Who Profits From Musiceuticals?

I've been wondering which companies might benefit from Musiceuticals. It's clear that Musiceuticals define a a new categorization of music, which means a new user experience. The two main results of the new user experience? Differentiation for the music or music system provider and new exposure for certain music and artists. Let's think about the first of these today. In thinking about music system providers, the question "Which companies benefit from Musiceutical innovation?" is the same as the question "Who benefits from differentiation?"

So who does? Well, Apple already used differentiation to dominate the industry. With 3 out of 4 music players some sort of iPod, Apple is riding that differentiation to the bank. Problem is, what's their motivation to take another leap? They seem more focused on hardware and video innovation than music user interface innovation lately. And it's certainly clear that if they DON'T focus on Musiceuticals, they won't want anyone else to…for the obvious reason that another company's strength in Musiceuticals creates a competitor with a real advantage over iTunes/iPod. By the way, since any Musiceutical data set is going to require lots of user data (see What Systems Do Musiceuticals Require below), the company with the most users--Apple--is actually best positioned to have the best Musiceutical data in the shortest time. But it is still hard to see them rocking their very profitable boat.

So who else? Microsoft Zune and Zune Marketplace seem the most obvious. Microsoft has given up on an open MP3-based music ecosystem because, well, they got crunched. So they recreated iTunes and iPod (with that ever-so-sexy flat brown look) and are poised to take over the music industry just as reinventing the Macintosh allowed them to own the computer industry. Will it work? Hmm. Lower user base. Stodgy image in a hip-oriented music world. Fewer songs (at least today). A bigger screen (but no better resolution). And players that really do look like your father's Oldsmobile. It seems like few today think they'll win, but you rarely win betting against Microsoft (not never, but rarely). They need differentiation. Differentiation that makes the music experience better. They are one company that needs Musiceuticals. And, of course, in the Zune world, they do indeed control the Systems Required by Musiceuticals….

Musiceuticals: What affects MY mood?

Most of the emails about Musiceuticals so far have fallen into two groups:

1. I want it, but it seems difficult, so it won't work.
   
2. I want it, but everyone is different, so it won't work.

I've been wondering about #2. It's what I referred to in the last post as the question: "What affects MY mood?" In other words, how can we account for the fact that music affects each of us in different ways?

As usual, I don't know for sure. But there are logical starting points that tie back to the two general approaches to song categorization: Algorithms and People. In either case, I think color is a good metaphor. Across cultures, different colors mean very different things. With thanks to About.com, below is an edited list of differences related to death:

Red

• South Africa: Color of mourning

Yellow

• Egypt: Color of mourning

Purple

• Thailand: Color of mourning (widows)

White

• Japan: White carnation symbolizes death
• Eastern: Funerals

Black

• Western: Funerals, bad guys
  

This variation is only in this one topic. We see similar variation in themes of purity, success, marriage, etc. Who among us on seeing lists like this hasn't found the difference interesting? Are the differences driven by something real and cultural or simply random changes that have taken root. For our wondering, it doesn't matter. Once they take root, they're real. For someone in a particular culture, you can take a pretty good guess that a color will register in their mind in a predictable way. I see Musiceutical customization happening in a similar way. Segments will form with similar reactions. How many segments will there be? Five? Fifty? 50,000? The data will tell us.

And how it might work takes us back to Algorithms vs. People.

If using Algorithms, songs will "cluster" in various ways. Whether by particular cadence, tonality, word choice, etc., songs will cluster. Some will be Musiceutically meaningful. Most won't. This is about people, after all, so people will have to register whether those clusters have meaning for them. Do songs in a particular cluster give you energy? ...bring you pleasure in a melancholy mood? ...get you psyched about an upcoming event? If so, similar songs will bring similar effects. And as people match themselves to clusters, the people themselves start to segment. For example, Group A will find songs in Cluster 23 energizing. Group B will find songs in Cluster 4 sad. Will people who fall into Group A agree on any other song Clusters? It'll be in the data. But I wouldn't be surprised.

If basing everything on People's song ratings, the effect is similar, but you can't really "pre-cluster" songs based on attributes. People's reactions, however, will start to segment with respect to particular songs. Since a pure "People" rated system can't guess in advance what cluster a song fits into, the people themselves have to be rated. That is, a person who consistently rates songs exactly like the rest of their segment becomes a trusted categorizer. If that person says a song is effective at driving or maintaining a particular emotion...it probably does. You can trust that "vote." It's worth noting that this person isn't more a leader or more insightful than anyone else. They just happen to respond more often to songs in a way that represents their segment. For someone who has more random reactions, you don't trust their vote as much and need lots more people to rate a song the same way before it goes in the hopper and gets labeled "an energy song" for those in the segment.

This isn't easy, but it will work and it will happen. The question is which main approach and how it's executed.

What Systems Do Musiceuticals Require?

I've been wondering about Musiceutical system support. It's clearly all about user experience (what a surprise).

In an obvious first step, devices have to support selection by Musiceutical. Listeners need to be able to say: "At this very moment, I want more energy." "At this very moment, I want inspiration and/or focus." Or perhaps even "deal my current sadness or melancholy." So obviously, providers need access to the device UI. Users need to be able to select Musiceuticals as easily as Playlists. If you control the whole experience, as Apple does, this is easy. If you sell commodity MP3 players differentiated only by physical design, you can alter your software to support Musiceuticals, but where do you get the songlists? Where do you get the data?

So the more interesting question is the data. This leads to two questions, which look similar, but are totally different. Both need research and new ideas:

1: "What affects my MOOD?"

2: "What affects MY mood?"

The difference here is: how to approach Musiceutical effectiveness in general vs. how to tailor them for varying taste, background, age, culture, etc.

This blog will already be long, so for today, I'll just offer initial thoughts about Question 1. As is typical in our Web 2.0 world, there are two approaches: Algorithms and People.

The algorithmic system needs to understand songs. It needs to deconstruct music to find relationships not visible simply by knowing artist and style. It's patently obvious that a single artist can have different songs with very different Musiceutical effects. I have to return to Pandora on this. Today, their focus is similarity for it's own sake, not for impact on emotions. They respond to the request, "Give me more songs like this." But you have to believe that a discussion of algorithmic approach among Pandora architects and engineers will instantly reach a higher level of sophistication than it would among a typical group. It's likely that an algorithmic system will require a "seed--a song that performs as desired leading to new options. I'd also expect that some subset of Pandora's "similarity algorithms" would be more applicable that others (e.g. cadence might be more important than instrument choice).

It also seems obvious that in this case, if a single "seed" is good, many seeds are better. A user who suggests more "effective" songs upfront or rates song effectiveness along the way will surely increase the effectiveness of any particular Musiceutical algorithm.

The other approach is People. With the right perceived value and painless user interface, large numbers of people will rate songs for Musiceutical application and effectiveness (e.g. this song "targets energy" and is "moderately effective at doing so"). This may be the shortest path to success since it requires less new thinking about the problem and can be a smooth expansion on current song ranking tools. For something to rise to the level of a "-ceutical," there's an implication of science or analytics beyond "voting," but this approach may provide better short-term results.

Both of these raise an immediate question: "Am I like the algorithm?" or "Am I like the others providing input?" In other words, "What affects MY mood?" That's for another day, but in the meantime, email ideas about algorithms or voting methods and I'll share them here.