.ART pricing model & Big Data algorithm

From the very beginning we were aware that it will take the art community some time to learn about .ART, while the domain community was aware of its emergence long before it actually happened. To avoid exploitation of this inequality, we spent a considerable amount of time and incurred significant expenses, being fully dedicated to the “right name to the right hands” principle.

The .ART premium names strategy is based on a unique algorithm – an exhaustive “big-data” collection and analysis approach that gathered hundreds of millions of words and word-combinations from online sources (e.g., Wikipedia and Alexa ranking) and static sources (e.g., art reference materials). This occurred over months of online data collection and included billions of queries. Each word combination was scored through a 14-dimensional matrix, against such criteria as word length (the shorter the better), and frequency of Google search (the more the better) in order to create relative values. These values were compared against every prior premium domain sale in the past, and, after appropriate normalization, a prospective market value was calculated for each of the possible domains.

Among 150 million possible combinations, only about 2% had significant value for the art community. These 3.5 million premium names were filtered out and called “inventory”. Most of them fall into the 100-300$ price range, thus being too expensive for cyber-squatters to purchase as a mindless investment. Premium names are also an indicator of recognisability in the art world. The more recognisable your name is, the more traffic is can generate, hence it becomes more expensive. This makes it more valuable and desirable for cyber-squatters. It practically becomes a liquid digital asset. At the same time, this doesn’t affect the wider artistic community, as they mostly purchase name+surname combinations, which are priced at a standard rate. This balanced approach is a natural barrier, making .ART a domain zone for those who truly belong in it.

Also published on Medium.

Tamara M