Now that some version of Apple’s iRadio contract terms are available online, we can begin to do some comparisons between the effective rates contained in these terms and those rates paid by Webcasters. In this post, I will (eventually) present some simple tables that adjust Webcaster rates to account for certain affordances in the Apple iRadio contract — specifically those terms through which a stream on iRadio would not trigger a royalty obligation. What we’ll find is that because Webcasters have to pay performance royalties for skipped tracks, while iRadio will not (for the first six tracks skipped), these Webcasters may pay more for music than most people may realize.
Churn is a topic in its cooling off period at the moment, with Mulligan, Resnikoff, Peoples, Dredge and others contributing a short time ago to a discussion on this issue as applied to new music services. The debate centers on whether the relative proportion of active versus inactive users (so-called zombies) is a good or bad sign for new music services.
I think something more important than churn may be taking place as the music services market expands. Essentially, the market grew quickest during a period that likely also offered the highest rate of churn. The US market for music services offers a particularly useful space for digging into this issue.
For the long story, head to Rockonomic.com …
Rumors now abound as far as the possible launch of an “iRadio” service from Apple (Forbes, Forbes again, the Verge, Cnet, ). Embedded within these rumors are discussions of the rates at which royalties from such a service would be paid.
Less discussed in these stories, however, is how these iRadio royalties might be distributed differently — to labels, performing artists, musicians, etc. — as compared to those royalties paid by other providers of radio-like experiences online (e.g., “Webcasters”).
A discussion of the difference in how these royalties might be paid is the purpose of this post.
Read on @Rockonomic.com…
While I do not have formal data on the subject, my sense of the historic landscape would be that university incubators, accelerators, and even startup weekends — relatively recent phenomena — may have already spun out a larger pool of surviving startups than that pool spun out over the entire history of university business plan competitions. If true, than the predominance of “Garages” (my word for incubators) on college campuses may need to be taken far more seriously.
There are some people out there who consider incubators a hot trend, if not a cliché. In the minds of these folks, incubators are simply playgrounds where monkey bars, see -saws, and slides have been replaced by computers, bean bag chairs, and whiteboards. The undeniably high failure rate of startups from incubators simply adds fuel to the argument that these new venture zones may be more akin to McDonalds playlots.
In my opinion, this “new” phenomenon of shared startup space is actually a reflection of something quite old. Incubators simply bring together and into and single location and clear sight those startup activities that were previously taking place in dorm rooms, apartments, coffee shops, and — well — garages. Furthermore, these community workzones encourage the sorts of behaviors common to vibrant startup communities: e.g., open innovation, tinkering, fluid teaming, demoing, and more.
If you see startup activity from an evolutionary (or natural selection) perspective, than the seemingly random attempts (and failures) of a large numbers of startups is what we need in order to happen upon that small number of surviving ideas and teams. In essence, the purpose of the incubator space is to accelerate natural selection — to curate an ecosystem within which both failure and the glimmer of success surface sooner rather than later, at as little cost as possible. In fact, this accelerated selection is more like evolution than incubation. Most chicks survive incubation. Most variations fail through selection.
From a purely pragmatic perspective, there are a number of more basic reasons for incubators having the potential to trump plan competitions, when it comes to raw startup development:
Building what you can build versus Pitching what you can’t build.
Incubators tend to require of the individuals therein either(a) the ability to build what they are imagining or (b) the tenacity to find someone, right now, to help build what they have imagined. As a result, the teams in these workspaces tend to be in position to act on their ideas, almost immediately.
Plan competitions, however, tend to be populated by ideas presented by teams who lack the capacity to pursue the idea at the moment. Instead, these people are pitching for the capital to acquire those things that early teams truly require — the portfolio of talent required to get through the most uncertain phases of the startup: proof of concept and customer #1.
Learning through mentoring versus Winning by judgment.
The best startup workspaces are more than just walls with whiteboards and power outlets. These spaces aggregate a community of individuals and, importantly, mentors. Building out a startup is ultimately a craft, and mentoring has been the method over the millennia through which craft is developed and refined.
Competitions are guided by judgement, and often conclude with that judgement. Winners are chosen, and that moment can often be the least time the team and the judges ever communicate again.
Furthermore, competition selection is wholly reliant upon the judgment of a small set of individuals. As a result, we significantly limit the population of new firms based upon the impressions of people who, while intelligent and experienced, may not have all of the information or experience that is needed for the best decision at the time.
The best of both worlds
When you think about it, what we really need is the best of both worlds: the market research, feasibility analyses, and capital that go into business plan competitions combined with the tinkering, teaming, and mentoring activities that are common among incubators. Certain universities have already begun to merge these two phenomena, and this combination may very well result in a compelling new cohort of student startups.
