Speculating on the Greek Crisis, Internet Edition

At the recent Milken Conference, I attended a panel moderated by Mike “Zappy” Zapolin. His claim to fame? He struck internet gold by developing generic web domains like beer.com, music.com, and the all-too-timely debt.com.

It’s much harder to follow in Zappy’s footsteps today since the obvious names are all gone. Except when new developments create new opportunities.

So it was last Thursday when I had an epiphany: Given the turmoil in Europe, Greece may eventually drop out of the euro. And instead of resuscitating the drachma, maybe Greece will opt for a currency called the “new drachma”.

I had this little insight about 2:35pm on Thursday afternoon. And then I got distracted by the hoopla over Wall Street’s “flash crash.”

I finally found my way over to whois.net today to see if “newdrachma.com” was still available. And here’s what I found:

Whois Server: whois.fabulous.com
Referral URL: http://www.fabulous.com
Status: clientDeleteProhibited
Status: clientTransferProhibited
Updated Date: 06-may-2010
Creation Date: 06-may-2010
Expiration Date: 06-may-2011

So close. Great minds think alike, he who hesitates is lost, and all that. I’m sure Zappy wouldn’t have let this opportunity slip by.

Of course, Greece isn’t the only country in trouble. So here’s a question: Would anyone like to register newpeseta.com?

As of 5:40pm DC time, it’s still available.

An Unusual Battle Between Amazon and Publishers

Over at the New Yorker, Ken Auletta has a fascinating piece about the future of publishing as the book world goes digital. Highly recommended if you a Kindle lover, an iPad enthusiast, or a Google watcher (or, like me, all three).

The article also describes an unusual battle between book publishers and Amazon about the pricing of electronic books:

Amazon had been buying many e-books from publishers for about thirteen dollars and selling them for $9.99, taking a loss on each book in order to gain market share and encourage sales of its electronic reading device, the Kindle. By the end of last year, Amazon accounted for an estimated eighty per cent of all electronic-book sales, and $9.99 seemed to be established as the price of an e-book. Publishers were panicked. David Young, the chairman and C.E.O. of Hachette Book Group USA, said, “The big concern—and it’s a massive concern—is the $9.99 pricing point. If it’s allowed to take hold in the consumer’s mind that a book is worth ten bucks, to my mind it’s game over for this business.”

As an alternative, several publishers decided to push for

an “agency model” for e-books. Under such a model, the publisher would be considered the seller, and an online vender like Amazon would act as an “agent,” in exchange for a thirty-per-cent fee.

That way, the publishers would be able to set the retail price themselves, presumably at a higher level that the $9.99 favored by Amazon.

Ponder that for a moment. Under the original system, Amazon paid the publishers $13.00 for each e-book. Under the new system, publishers would receive 70% of the retail price of an e-book. To net $13.00 per book, the publishers would thus have to set a price of about $18.50 per e-book, well above the norm for electronic books. Indeed, so far above the norm that it generally doesn’t happen:

“I’m not sure the ‘agency model’ is best,” the head of one major publishing house told me. Publishers would collect less money this way, about nine dollars a book, rather than thirteen; the unattractive tradeoff was to cede some profit in order to set a minimum price.

The publisher could also have noted a second problem with this strategy: publishers will sell fewer e-books because of the increase in retail prices.

Through keen negotiating, the publishers have thus forced Amazon to (a) pay them less per book and (b) sell fewer of their books. Not something you see everyday.

All of which yields a great topic for a microeconomics or business strategy class: Can the long-term benefit (to publishers) of higher minimum prices justify the near-term costs of lower sales and lower margins?

Web Coupons, Privacy, and Price Discrimination

Suppose you’ve got a successful business, selling your product to a diverse set of customers. Life is good. But you’d like to increase profits even more. What should you do?

One option from the MBA playbook (among many) is to think creatively about your pricing. Maybe there’s a way to distinguish your customers from each other and charge them different prices. Perhaps you can charge higher prices to some of your existing customers without driving them away or charge lower prices to folks who aren’t yet buying from you, or a combination of the two.

Businesses have myriad ways of doing this but, not surprisingly, the web has opened up new vistas. Saturday’s New York Times has an interesting article about the extent to which web coupons can be used to distinguish customers, track their behavior, and optimize marketing and pricing strategies (ht Diana):

The coupon efforts are nascent, but coupon companies say that when they get more data about how people are responding, they can make different offers to different consumers.

“Over time,” Mr. Treiber said, “we’ll be able to do much better profiling around certain I.P. addresses, to say, hey, this I.P. address is showing a proclivity for printing clothing apparel coupons and is really only responding to coupons greater than 20 percent off.”

That alarms some privacy advocates.

Companies can “offer you, perhaps, less desirable products than they offer me, or offer you the same product as they offer me but at a higher price,” said Ed Mierzwinski, consumer program director for the United States Public Interest Research Group, which has asked the Federal Trade Commission for tighter rules on online advertising. “There really have been no rules set up for this ecosystem.”

