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The Long Tail

A public diary on themes around my books


The miraculous power of scaleNovember 17

In this talk at UC Berkeley, Google's Sergey Brin confesses (at minute 1:27) that he thought Wikipedia couldn't work. Most people wouldn't contribute, he rightly assumed, and it would never reach critical mass.

He was in good company. In the classic "free rider" problem, you imagine an elementary school class with 20 students. If only two parents (10%) agree to volunteer to  help out as room parents and drive on field trips, the whole system breaks down: there aren't enough helpers and the two parents get angry at the others for not joining in. And that's exactly what Brin assumed would happen with Wikipedia.

But he was wrong, he says, because he--even he!--had underestimated the way scale can change the game. Sure, the experts say only 1% of Wikipedia's users actually contribute to making it better. Indeed, if you do the math, it's even worse than that: probably closer to 0.01% (today, Wikipedia has 75,000 active contributors out of 684 million visitors). But that 0.01% have created 10 million articles.

Most people don't contribute, just as Brin had feared, but it doesn't matter because the tiny fraction that do are a tiny fraction of an absolutely whopping number.

The lesson is that more is different. The Internet, by giving everybody access to a market of hundreds of millions of people, can work at participation rates that

Does the Long Tail create bigger hits or smaller ones?November 16

schmidt Over the past few weeks there has been a flurry of reappraisals of the Long Tail, most of which center around the question of whether it creates bigger blockbusters or smaller ones (more concentrated markets or less concentrated ones).

My predictions have always been that massive increase in variety plus massive improvements in "filters" (tools to make it easier to find new stuff that's right for you) would tend to reduce the blockbuster effect and redistribute attention over a wider range. And, indeed, that's what the data I cited in my book showed, where online markets of books, DVDs and music saw between 20% and 40% of the demand shift to products not available in traditional bricks and mortar stores.

But there were clearly exceptions to this. One of the main ones was the irony that there was a very short Head of Long Tail aggregators: Amazon, iTunes, Google and their kin dominate their markets to a blockbuster-like degree.

I blamed this on a still-young market and assumed that even aggregators would fall victim to the flight from one-size-fits-all someday. But

Freemium math: what's the right conversion percentage?November 14

In my original Wired article on Free, I described Freemium as the opposite of the traditional free sample: instead of giving away 1% of your product to sell 99%, you give away 99% of your product to sell `1%. The reason this makes sense is that for digital products, where the marginal cost is close to zero, the 99% cost you little and allow you to reach a huge market. So the 1% you convert, is 1% of a big number.

But that was just a hypothetical percentage split, to make a point. In the real world, what's the right balance? The answer varies from market to market, but some of the best data is in the games world.

In online free-to-play games, companies aim to structure their costs so they can break even if as little as 5-10% of the users pay. Anything above that is profit. Which is why these numbers from MMPOW, a blog that covers the industry, are so impressive:

  • Club Penguin: 25% monthly uniques pay, $5/mo per paying user
  • Habbo: 10% monthly players pay, $10.30/mo per paying user
  • Runescape: 16.6% monthly uniques pay, $5/mo per paying user
  • Puzzle Pirates: 22% monthly players pay, $7.95/mo per paying user

As the blog notes, that compares very well to the 2% of the casual downloadable game market that pays, or a 3-5% that a lot of "penny gap

Finding a Freemium model that works for youNovember 13

I was chatting last night with the CEO of one of the biggest software-as-a-service companies about how he could release a version of his product with a freemium model. The options that seemed best for him were these four:

1) Time limited (30 days free, then pay. This is the Salesforce model)

  • Upside: Easy to do, low risk of cannibalization
  • Downside: Many potential customers will be unwilling to commit enough to give the software a real test, since they know that if they don't pay they'll get no benefit after 30 days.

2) Feature limited (basic version free, more sophisticated version paid. This is the WordPress model)

  • Upside: Best way to maximize reach. When customers convert to paid, they're doing it for the right reason (they understand the value of what they're paying for) and are likely to be more loyal and less price sensitive.
  • Downside: Need to create two versions of the product. If you put too many features in the free version, not enough people will convert. If you put too few, not enough will use it long enough to convert.

3) Seat limited (can be used by up to some number of people for free, but more than that is paid. This is the Intuit QuickBooks model)

More Long Tail debate: mobile music no, search yesNovember 9

Over in the UK, Will Poole, who works for copyright collection society there, presented some interesting data at a music conference last week that suggests that the Long Tail's usual powerlaw shape doesn't fit the sales they're seeing.

I can't find his presentation online, so I have to go with the good coverage by Yankee Group analyst Benoit Felton and the comically angry coverage of The Register. The basics are that Page and the founders of Mblox, a mobile music provider, presented research that showed that a dataset of the sales of 13 million tracks showed a lognormal distribution, rather than the usual powerlaw, and as the result the total sales in the Tail were lower than the theory would predict.

Here are the two specific datapoints that came out in the coverage

  • 80 per cent of the revenue came from the top 52,000 songs (which is the inventory in a typical music store, my definition of "head"). In my own Rhapsody research, only 60% of the downloads were in top 52,000. So the tail was half as big in their dataset as it was in mine.
  • Of the 13 million available tracks on iTunes UK, 10 million don't sell at all. They don't say over what period.

And here are my three thoughts on that: