Now that you’re presiding over your newly formed digital empire don’t think you can rest on your laurels, you can’t take a Field of Dreams approach to this stuff just because you build it does not mean people will flock to be part of it.

The digital world has put control firmly back in the hands of the consumer, this is no more evident than in the music industry. Fans can now download the once precious commodity of recorded music for free, all be it illegally, therefore you need to compete with free.

Now more than ever you need to “monetize”, “incentivize” and “supersize” your fan base, or just turn it up to 11… whatever floats your boat.

If you followed our tips in part one I wouldn’t be surprised if people were clamouring to get your music, come to your shows and who knows maybe even financially reward you for your efforts?

One of the hardest things about growing a following in the current media landscape is the ADD culture within which we live, nowadays there’s a new buzz band for everyday of the week. So given that you’ve worked hard to make it to buzz band of the day you’ll want to remain there, at least until the end of the week.

So let’s give your rabid music fans something to stop them salivating…

So you’ve made the decision, you want to be a superstar…

Congratulations…

You’ve taken the first step, ahead of you lies misery, heartache, betrayal, divorce courts, a crippling alcohol/drug addiction and if you’re lucky, you’ll survive long enough to make your triumphant comeback tour (Whitney Houston aside)

But all that’s in the future right now your puzzling over why your internet search for “how do I harness the all encompassing power of the internet to become an incredibly famous and successful musician overnight?” hasn’t turned up anything useful.

An update from the development team…

Our aim at musicmetric is quite simple: We will collect and analyse all the data on the web (and some that isn’t) related to trends in music and present it to our users in an easily accessible and actionable format. Over the next few months we will have downloaded and analysed a large proportion of all relevant published articles, and will continue to do so as they are written to keep right up to date with opinions, trends and buzz.

Our aims are simple, but the challenges we’ve faced over the last year and a half approaching our launch have been far from trivial, and hopefully this post will give some insight into the technical side of what we’re doing.

Gathering the data, although the easy part, needs an extensive hardware infrastructure to download, extract and archive text from millions of pages a month. Accurately analysing, scaling and detecting patterns in the data locked up in these terabytes of text is the real challenge and most interesting part of working on musicmetric. It would be naive to simply present raw data as trends in the global music landscape (although we do supply raw data), the trend tracking methods we have developed would be useless if not scaled by accurate influence ranking for the sources of these trends, and simply calculating these scores is a huge task in itself.

Likewise, following activity on just one or two social media websites and presenting this as trends would give a massively biased view of where an artist is actually popular. For example, the social media website Orkut is hugely popular in Brazil, so all data originating from this website would be biased towards that country. Likewise with Twitter, trends would lean towards the UK / USA and not necessarily reflect a global view. We are rolling out tracking for multiple social networks over the next month.

Another challenge faced are the methods we have developed for text mining and sentiment analysis (and not just the fact that we need to analyse over a million documents per day). An example would be the band Pavement. How does a machine know if a piece of text is referring to the band, or a pavement alongside a road. What about two artists with the same name? There are three artists that go by the name Nirvana, seven are called Justice. Which one does our customer care about? Perhaps all of them? Disambiguation is key for these applications to work correctly. The methods we use for sentiment analysis also have to cope with changing vocabulary, or even different languages so adaptive methods are key, for this reason we employ a machine learning approach to this problem, which again has taken a long time in development.

Because we know our customers are using this data to make important decisions in how they run their business or manage their artists, we are making absolutely sure that the data is reliable, trustworthy and complete. Traceability of data sources is paramount to reliability. Our infrastructure allows full audit of any piece of data at any time, from how it was scaled or normalised, right back to which one of our servers originally collected the raw version. This is important for a variety of reasons, particularly the ability to show exactly why trends are occurring, and improves trust in our analytics. It is one thing displaying a line chart or an index showing success for an artist, it is quite another presenting a full breakdown of each source of data and how it was included in the analysis, giving clear perspective on how that line chart or index was calculated.

musicmetric is a well funded team of 6 fulltime staff (and growing) with extensive backgrounds and deep knowledge in the field, we are using cutting edge technology and work closely with our partners to solve difficult problems and have spent the last year and a half working these out. We are extremely excited to be coming towards the end of our development / alpha stage and into our official beta, then preparing for our full launch in November.