The web thus offers new ways for companies to pursue the holy grail (from their point of view) of pricing: the ability to personalize prices for each potential customer.

Needless to say, this is sometimes bad news for consumers. After all, increased information can allow firms to jack up prices to consumers that the firms believe are unlikely to stop buying.

Less appreciated, however, is the fact that this can benefit consumers as well. For example, increased information can sometimes help firms offer lower prices to select customers who wouldn’t otherwise choose to purchase.

Without further information, it’s hard to know how such creative pricing will affect consumers in the aggregate. Except that the variety of prices will increase, making more of the marketplace look like the airline industry, in which it sometimes seems as though every seat was sold for a different price.

Now Available in Dozens of Languages

Good news for international readers: Thanks to Google Translate, you can now read this blog in several dozen languages. Just click on the language you want in the box to the right.

(For those of you reading this via email, Google Reader, etc., here are some example links: German and Spanish.)

P.S. Kudos to the WordPress member who wrote the code for this.

Google’s Public Data: Much Improved

Google recently released some major improvements in its public data efforts. If you click on over to Public Data, you will find a much broader range of data sets including economic information from the OECD and World Bank, key economic statistics for the United States, and some education statistics for California. Google has also included more tools for visualizing these data, from standard line charts to the evolving bubble charts that have made Hans Rosling such a hit at TED.

As an example, I made a flash chart of state unemployment rates from 1990 to the present. Puerto Rico (which counts as a state for these purposes), Michigan, Nevada, and Rhode Island currently have the highest unemployment rates, so I thought it would be interesting to see how they stacked up against the other states over the past twenty years.

WordPress doesn’t allow me to embed Flash, but if you click on the image above and then click play, you will see the evolution of state unemployment rates over time. (Spoiler alert: All those colored bars move sharply upward toward the end of the “movie”.)

Long-time readers may recall my series of posts criticizing Google for directing its users to unemployment data that have not been seasonally adjusted. Happily, Google now allows the user to use either seasonally adjusted or non adjusted data. Two cheers for Google.

Why only two cheers rather than three? Because Google still directs unsuspecting users to unadjusted data–without the ability to switch to seasonally adjusted–if they do a Google search on “unemployment rate United States“. That’s a big deal, particularly for February 2010 when the official unemployment rate was 9.7%, but the unadjusted figure reported by Google was 10.4%.

Clearly, the two parts of Public Data need to integrate a bit more.

Google and Me, Part II

My existential crisis is over. As of last Thursday, Google is again including this blog in its search results. So, welcome to all the new readers who’ve come here after Googling information on the Eggo shortage and the debate about whether kids should get one H1N1 shot or two.

This is probably of interest only to other bloggers, but for the record: When I first started this blog, it took about six weeks for it to appear regularly in Google search results. After several months, the blog inexplicably (to me, at least) disappeared from Google’s results. As in *really* disappeared; as one friend pointed out, you couldn’t even find it if you searched for “Donald Marron blog”.  About eight weeks elapsed before it reappeared regularly in the first few pages of Google’s results.

My eight-week exile provided a nice natural experiment for evaluating Google’s importance. Not surprisingly, Google drives a good amount of traffic; readership is larger when Google knows about the blog. The more interesting impact, though, is a version of the Long Tail: with Google’s help, more posts find readers on any given day.


Netflix Avoids the Sunk Cost Fallacy

The highlight of this month’s Wired magazine is a profile of Netflix and its CEO, Reed Hastings. The theme is Netflix’s strategy to thrive even as their business model changes (e.g., as on-line streaming replaces DVDs by mail).

The opening paragraphs document an impressive willingness to change course:

It had taken the better part of a decade, but Reed Hastings was finally ready to unveil the device he thought would upend the entertainment industry. The gadget looked as unassuming as the original iPod—a sleek black box, about the size of a paperback novel, with a few jacks in back—and Hastings, CEO of Netflix, believed its impact would be just as massive. Called the Netflix Player, it would allow most of his company’s regular DVD-by-mail subscribers to stream unlimited movies and TV shows from Netflix’s library directly to their television—at no extra charge.

The potential was enormous: Although Netflix initially could offer only about 10,000 titles, Hastings planned to one day deliver the entire recorded output of Hollywood, instantly and in high definition, to any screen, anywhere. Like many tech romantics, he had harbored visions of using the Internet to route around cable companies and network programmers for years. Even back when he formed Netflix in 1997, Hastings predicted a day when he would deliver video over the Net rather than through the mail. (There was a reason he called the company Netflix and not, say, DVDs by Mail.) Now, in mid-December 2007, the launch of the player was just weeks away. Promotional ads were being shot, and internal beta testers were thrilled.

