What is audience fragmentation on Meta?

Meta's second argument is about learning. It says that when you run too many ad sets at the same time, each one gets fewer opportunities to learn and therefore fewer results, so ad sets may spend more time in the learning phase, and you may spend more budget before the delivery system can fully optimise performance.

Meta's example splits ad sets to find the ideal audience, and its page names a second reason advertisers fragment: reporting. For that, its advice is, instead of fragmenting audiences for reporting purposes, to consider using breakdowns in ads reporting, which show performance across segments such as age, gender, country, region or business locations. It says you can create an automated rule to combine ad sets automatically if audience fragmentation is detected. In its opportunity score example, fixing an audience fragmentation issue may improve the score by 35 points, against 5 for applying an Advantage+ placements recommendation.

Our reading, not Meta's: finding the ideal audience is a question that separate ad sets cannot settle. They give separate numbers, but not a controlled comparison. Meta says it does not recommend testing informally, and that informal testing can lead to overlapping audiences, while its A/B test shows each version to a segment of your audience and ensures that nobody sees both.