What if startup success might be best modeled and even navigated if it were imagined to be akin to a random walk down Sandhill Road? This question is the one that has recaptured my interest lately.
Simply stated, a random walk is a process that can be understood as a series of steps or stages, wherein each stage or step can be described according to some probabilistic set of outcomes (e.g., a coin toss, or a normal distribution). One of the classic examples of a random walk involves mapping the circuitous walk home of an individual who has had too much alcohol. At any corner on their walk home, this inebriated individual might turn left or right, go forward or turn back. While a map of this drunken walk betrays the underlying randomness of the clouded mind, the probability that the individual will make it home is actually pretty good — even when each decision at each corner is understood as simply a 1 in 4 chance of heading in the right direction.
If startup success were a random walk, we might find that attempts to predict success in this market would be foiled. Investment returns would be highly volatile, with low success rates but (potentially) very high returns. Investors in this sort of market would be very likely to embed “options” and other downside protections into their deal terms (e.g., liquidation preferences, follow-ons, anti-dilution, etc.).
Furthermore, and from a sociological perspective, actors in the market would be very likely to imitate the behaviors and characteristics of successful individuals (e.g., hoodies), given the ideal attributes of success would be hard to predict. For example, since Google instituted a 20% rule for employee projects, other firms would emulate this rule just in case it had something to do with Google’s success.
Evidence for random walk: present.
By and large, we tend to associate startup success with some indispensable personality trait (e.g., gambler, gunslinger, genius, or guru, according to my research), defendable market position, inimitable technology, or simply dumb luck. In this context, asserting that startup success is a function of something “random,” is largely interpreted to mean that all of those other factors are trumped by the last … dumb luck.
In truth, however, each of these factors — whether realistic or foolhardy — could play a role in navigating a random walk. I don’t translate “random” to mean that survival is based purely upon dumb luck. Instead, I tend to see randomness, when affiliated with risk, as a phenomenon that can be navigated and managed through a range of strategies.
From this random walk perspective, a phenomenon that may be random, at its core, can be managed successfully through a portfolio of options and other strategies that accomplish two interconnected goals: (1) limiting exposure to negative outcomes (aka, downside), while (2) opening if not increasing exposure to positive outcomes (aka, upside). My word for this portfolio of strategies is “enterprising.”
For example, the premise of affordable loss emerging from the effectuation school in entrepreneurship research can be seen as an effort to manage downside risk in a random walk; never bet more than you are willing or able to lose. This rule limits downside risk by simply limiting the size of the investment. Affordable loss may be a “weak” strategy, however, since it likely limits both upside and downside by constraining the entire investment amount. Other strategies may be “stronger.”
Importantly, the odds of success are not really changed with these strategies, all other factors held equal. Instead, the firm’s exposure to both desirable and undesirable outcomes is altered. This difference is important. We may not really change the probability of survival in a set of firms, all in the same market, all with the same initial investment, etc.
However, we would see that the payoff profile of the more enterprising firms — those that manage risk in this asymmetrical manner — would be quite different from the payoff profile of the less enterprising firms.
What if startup success were best understood as a random walk down Sandhill Road?
I found a napkin next to my coffee cup this morning and decided to estimate the value of a video stream, per viewer, for VEVO.
The maths scribbled on this napkin didn’t have to be too complicated since the company has been rather forthcoming in public regarding (a) the total amount paid in royalties to music owners and (b) the number of video views, on a global basis, during various months.
Using a collection of the inputs (a) and (b), a back-of-the-napkin estimate of the effective rate per stream paid by VEVO would be:
$0.00238 per viewer stream (pvs), with a range from $0.00222 to $0.0025 pvs.
I will leave it up to the reader to compare that pvs rate for VEVO to the various per listener stream (pls) for the range of alternative venues for music experiences (e.g., music services like Spotify, Rhapsody; YouTube; Webcasters; Broadcast Radio). For example, most reports would now place Spotify pls rates at about 2 to 3 times larger than this estimated VEVO pvs rate.
For more details, head to Rockonomic.com
In this post, I am going to seriously question the “10 Tracks sold equal 1 Album sold” that has historically been the default setting for the “Album Equivalent” ratio for the music industry. I am going to recalculate the total Equivalent Albums sold over the last decade assuming this ratio were actually around 3, in order to provide an example of just what a difference a new ratio makes.
Most importantly, I am going to suggest that the Album Equivalent ratio should be based upon actual consumer behavior — meaning, based upon observations of the actual ratio at which consumers are willing to substitute track purchases/streams for albums purchases/streams — rather than based upon how the recorded music industry puts tracks into buckets and calls those buckets albums.