The culture at Netflix

Netflix CEO Reed Hastings has a very interesting presentation, "Our Freedom & Responsibility Culture", with some thought-provoking ideas on how to run a company.

Some excerpts:

Imagine if every person [you worked with] is someone you respect and learn from ... In creative work, the best are x10 better than the average ... [A] great workplace [is made] of stunning colleagues.

Responsible people thrive on freedom and are worthy of freedom ... [They are] self-motivating, [pick] up the trash lying on the floor, [and behave] like an owner ... Our model is to increase employee freedom as we grow rather than limit it ... Avoid chaos as you grow with ever more high performance people, not with rules.

Pay at the top of the market is core to high performance culture. One outstanding employee gets more done and costs less than two adequate employees ... We pay at the top of the market ... Give people big salaries ... no bonuses ... no stock options ... [and a] great health plan ... [Everyone feels] they are getting paid well relative to their other options ... Nearly all ex-employees will take a step down in comp for their next job.

We try to get rid of rules when we can .... The Netflix vacation tracking policy [is that] there is no policy or tracking. There also is no clothing policy at Netflix, but no one has come to work naked lately ... Netflix policy for expensing ... [is] five words long ... "act in Netflix's best interest" ... You don't need detailed policies for everything.

Reed also makes a great point about how to organize large companies, saying he prefers to align groups on goals and strategies while minimizing meetings over tactics. He contrasts this with "tightly-coupled monoliths" where everything is inefficiently controlled (usually from the top down) and "independent silos" where groups (e.g. engineering and marketing) work so independently that "alienation and suspicion" creep in.

There are some suggestions I disagree with. First, I think Reed's claim that Netflix should fire with "generous severance" people who managers would not "fight hard to keep at Netflix" if they were to threaten to leave conflicts with Reed's later advice that managers should not blame someone who "does something dumb" but rather ask themselves what "context [the manager] failed to set." Personally, when someone I manage is not doing well, I blame myself, not them, and I think Reed should have emphasized finding people the right challenge rather than suggesting just giving them the boot.

Second, I think Reed's advice to push new software to the website every two weeks is not nearly frequent enough -- I prefer at least daily -- and I also see this as at odds with his later claim that he wants "rapid innovation", "excellent execution", and "to be big and fast and flexible".

But, overall, a great presentation with excellent food for thought. It is a must-read for anyone thinking about how to use organizational culture to help manage a company, from little startups to bloated corporate empires.

Please see also my old 2006 post, "Management and incentives at Google", that discusses Google's corporate culture.

[Netflix slides found via Ruben Ortega, TechCrunch, and Hacking Netflix]

Update: Scott Berkun has some good thoughts on the slide deck, including nice references to Zappos' "pay to quit" idea and the "Lefferts law of management".

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Rapid innovation using online experiments

Erik Brynjolfsson and Michael Schrage at MIT Sloan Management Review have an interesting take on the value of A/B tests in their article, "The New, Faster Face of Innovation".

Some excerpts:

Technology is transforming innovation at its core, allowing companies to test new ideas at speeds -- and prices -- that were unimaginable even a decade ago. They can stick features on Web sites and tell within hours how customers respond. They can see results from in-store promotions, or efforts to boost process productivity, almost as quickly.

The result? Innovation initiatives that used to take months and megabucks to coordinate and launch can often be started in seconds for cents.

That makes innovation, the lifeblood of growth, more efficient and cheaper. Companies are able to get a much better idea of how their customers behave and what they want ... Companies will also be willing to try new things, because the price of failure is so much lower.

The article goes on to discuss Google, Wal-mart, and Amazon as examples and talk about the cultural changes necessary (such as switching to a bottom-up, data-driven organization and reducing management control) for rapid experimentation and innovation.

I am briefly quoted in the article, making the point that even failed experiments have value because failures teach us about what paths might lead to success.

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Can we make make all advertising useful, relevant, and helpful?

I have a new post at blog@CACM titled, "Is advertising inherently deceptive?"

It discusses some of the moral and ethical qualms I have when working on personalized advertising. It attempts to start a discussion around the question of whether personalized advertising will be used for good.

An excerpt:

Let's say we build more personalization techniques and tools that allow advertisers and publishers to understand people's interests and individually target ads. How will our tools be used? Will they be used to provide better information to people about useful products and services? Or will they be used for deeper and trickier forms of deception?

Is advertising an industry fundamentally fueled by deception? Or is advertising better understood as a stream of information that, if well directed, can help people?

If you have thoughts on this topic, please contribute to the discussion, either here or over on the full post at blog@CACM.

Update: About one month later, in the October 12 issue of the New Yorker, Ken Auletta has an article, "Searching for Trouble", that describes a 2003 conflict between the COO of Viacom and the founders of Google on exactly this issue, deception in advertising. An excerpt:

[You want] salesmanship, emotion, and mystery. [Viacom COO Karmazin said], "You don't want to have people know what works. When you know what works or not, you tend to charge less money than when you have this aura and you're selling this mystique."

The Google executives thought Karmazin's method manipulated emotions and cheated advertisers.

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Microsoft and Yahoo, kissing behemoths

It looks like we have a Microsoft-Yahoo deal. If you smack two amorous giants together enough times, I guess you are going to get a love child.

