Image copyright Petr Vaclavek

Armed with mountains of data, most experts put in charge of predicting outcomes still can’t come up with accurate answers. Why? Because most people have a human bias to manipulate data to deliver the outcomes they really want to see.

Philip Tetlock, a psychologist at the University of California at Berkeley, published a book in 2005 titled “Expert Political Judgment — How Good Is It?” For the book, a team of researchers interviewed 284 political experts and extracted 80,000 predictions from them over 15 years, rating the probabilities that each event would occur (they gave each prediction three possible outcomes). The result was surprising: the experts’ predictions were worse than those made simply by assigning equal probabilities to all three outcomes. Dart-throwing monkeys could have performed just as well. It’s easy to make fun of talking heads and political pundits, but what about experts in other fields?

Another psychology professor and a past President of the American Psychological Association, Paul Meehl, has conducted similar research in other fields. In his work, he studied whether a simple algorithm could predict freshmen grades at the end of a school year better than expert college counselors could. The algorithms were only given SAT scores and grades. The expert counselors, on the other hand, were given the same data as well as a 45-minute interview and a personal essay. Again the simple algorithm won. In yet another example of experts versus algorithms, Paul’s work pitted algorithms predicting the future price of a bottle of wine against connoisseurs. In this case, the wine predicting algorithm had access only to three weather variables while the wine connoisseur, on the other hand, had all of the data and the ability to taste the wine. By now it should be obvious the algorithms beat the connoisseurs when it came to predicting the wine’s price. Paul’s analysis didn’t stop there. It extended to medical experts, flight trainers, parole officers, bankers and many others. In all the cases, the algorithms won. So where did all these experts go wrong? There were several reasons — but we will focus on just one here.

The biggest bias experts have is they tend to value perceived causes or stories over the underlying data. Storytelling is a powerful way to inject order into chaos, but reality is often far more complex than a simplified story. Whenever data supports their stories, they promote the data, but when data contradicts their stories, they question or ignore the data. Experts become really good at “finding the data” to promote their case.

What does all this have to do with marketers? A marketer’s job is to tell a story. Any expert will have a tendency to manipulate data to get it to conform to his or her story. Marketers are susceptible to the same sins, except they’re even more prone to storytelling. There is also a terrible feedback loop at work here. There may be good marketers who don’t want to torture data to fit into a clean story, but these marketers couldn’t make the bold claims that end up as headlines. So in short, marketers are some of the worst “experts” at predicting specific outcomes because their job biases them to storytelling and rewards them for making predictions that end up in the news.

To better understand why marketers tend to ignore data, let’s look at one possible scenario.

Imagine a Father’s Day ad that’s unfortunately misplaced next to a news article with the headline “One in Four Women Victims of Domestic Violence.” As a marketer, you are presented with the following two formulations about the poorly placed ad and you want to find out which publisher you should blame:

Problem Description A
85 percent of the impressions in the Father’s Day ad campaign are served in publisher A, and 15 percent are served in publisher B. Ad verification software says the ad was found in a bad context on publisher B, and the verification software is right 80 percent of the time.

When asked, most marketers choose publisher B as the most likely cause of the problem. But let’s try an alternate formulation of the problem:

Problem Description B
Publisher A and publisher B served the same number of ad impressions.
We learn that 85 percent of ads served in publisher A are served into inappropriate content. Ad verification software says the ad was found in a bad context on publisher B. The verification software is right 80 percent of the time.

Now which publisher do you think caused the problem? Most marketers choose publisher A as the culprit.

It turns out both formulations of the problem result in the same probabilistic answer — that is, 61 percent probability that publisher A caused the problem, according to Bayes rule in odds form. Most marketers get the first version wrong and the second version right, even though it’s the same math problem! Why do most marketers get the second version right? In the first story, how you use the fact that 85 percent of impressions are served in publisher A is not obvious. In the second story, you have a cause. Publisher A is, for all practical purposes, almost always inappropriate. Marketers prefer stories that point to an understandable cause over statistics. Marketers will hone in on a story and ignore the rest of the data, no matter how big the data set.

