Understanding the Influential Network in the Music Industry
Estimated reading time: 3 minutes, 12 seconds
Something pretty strange happened at RIMC. During his talk, Matt Roberts used the Linkdex network visualization tool to identify ‘who he should take for lunch’ i.e. who were the most influential speakers in the room that he hadn’t already met.
According to the tool, I was that person. And by complete coincidence, Matt and I were sat opposite at the speaker’s dinner.
After catching my first glimpse of the Northern Lights and being initiated to Icelandic ‘Black Death’, I (probably not so soberly) asked Matt whether we could analyse the whole music business community as a network using Linkdex.
I chose the music business community because I have a fairly good real-world understanding of who knows who, and who’s influential online, so I was interested in seeing how similar or different Linkdex’s analysis of the music industry would be in comparison to my understanding.
Here’s what Linkdex came out with:
At first glance, this is seriously impressive. While there are a few obvious anomalies (e.g. Gerd Leonhard hasn’t been involved in music for years, Fred Wilson is not really involved in music (he’s a VC), and Derek Sivers is far more influential than this graph suggests), it seems pretty accurate to me.
When looking at this analysis it’s worth bearing a few things in mind.
- The analysis was based on recent activity (last 200 tweets). It’s based on twitter conversation. Lots of chat happens via email, phone and face to face, so this is far from gospel. However, that doesn’t mean that it’s not useful or valid as a method of understanding relationships between people.
- When talking about ‘influence’ we’re referring to someone’s ability to change another person’s behaviour, not social popularity. The most influential people in this analysis are those who have the biggest impact on changing other people’s behaviour.
- The direction of the arrow indicates who is influencing who.
- The length of the arrow indicates relationship depth. The shorter the arrow, the closer the relationship.
- The people towards the center of the diagram are most important / influential in this community.
- The larger a person’s circle, the more influential that person is generally.
- The colour of the circles has no relevance (blue is me, orange were the starting nodes).
- A major limitation of this analysis is that it’s based on Twitter activity. While generally quite accurate, there are anomalies.
What’s the point of understanding networks?
In this specific example, the analysis was more a nice-to-have, but nonetheless it still gave me insights that have changed my behaviour. It opened up conversations and meetings with a number of interesting music folks that I hadn’t already met. It also gave me a better understanding of who’s speaking to who, and who my friends in the music industry are following that I’m not.
I’ve also used this network analysis to identify how the influence of different music industry publications differs, which could be very valuable for working out where to seed content or who to offer exclusive news to.
If I had no prior understanding of the music industry (or any industry), this would be a brilliant snapshot to begin an outreach campaign with. It’s clearly identified who the most socially influential people are in the whole industry, which would otherwise take hours of online analysis or years of immersion in the industry to fully understand.
I really like this tool. While it’s not something i’d use often, the insights it can provide you at the start of a project are invaluable in aligning your trajectory so that you’re engaging with the right influencers from the start. If you’re working in an unfamiliar niche or industry, these visualisations can be extremely useful in giving you a bit of direction.