I doubt Google has much to fear from the laggard that likely will result. Just as two wrongs don't make a right, combining two struggling organizations is unlikely to fix dysfunction.

I hope I am wrong. If the deal provides focus rather than distractions, if it allows both organizations to rapidly iterate on, develop, and deploy products people actually want, it has some chance of succeeding.

But, as separate groups, both organizations barely can control the internal squabbling of hordes of product managers, "none f-ing getting anything done", as Carol Bartz colorfully put it. Combined, someone will have to keep these beasts from pulling on and tripping over each other while they desperately pursue the leader of the pack.

Update: Excellent commentary on the deal by Danny Sullivan, Jason Calacanis, and Saul Hansell.

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Facebook versus Google?

Greg Sterling has some good thoughts on why Facebook and Google are not in competition:

Currently the use cases for Facebook and for search are quite different.

Facebook is entertaining, Facebook is fun, Facebook kills time, Facebook enables me to keep in touch with people. But Facebook, generally speaking, is not "useful" in the sense that Google is.

For its part, Google delivers information efficiently but is generally not "entertaining" or "fun."

It's very likely that the two sites will simply co-exist fulfilling different types of needs and interests ... Neither can be expected to fundamentally undermine the core business of the other.

But what are Facebook and Google's core businesses?

It is true that the uses of Facebook and Google differ. People mostly seem to go to Facebook because they find it entertaining. People mostly go to Google because it is useful.

But, the core business of both, where they get their revenue, is from advertising. And, while Google's search advertising does quite well, they have struggled much more in non-search advertising. And non-search advertising is the problem Facebok needs to solve.

Toward the end of the Fred Vogelstein's Wired article (which Greg Sterling references), Fred pinpoints the critical area of conflict:

Facebook [is] confronted with a difficult challenge: turning [their] massive user base into a sustainable business.

[Google] inked a disastrous $900 million partnership with MySpace in 2006, a failure that taught them how hard it is to make money from social networking. And privately, [Googlers] don't think Facebook's staff has the brainpower to succeed where they have failed.

"If [Facebook] found a way to monetize all of a sudden, sure, that would be a problem," says one highly placed Google executive. "But they're not going to."

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Google's thin client distraction

Recently, Chris O'Brien at the San Jose Mercury News wrote:

It's getting harder every day to articulate what Google is. Is it a Web company? A software company? Something else entirely?

It's not just that it's hard to see how [Google's operating systems] fit into Google's stated mission. It's also that it's hard to explain to someone exactly what they are, or why they might, or might not, want to use them. Or to communicate why they are different from or better than any other things out there.

These new products have the whiff of engineers building things for other engineers, rather than you and me.

Even worse, these new products have the whiff of executives being unable to let go of their past battles.

For decades, Google CEO Eric Schmidt led Sun and Novell in mostly failed attempts to build thin client computers. At Google, Eric appears to be doing it again.

But Google is not a computer company. It is an advertising company. Google makes its money from advertising.

It is not as if there isn't enough to do in advertising. Despite Google's success in making search advertising more useful and helpful, most other advertising remains awful.

Fixing advertising not only would be lucrative, but also it directly fits into Google's mission to "organize the world's information and make it universally accessible and useful." At their best, ads provide useful information about interesting products and services. Right now, most contextual and display advertisements are more annoying than useful. It doesn't have to be that way.

If Google could be the solution to annoying advertising, it could reap all the rewards. Instead, Google is being led off by its generals to fight the last war.

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Time effects in recommendations

The best paper award at the recent KDD 2009 conference went to Yehuda Koren's "Collaborative Filtering with Temporal Dynamics" (PDF).

The paper is a great read, not only because Yehuda is part of the team currently winning the Netflix Prize, but also because it has some surprising conclusions about how to deal with changing preferences and interests over time.

In particular, it is common in recommender systems to favor recent activity, such as more recent ratings by a user, either by only using the last N data points or by weighting more recent data more heavily. But Yehuda found that ineffective on the Netflix data:

The consistent finding was that prediction quality improves as we moderate ... time decay, reaching [the] best quality when there is no delay at all. This is despite the fact that users do change their taste and rating scale over the years.

Underweighting past action loses too much signal along with the lost noise, which is detrimental given the scarcity of data per user .... We require an accurate modeling of each point in the past, which will allow us to distinguish between persistent signal that should be captured and noise that should be isolated .... for understanding the customer ... [and] modeling other customers.

As in some of Yehuda's past work, he combines two models, one a latent factor model, the other an item-item approach. The models yielded "the best results published so far" on the Netflix data set by allowing them to represent temporal effects such as finding stronger relationships between items related in a short timeframe, handling that people tend to give higher ratings to older movies (if they bother to rate them at all), allowing for people to shift to giving higher or lower ratings on average over time, and capturing that people tend to use the same rating for multiple items rated in a short timeframe.

The paper is full of other cute tidbits too, like that they tried to detect day of the week effects -- do people rate lower on Mondays? -- but could not. They also discovered an unusual jump in the average rating in the data in 2004, which they hypothesize was due to features launched on the Netflix.com site that started showing people more movies they liked. Definitely worth a read.

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