With that information I can: • Say with a lot of confidence that billy69420@cbt.eu is owned by one person, called William. • Infer that, since the e-mail says Billy, but the name says William, the latter is probably what appears on any official documentation. • Infer that any services signed up to using billy69420@cbt.eu as the e-mail address are more than likely tied to this William person - including any services I run or otherwise have administrative access to. • Search for billy69420@cbt.eu on various public information repositories and reverse email searches. • Add an entry to a database using billy69420@cbt.eu as a primary key (e-mail addresses are necessarily unique) to enable me to keep track of my current and future information and inferences, thus building a profile on the person.

I can also understand that the sex and weed references mean that you've probably not given a real e-mail in this example, but if it were real I might be able to infer an age range (or at least a level of maturity) of the user from that, although this would be unreliable as the user could have made the e-mail account some time ago and just still happen to be using it.