We implemented a Multi-Layered Audience Strategy that prioritized high-intent, in-market users over broad demographics. By using over 20+ behavioral and interest filters, our data and strategy teams built over 45+ unique user personas—capturing nuanced attributes like shopping habits, dining behavior, wireless carriers, device preferences, and even career or housing interests.

To avoid audience overlap and wasted impressions, we implemented exclusion targeting and placement-level whitelisting—ensuring every new layer added incremental value to the campaign.

Our campaign was fueled by predictive analysis, persona-based targeting, and content-safe placement optimization. We focused ad delivery on top-performing African content sites and apps, and optimized performance by learning from trend data across devices and ethnic geographies.

Smartphones remained the dominant touchpoint for new acquisition, while desktop devices gained traction in remarketing phases—helping us build a more comprehensive funnel. Creative-wise, the 300x250 banner led performance across all audiences and devices, while 320x50 dominated on mobile.

The campaign blocked over 15 million brand-unsafe impressions using our AI Osmosis engine and integrated IAS, MOAT, and DV for third-party brand safety.

Through hyperlocal content placements, in-market user signals, and device-specific creative testing, we achieved 70%+ reach penetration across key African diaspora audiences in the U.S.

By combining real-time behavioral insights with a highly segmented targeting strategy, the campaign successfully bridged the gap between interest and intent. Our emphasis on data-led creative testing, lookalike modeling, and multi-device strategy enabled MoneyGram to scale efficiently while maintaining brand safety and performance.