What is dark share?

Most listening tools work from keywords: they find the posts that spell the brand’s name. But a large slice of real presence never names the brand at all. The jersey logo in a fan photo, the sneakers in an unboxing video, the shorthand people actually use (think “Maccas” for McDonald’s), the misspellings. All of it registers with the humans who see it, and none of it registers with a mention counter.

Dark share matters because it distorts the metrics teams already report. Two brands with identical tagged mention counts can have very different real footprints, and category moments often surface first as untagged visual presence, long before anyone types the name. Seeing it requires going beyond text matching: recognizing logos in images, products in video, and aliases and misspellings in text, then folding those appearances into the same share math as the tagged mentions.

The practical trap is treating tagged share of voice as the whole picture and optimizing against it. Brands with strong visual identities, or brands people discuss in slang, are systematically undercounted; a text-only tracker can report that a streetwear label is losing conversation share in the same week its logo is everywhere on the platform.

How this shows up in Waldo

Dark share is the problem Waldo was built around: the platform stores and analyzes the full record for tracked brands and categories, text, images, video, and metrics together, so untagged appearances are part of the dataset rather than a blind spot. When a share number moves, the unnamed presence is in scope, and every output links back to the underlying posts.

Related terms and reading