recorded as "not found."

The original article also stated that, for VIP customers who saved an original-post advertisement, SocialPeta would automatically track changes in that post's engagement value.

Together, Featured Ads and Saved Ads were designed to turn ad discovery into a more continuous research workflow: identify creatives gaining engagement, save useful references, organize them with labels, and continue observing their performance signals.