Asymmetric Data

Asymmetric Data is a keyword clustering service for SEO and content teams. You give it a list of keywords from your keyword research tool, and it returns a spreadsheet that groups those keywords into articles you could write. The aim is to turn a long, messy keyword export into a clear content plan.

How it works

The clustering is based on Google's search results. For each keyword, the service looks at which pages rank. When two keywords share several of the same ranking URLs, Google treats them as having the same meaning, so they can be targeted on one page. Keywords that do not overlap are placed in separate groups. This avoids writing several articles that compete for the same search intent.

Along with the groups, the report gives you the rank, estimated traffic and estimated opportunity for each keyword, so you can decide which articles to create first. The site says it supports clustering in most languages and many countries, including the United States, United Kingdom, Canada, Australia, much of Europe, Japan and Mexico. A full list of locations is linked from the homepage, along with an example report.

Pricing

Pricing is pay as you go, with no monthly plan to cancel. You sign up for free and buy credits, which stay valid for 12 months. At the time of writing, a launch offer charges $4 per 1,000 keywords at every order size, compared with the usual $5 to $7. For example, 10,000 keywords cost $40 and 100,000 cost $400, with no extra fees.

Who it is for

It suits SEO specialists, content strategists, agencies planning content for clients, and site owners with large keyword lists. The homepage quotes SEO practitioners who have used it on big projects.

Things to consider

Clustering tells you which keywords belong together, not whether you can rank for them. You still need to judge difficulty, your site's authority and business value. Search results change over time and differ by country, so clusters made today may shift. The traffic and opportunity figures are estimates. For a small list of a few hundred keywords, manual grouping may be enough, but for thousands of keywords this approach can save many hours.