Circl.es is an online dating site that matches people using a people’s public Facebook profile rather than relying on the data produced by a complex algorithm of self-reported answers.
To be clear:
Nobody on Facebook can see you’re using Circl.es, but integrating with Facebook allows us to ensure we show real people with real friends and interests, and it allows us to dramatically simplify the process of online dating – signing up takes two minutes. We also filter out your Facebook friends so you don’t see people you know.
As dating sites do, Circl.es generates a lot of data. Last month, it generated over 100,000 yes/no answers. “[P]eople say yes when they’re interested in someone, and no if they’re not. So, for each person, you have something we call the ‘Desirability Quotient’ — the percentage of “yes” answers out of the total yes and nos that person receives.” There is also the “Selectivity Quotient” — the percentage of yes answers that person gives to other people.
The people behind Circl.es are interested in finding out whether any of that data can be used to the benefit of user experience, so they’ve started a series of blog posts within which they analyse and present their findings on a given set of collected data.
The first set of data they looked at and blogged about was email domains. On the outset, Circl.es expected to find that Gmail users were more attractive than users of other email domains.
Interestingly, women using “other” email domains get almost a full 10 per cent more “yes” clicks than average. Mostly, these are .edus, college email accounts, for women who are either in college or recent graduates who haven’t updated the email they use for their Facebook accounts. It’s not especially surprising or new information, but only further supports the theory that a young, college educated woman is the most desirable to date.
• Circl.es users can’t see each others’ email addresses — only we can see this information in our database.
• Circl.es has an option for non-binary genders, but we don’t have enough data on these users to include them in this analysis. We also didn’t have enough data to include AOL users.
• This is basic analysis, and not perfect. Ideally this data would be adjusted for age and other factors — eg, certain email domains skew younger and this may be a confounding factor.