But Hastings wasn’t celebrating. Instead, he felt queasy. For weeks, he had tried to ignore the nagging doubts he had about the Netflix Player. Consumers’ living rooms were already full of gadgets—from DVD players to set-top boxes. Was a dedicated Netflix device really the best way to bring about his video-on-demand revolution? So on a Friday morning, he asked the six members of his senior management team to meet him in the amphitheater in Netflix’s Los Gatos offices, near San Jose. He leaned up against the stage and asked the unthinkable: Should he kill the player?

Three days later, at an all-company meeting in the same amphitheater, Hastings announced that there would be no Netflix Player.

In short, Reed Hastings is not a man who gets locked in by sunk costs: he’s willing to kill projects (or, in this case, spin them off) even if he’s got years invested in them. A good example for my students when we discusses costs in a few weeks. And just another example of the strengths of Netflix’s culture.

Netflix Boosts Prize Economics

By at least one metric – the number of people who have mentioned it to me – my brief post about Netflix appears to be my most popular one so far.

The post linked to a remarkable slide deck about the corporate culture that Netflix has embraced in its quest for excellence. Most memorable line: “adequate performance gets a generous severance package.” If you haven’t seen it, I encourage you to click on over. It’s worth your time.

Yesterday’s award of the first Netflix prize highlights another strength of Netflix’s culture: it clearly does not suffer from “not-invented-here” syndrome. Indeed, quite the reverse. A few years ago, Netflix realized that it had reached its limit in trying to improve the accuracy of its movie recommendation system. Even though users may rate dozens (or more) movies, it turns out to be difficult to predict what other movies they will like.

So Netflix decided to outsource this problem in an ingenious way: it offered a $1 million prize to any person or team that could improve the recommendation algorithm by at least 10%. Stated that way, the problem seems deceptively easy. But it took nearly three years before the winner – a team led by AT&T Research engineers – took home the prize.

As recounted in Netflix’s press release, this marathon ended in a race to the wire:

“We had a bona fide race right to the very end,” said [CEO Reed] Hastings. “Teams that had previously battled it out independently joined forces to surpass the 10 percent barrier. New submissions arrived fast and furious in the closing hours and the competition had more twists and turns than ‘The Crying Game,’ ‘The Usual Suspects’ and all the ‘Bourne’ movies wrapped into one.”

Netflix said “BellKor’s Pragmatic Chaos” edged out a team called “The Ensemble,” another collaboration of former competitors, with the winning submission coming just 24 minutes before the conclusion of the nearly three-year-long contest. The competition was so close and the submissions so sophisticated that it took a team of external and internal judges several weeks to validate the winner after the contest closed on July 26.

Happily, the resulting algorithm won’t be exclusive to Netflix:

The contest’s rules require the winning team to publish its methods so that businesses in many fields can benefit from the work done. The winning submission and the previously hidden ratings used to score the contest will be published at the University of California Irvine Machine Learning Repository. The team licensed its work to Netflix and is free to license it to other companies.

On the first day of my microeconomics class, I told my students that economics is all about incentives. As an example, I used the famous prize for a way to measure longitude, which inspired the invention of the chronometer (i.e., a clock of sufficient precision to measure longitude). Next time around, I will mention the Netflix prize as well.

P.S. Not one to rest on its successes, Netflix has already announced plans for a second Netflix prize. This one aims to find a better way to recommend movies to people based on demographic data (e.g., where they live) rather than movie ratings.

Google and Me

A strange this happened last week: Google misplaced my blog.

I’ve run all the usual diagnostics, and I can confirm that Google still knows that my blog exists. But it no longer appears in any of the searches – e.g., “natural gas price”, “unemployment”, “budget deficit”, or “brooke boemio” – that used to help new readers find posts on my site.

Things are so bad, in fact, that my blog doesn’t even come up when you search for “donald marron”. I feel an existential crisis coming on.

I presume this is just the result of some obscure algorithm tweak and that, over time, my posts will reappear in the ranks of the Google-worthy. But it’s fun to imagine that Google is mad at me for my posts criticizing the way it reports unemployment data.

I just checked and, no surprise, Google is still reporting the wrong data. If you search for “unemployment rate”, Google will tell you that the U.S. unemployment rate was 9.6% in August, when in fact it was 9.7%. Why the difference? Because Google is reporting an obscure measure of unemployment, not the one used by 99% of the world.

Voyaging Through U.S. Jobs

In honor of Labor Day, you may want to check out Job Voyager by Flare. It provides a graphical history of the rise and fall of different types of jobs in the United States from 1850 to 2000.

Here’s what you get for “Farmer”:

Farmer Jobs

Back in 1850, farmers accounted for more than 40% of reported jobs. Today, less than 1%.

If you click around, you will find that the decline in farmers has been offset by growth in a host of jobs, including clerical, retail, and nurses.

And economists? Well, we grew rapidly until 1990, and then tailed off. Perhaps the would-be economists ran off to Wall Street instead?

Economist Jobs

P.S. The Job Voyager charts were inspired by the famous Name Voyager charts that let you track the popularity of first names.