To circumvent Facebook's strict housing ad targeting, a marketing agency used a two-step funnel. First, they ran a broad, non-real estate video ad to a specific neighborhood. Then, they retargeted people who watched the video with their actual housing ad, effectively creating a hyper-targeted audience despite platform restrictions.

The next evolution, the Generative Ads Recommendation Model (GEM), aims to fully automate ad creation. Marketers will simply provide an image and a budget, and the AI will generate the entire ad library. This shifts the marketer's primary value from ad creation to optimizing the post-click customer journey and offer.

General advice is easily dismissed. By providing hyper-specific guidance tailored to a customer's unique context, like gardening tips for their exact climate zone via geo-targeted ads, you demonstrate a deep understanding of their problem. This specificity builds immense trust and confidence.

Previously, marketers told Meta who to target. With the new AI algorithm, marketers provide diverse creative, and the AI uses that creative to find the right audience. Targeting control has shifted from human to machine, fundamentally changing how ads are built and optimized.

Large tech firms often struggle with global ABM because strategies are dictated by a central, US-centric corporate team. This leads to a disconnect with regional field marketing teams who understand local nuances, cultural differences, and specific account needs, crippling campaign effectiveness.

With Meta's Andromeda algorithm automating audience targeting, the primary reason for poor ad performance is no longer incorrect targeting settings. Wasted money is now almost exclusively a result of insufficient or non-diverse creative, making creative strategy the most critical component of a successful campaign.

Businesses building their entire model on leads from a single platform like Google or Facebook Ads are at severe risk. An algorithm change can instantly destroy their customer source, highlighting the need for a diversified, systems-based marketing approach rather than tactical dependency.

A new Marketing API feature allows Meta's AI to allocate up to 5% of ad spend to placements explicitly excluded by an advertiser. This signifies a major shift towards autonomous campaigns, reflecting Meta's confidence that its system can identify performance opportunities even in channels that human advertisers have ruled out.

Meta's ad recommendations excel because Apple's privacy changes created a do-or-die situation. This necessity forced them to pioneer GPU-based AI for ad targeting, a move competitors without the same pressure failed to make, despite having similar data and talent.