The first real-world test came in Togo during COVID-19, when lockdowns made door-to-door surveys infeasible. Working with the Togolese government, researchers used phone metadata and machine learning to help identify people eligible for emergency cash transfers when lockdowns made in-person surveys impractical. The program reached over 138,000 people, and when researchers later compared different targeting approaches, the machine-learning method reduced exclusion errors by 8-14% relative to geographic targeting. Extrapolating a bit from the sample, this means roughly 4,000-8,000 additional people who should have received aid actually did.