Publish an original statistic AI models can cite

A single strong, original statistic occupies a specific and genuinely powerful niche in citable content — unlike a full research report, which requires substantial context to fully absorb, a well-documented individual statistic can be picked up, cited, and repeated across many different contexts almost as a standalone unit, each citation carrying an implicit link back to your business as the original source.

The genuine originality requirement here matters more than it might initially seem, and is worth being honest about. A statistic that's simply a rephrased or lightly adjusted version of an already-existing, publicly available number provides none of the citability benefit this mission is aimed at — the value comes specifically from producing a number that genuinely did not exist in public form before you published it, whether through original data collection, original analysis of your own operational data, or genuinely novel analysis of existing public data that produces a new insight.

Documentation of methodology is, again, what separates a citable statistic from one that gets treated with appropriate skepticism and ignored. Even a single number benefits from a clear, if brief, account of how it was derived — the sample size, the timeframe, the specific method of calculation — since this is exactly the information any careful evaluator needs before deciding whether to cite the number as reliable.

The promotional effort behind a strong original statistic determines whether its genuine potential for wide citation actually materializes. A well-documented, genuinely original statistic published quietly with no active promotion may never be discovered by the people and systems that would otherwise cite it widely; the same statistic, actively shared with relevant audiences and made genuinely easy to find and reference, has real potential to become a recurring, cited data point across your industry's broader content ecosystem.