data-and-original-research

The original-data content type — find a question inside a data void, run a sound method, analyse it honestly, voice the one finding that travels, and engineer it for citation. A study people have to cite; the format writers turn it into cuts, WoopSocial publishes, and recurring studies map into the content-calendar.

The POV: own a number and the internet has to come to you

Most content is undifferentiated — ~94% of published pages earn zero external links (per Backlinko). Original data is the rare exception: publications link to stories, not products, and a data finding is a story. It's also the #1 GEO asset — adding statistics is among the strongest levers for AI-answer visibility (per the Princeton/KDD GEO study), and original data is statistics nobody else owns. Brands skip it because it's harder than a listicle — which is exactly the moat. The catch: a study is worth nothing the moment one number is wrong. Rigor isn't pedantry; it's the entire value. So the skill is knowing what to study, how to get real data, and how to make the finding impossible not to cite — never inventing it.

Read these first

1. brand-profile — the proprietary data/angle you actually own.
2. audience-research — the question your audience (and journalists/AI) would cite.