Predicting Music Popularity and Marketing Analytics

What do Taylor Swift's videos have to tell us about marketing?

There's a machine learning competition running here in Salt Lake City that deals with predicting the popularity of Taylor Swift videos from snippets of music. And by snippets I mean 1/10th of a second of a song. The goal is to classify whether a song is a 'success' or 'not' (if you can take 30m views as not being successful and 300m as success.)

Source

In marketing we try to understand what makes a consumer tick, but in this case an audio track has been converted to a series of numbers that look something like the following. Now imagine 3,300 columns of numbers for each sample...

  Success     V1        V2      V3        V4       V5      V6      V7     V8        V9
       0       -5787    9566    -511    -2274 18589   1170    2232   5073   -2578
       1        1067   -  521    1524     -209    -957    -777  -2666     716   -3273
       1          397     -314   -1701   -2568    -463   1123    1041     916      789
       1          784   -1017    2038     4134   1453    3731   3644    2759  -2033
       0     -19632 -13344 -14428 -13195  -3940  -1306  -1206   -1303  -1256

This is where you have to trust the machine and the team. There will be no conference room debate as to what impacts success - the math just works. And that raises an important question: how do we now improve the consumer experience and deliver on the brand promise?

Keeping with the music theme, a musicologist can deconstruct the signals into themes and styles much in the same way that Pandora was created from a music genome. As a result the experts won't be just the ones who can apply a Restricted-Boltzmann-Machine to a classification problem, but those who can translate the results. Interpreters will be in as much demand as the coders.

Keeping my Rosetta Stone polished.