From Listen to Live Show, Music Streaming Just Got Smarter

Spotify has undoubtedly made my music listening life infinitely better, but there remain several areas in which it can improve. Recently the guys and gals in green made a huge stride in one of these areas, namely that of applying data to discovery.

Spotify Discover Function

From listen to live show, can apps like Songkick drive up show attendances?

I have plenty to write about on music discovery via algorithms versus the human touch, but here I simply want to shine a light on the elegant simplicity of integrating local concert listings into this broader recommendations channel.

 

From the Stream To the Show

Who makes up your music audience?

Image Credit: Anirudh Koul

This may seem like a natural extension – indeed, one that was already available by visiting the existing Songkick app tab – but the significance of bringing the show alongside song recommendations should not be overlooked.

Many users look for guidance on what to play when they first open a listening platform such as Spotify, meaning that the Discover page will be a highly visited area. Throw in what appears to be a much refined recommendation engine, one that has thrown up some genuinely intriguing unknowns for me in the last week, and you have the potential for a lot of eyeballs perusing these listings.

If even a small percentage begin to show an interest in the concert element of the page, it seems like something that all parties involved would benefit from developing further.

Personally, I see an increasingly valuable place for services like Songkick in both becoming a go-to source when I want to browse gig listings and delivering concert news to me. Combining my online listening history with that service helps to filter and improve the latter, making both services even more useful and raising the likelihood that I can be persuaded to purchase a ticket.

 

Next Steps

From integrating Facebook data to recommend shows based on the upcoming events of friends, to converting fan follows and listener likes into information that artists can use to better target their marketing, there are a great many extensions of this move that may bode well for music makers.

At a time when streaming services are regularly under fire for simply making money off the backs of the creators whose content fuels their business model, it is heartening to see moves being made to use the vast data sets they collect to pull fans further into the music.

Whether or not such connections actually drive up sales and attendances remains to be seen but, as any marketer will tell you, visibility, relevance, and a compelling call to action are key. Functions like Spotify’s Discover begin to solve the first two elements, but there will be a great deal of tweaking and dealing on the third before we begin to see a truly valuable connection between the listen and the live show.

 

Music Marketing Matters: How to Win Data & Influence People

In broaching the subject of data gathering and marketing for musicians last week, I quickly realized that this was going to be bigger than one post would allow. Unless one is talented enough to compose enormous articles that remain fresh and coherent throughout – as can Judy Gombita, for example – one should make like a dubstepper and Break. It. Down.
 

As luck would have it, this is also the approach that you should take with your data gathering.

Break down the walls of your fan data

Image Credit: Ross

Bite Size Data

Unless you’ve been diligently collecting and organizing your fan data for years, which I imagine is akin to the 1%, it’s likely you have one large block of unsorted data, knocking around with several other scraps. Taken as a whole, these form your overall audience database.

And take them as a whole is exactly what we’ll do first, before going on to break it all down again. This time, however, we’ll be doing so in a more productive manner.

 

Building Your Audience Database

Follow these steps to build your initial database. If you already have this in good order, please move on to the next section, ‘Music Marketing Segmentation’.

 
1. Gather every piece of fan data that you’ve collected on one place, in an easy to read format.

 

2. Start a spreadsheet in Excel (or a free equivalent, like Open Office‘s Calc or Google Docs) and type out each relevant category across the top row of this sheet. For every recurring piece of data you have, e.g. First name, last name, E-mail, location etc, you should have a category for it on your sheet. Discard any infrequently occurring data like nicknames or feedback. If it’s pertinent, we can record it later in a general ‘Notes’ column.

 

3. Transfer all the data, once you have all the column headings you need, over to your new spreadsheet. Yes, this is the particularly tedious part… don’t worry, I can wait….

 

4. Done? Congratulations! Have a cup of tea to celebrate and come back in half an hour.

 

5. Now begins the fun… data gathering and filling in the blanks. Add any ‘would like to have’ pieces of information to your column headings. Examples might be income, job title or industry, type of relationship (friend, family, or some more complex measure of acquaintance that we can work on). Don’t stress too much on these, as we can add more later, but DO think about the type of information that you’d like to know about your fans. What would help you connect to them more effectively?

