One of two workflows Data Dive offers for building a competitor set: an automated "Niche Dive" versus manual ASIN-tray curation.
Fast, roughly 95% sufficient on its own. Surfaces candidate competitors with a per-competitor fit score, letting a seller quickly approve or reject suggestions rather than building the set ASIN-by-ASIN.
Slower but more precise — the seller manually adds each competitor ASIN to a tray. Used to hand-correct the last mile of a niche dive, or to build a set from scratch when automated fit scores are unreliable (e.g. very small or unusual niches).
Niche Dive works by comparing a selected hero listing against related search terms and subcategories, auto-selecting roughly 15 closest-fit competitors and assigning each a percentage fit score. It is the fast default for building a niche and is described as sufficient about 95% of the time — sellers should still review each suggested competitor's fit score before finalizing the set, falling back to the manual workflow in Data Dive Competitor Set Curation when the automated picks look questionable.
Из тем: Product Research & Validation