Crawld for publishers
Archive-scale corpora, where overlap and decay compound quietly.
The situation
A large archive accumulated over years, much of it still ranking, some of it competing with itself, and no practical way to audit all of it by hand.
Which findings dominate here
The product is the same product for every segment: one rubric, 129 checks, the same three outcomes. What changes is which findings do the damage.
Cannibalization at scale
A decade of coverage on a recurring subject produces dozens of pages targeting the same intent. This is the segment where corpus-level analysis pays for itself fastest.
E-E-A-T signals
Named authors, dates, sourced claims and organisation identity: heavily weighted, and the things answer engines use to decide whether you are attributable.
Answer-engine citation
Publishers are among the most cited and least measured. Citation share per engine is the metric this segment currently flies blind on.
What the loop looks like
Scan, work the overlap map across the archive, re-measure. Archive consolidation is the highest-leverage work and the easiest to get wrong without a before-and-after.
Where this is a poor fit
The crawl covers what it can reach; a very large archive will report partial coverage, and the coverage figure is what tells you how much of it the score actually describes.