 

6. Once you’re happy with the skeleton of your database, it’s time to add as much flesh to the bones as possible. Thankfully we live in an age of seemingly constant sharing, so stalking… researching your fans to fill in any data blanks is more viable than ever before. Start with a basic Google search of names, focusing on social networks to begin with as they have more standardized information layouts. If you’re still drawing some blanks, delve into blogs they frequent, pseudonyms that they use for online handles, or combination searches involving other data that you already have.
 

Once you’ve exhausted as many avenues as you can think of to complete your data set, accept any omissions and save the sheet in a couple of safe places, one hard drive and one accessible remotely, if possible.

Et voila, your information foundation is set! 

Remember to use the header categories that you’ve laid out here for all future data that you collect from people. This keeps everything complete and aligned with what you have identified as important things to learn about your fan base.

 

Music Marketing Segmentation

 

Types of segmentation

A preferable form of segmentation

As I mentioned earlier, we only built this up so that you can break it back down again. This time we’ll do it in an orderly fashion, however, by segmenting the market for your music.

Having gathered all this data about your existing fans, you can use it to make your communications to each of them more targeted. This benefits you because you can offer more clear and relevant news and offers to each segment of fans. It benefits the fans as well, as you aren’t just blasting out general announcements to your entire list, hoping that some of of will stick.

 

Segment Suggestions

You can slice and dice your database into segments in many ways, subject to your targets and the data that you managed to gather.

Here I’ll offer up five segmentation suggestions to get you started. If you start to play around with these, you should find that you begin to understand your data set and develop your own segments.

 

1. LOCATION: Where people live is one of the easiest and readily available pieces of data that you’ll have to hand. It is also one of the most potentially valuable, allowing you to identify clusters of fans for tour plans, geographical trends, and areas for potential street teams or fan meet ups (if coupled with number three on this list). Location is a solid place to start to feel out your data and get comfortable manipulating it into groups. If you need to add broader categories such as East coast, Midwest etc, feel free to create another column and segment in this way as well. 

 

2. AGE: How old your audience is can help you to infer many follow on points, such as their spending power, media preference, musical tastes, and much more. Although some of this will be an educated guess, it also gives you a platform from which to ask these questions the next time you engage them. You can also combine with other data, such as location, to identify audience diversity in various regions. This can help with anything from merchandise choices to venue decisions e.g. if much of your audience in Detroit is under 18, you’ll know you need to find a venue without drinking age restrictions. 

 

3. FAN STATUS: You can add an extra nuance to your data by assigning your own ranking of fan level. This can be based on any number of factors, including number of gigs attended, purchases made, length of relationship, feedback received, or some combination thereof. Digging this deep will allow you to tailor communications to the appropriate sections of your fan base. For example, to crowd fund a limited edition vinyl release you will probably only approach ‘super-fans’, where as sending brand new fans only a special offer for your older material will avoid a pointless communication to long term fans who already have those releases. 

4. PREFERRED GENRE: Understanding the types of music that individual fans enjoy gives you the chance to hyper-target new material, right down to releasing an individual song especially for that group. It can also help to refine set lists when combined with location and age related data, target recommendations of similar artists when you try to help out other artists, and perhaps even influence the way you write your next material. I know, I know, you write what’s in your heart. But it can’t hurt to have an insight into what your fans like as well, can it?

 

5. INFLUENCE LEVEL: This may require further research, or you may simply have a good feel for those of your fans who are influential over the tastes of others, but either way, knowing who to approach to spread your music is a valuable piece of data to have available. Though there are sites like Kred that can help you to ferret out influencers in a certain field online, your most likely route to segmenting in this way is to assign a simple rank for each person, based on recommendation behavior you see online (or lack thereof… perhaps you use a null value, in cases where you simply cannot tell). Does the person regularly share music, post YouTube videos that get likes on Facebook, or write about their tastes online? All are indicators that they should be added to a segmented group that you can go to when your hot new tune needs that extra push.

Over To You!

Have you already worked on something similar to this? How did you segment your data and what results did you see?

For those of you just starting out, does this seem like a valuable exercise? What questions are lingering for you?

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