iPOD statistics...

About a month ago, I finally decided to buy an iPOD. Actually I just decided to buy anything that plays music and is small but liked the 'iPOD' so bought one. The first thing that hit me when making the choice was the total amount of music I own ( in any format and obtained by any means ) is less than half the memory of the lowest model of the series. So being the practical koknastha bought a 1GB iPOD shuffle. YaY glorious day !! now i have a player and so to the next complicated step of the project : filling it with music. Having almost never used windows ( thats for wimps anyway ) was fiddling around the net trying to figure out what to do.. so found salvation in gtk-pod an amazing gtk widget to synchronise with the apple pods ( 'i' or no 'i' ). It turns out that while using gtk pod is fun and you get a sense of accomplishment of putting one over the microsoft guys, there are a lot of things that one misses in gtk-pod. I realised that there is something called play count for every track one puts in. I was thinking whoever has done this is barmy.. why the hell would I want to know how many times I played a track ?. Then I discovered what iTUNES is all about. Though unfortunately this is available only for windows and MAC os ( damn those guys are after my money ), I was shocked out my headphones when I saw the online music store where one can buy tracks, single tracks!!!. But the most amazing thing is : iTUNES has 3 parameters to every track. play count, skip count ( the most interesting one I think among all the three ), and track rating ( ranges from 1 - 5 stars ) for grading all the music that one puts in. I guess these are parameters for the shuffle feature to learn what you like. BUT, it opens a huge universe of data for the music industry... here is what you get :
1. because They always ask you to 'register' the iPOD ( every time you run the program in fact )
so they know who has bought the iPOD and where they live
2. If there is a music store, there's gotta be a database of some sort of pod users, so then it is not a long leap to saving the 'names' of tracks used by these people sorted according to other data ( occupation age and so on )
the fun starts here :
each track can potentially be saved by the database with the parameters of how many times you played the track in a fixed time duration, how many times you skipped a track in that time duration, what you rate the track as.
So it is pretty simple that the track you played the most, or the one that you rate the most is the one you like the most, but what is even more interesting is the that track you kept on your iPOD but skipped playing the most. See you put if there because you like it, you did not delete it but kept skipping it. For example the pod comes with a shuffle mode so you could be doing it out of mere annoyance of getting the track at the wrong time to interfere with your mood.
so the most interesting thing to study would be how many people skip a particular track but don't delete it and how do they rate the track as. it means they want to listen to it, but not just now. maybe they associate it with something specific, or it reminds them of something specific, something that you don't listen to in traffic but listen to but like to pay attention to while listening.
Couple the skipping data with rating and how many times the track is played and you start getting all sorts of fun patterns.. there is one track on my iPOD that I have rated as 5 stars, but I don't listen to it all the time. in fact I SKIP it and listen to it only when I am in that mood.
compiling data using most listened to tracks is all fine, but compiling data with skipped but listened to tracks would be more interesting.
I mean I skip tracks that I don't want to listen to now, but I still keep them on the pod. Now i don't own much music and mostly listen to what I really like, maybe people have millions of tracks that they listen to on a single pod and therefore skip just because they don't like it.
But if iPOD statistics could be done I guess a pattern would emerge of some kind of tracks being special due to skipping. I have decided to compile some statistics of the songs that I listen to a lot and skip a lot, maybe I could discern a pattern over time. If anyone wants to help in this endeavour please send me YOUR iPOD statistics. you could ask for my contact id in the comments here.
Now this is amazing and a FREE demographic tool about any damn kind of music on the planet. Why have ratings and so on ? just use your iPOD user pool to figure out what the people like. Wonder if they use it in the iTUNES store to push songs onto people.. :D