What is Fan-out coverage ratio?
The fan-out coverage ratio is the headline metric of a sibling page architecture program: the count of dedicated sibling pages (the 'yes' coverage on the branch scoring) divided by the count of inferred mid-layer branches in the fan-out tree. The ratio is computed per head query and averaged across the priority set. A ratio above 0.7 is category-leading; 0.5–0.7 is competitive; below 0.5 is exposed and the program is leaving meaningful citation share on the table. Mid-2026 cohort medians: roughly 0.35 on mid-market programs, 0.62 on category-leading programs. The ratio is the single highest-correlated input to total citation share lift per editorial hour spent — the metric outperforms raw URL count, word count, and backlink count as a lift predictor. Track quarter over quarter alongside total citation share; the two move together with a 6–10 week lag.
How it relates to AI UGC
On multimodal-active branches the coverage ratio reads the visual-layer 'yes' separately from the text-layer 'yes' — a sibling with text coverage but no persona-locked image set scores 'partial' on the multimodal axis. Programs that ship the visual layer at the same cadence as the text layer hold a parallel ratio on the multimodal surface that compounds the text-side citation lift. ppl.studio production cadence supports the parallel visual-side coverage ratio without proportional production cost.
Key statistics
- Mid-2026 fan-out coverage ratio medians: 0.35 on mid-market programs, 0.62 on category-leading programs; ratios above 0.7 are category-leading (coverage-ratio cohort, 2026).
- The coverage ratio is more predictive of total citation share lift per editorial hour than URL count, word count, or backlink count — it is the single best lift predictor across the metric set audited in mid-2026 (lift-prediction audits, 2026).
- Coverage ratio and total citation share move together with a 6–10 week lag — programs that lift the ratio in a sprint see citation share lift land at week 6–10 post-publish, matching the engines' re-embedding cycles (lag-correlation cohort, 2026).