Think of the many problems marketers have that involve some form of prediction. Who is the target audience for this product or service? Why do they need, like or want the product or service? Where can we find this audience? What creative will succeed with this audience? We even have a whole industry built around the RFP (request for proposal), which itself is a form of a story. The RFP mechanism guides billions of dollars of ad spending, and ad allocation decisions are forms of predictions.

So can marketers learn to overcome their biases and use data to make more accurate predictions? It may well be possible.

In his book, “The Signal and the Noise, ” Nate Silver tells a great story about how the PECOTA system he built to predict pitcher performance in baseball was beat by expert scouts. In 2006, Nate’s program PECOTA produced a list of 100 prospects from the minor leagues. He then pitted that list against the Baseball America Top 100 prospects list, which was constructed by scouts. After watching six years of actual performance, and worrying their jobs would disappear due to PECOTA, the scouts performed better than the PECOTA list by a good margin. It turns out the scouts had learned to tone down their own biases and instead “listen” to the data. They learned they could combine their expertise with the reams of public data available.

Marketers can learn to separate the signal from the noise. Marketers are dedicated storytellers who believe their own stories, downplay luck and impose cause on randomness. In the world of Big Data, marketers have to learn to to let the data speak for itself. Otherwise, all the big data in the world won’t help marketers understand their customers better.

Omar is the co-founder and CEO of BlueKai, the data activation system that supplies both Fortune 100 companies and leading publishers with solutions for managing and activating first- and third-party data for creating highly effective customer and marketing campaigns. Omar’s previous roles include Chief Advertising Officer for mobile search and advertising solution Medio and Chief Marketing Officer for early behavioral data leader Revenue Science.

Image via vichie81

At Rocket Fuel, we’re big believers in the power of algorithms. This is because data, no matter how rich or augmented, is still a mostly static representation of customer interest and intent. To use data in the traditional way for Web advertising, choosing whom to show ads on the basis of the specific data segments they may be in represents one very simple choice of algorithm. But there are many others that can be strategically applied to take advantage of specific opportunities in the market, like a sudden burst of relevant ad inventory or a sudden increase in competition for consumers in a particular data segment. The algorithms can react to the changing usefulness of data, such as data that indicates interest in a specific time-sensitive event that is now past. They can also take advantage of ephemeral data not tied to individual behavior in any long-term way, such as the time of day or the context in which the person is browsing.

So while the world of data is rich, and algorithms can extend those data assets even further, the use of that data can be even more interesting and challenging, requiring extremely clever algorithms that result in significant, measurable improvements in campaign performance. Very few of these performance improvements are attributable solely to the use of more data.

For the sake of illustration, imagine you want to marry someone who will help you produce tall, healthy children. You are sequentially presented with suitors whom you have to either marry, or reject forever. Let’s say you start with only being able to look at the suitor’s height, and your simple algorithm is to “marry the first person who is over six feet tall.” How can we improve on these results? Using the “more data” strategy, we could also look at how strong they are, and set a threshold for that. Alternatively, we could use the same data but improve the algorithm: “Measure the height of the first third of the people I see, and marry the next person who is taller than all of them.” This algorithm improvement has a good chance of delivering a better result than just using more data with a simple algorithm.

Choosing opportunities to show online advertising to consumers is very much like that example, except that we’re picking millions of “suitors” each day for each advertiser, out of tens of billions of opportunities. As with the marriage challenge, we find it is most valuable to make improvements to the algorithms to help us make real-time decisions that grow increasingly optimal with each campaign.

There’s yet another dimension not covered in Omar’s article: the speed of the algorithms and data access, and the capacity of the infrastructure on which they run. The provider you work with needs to be able to make more decisions, faster, than any other players in this space. Doing that calls for a huge investment in hardware and software improvements at all layers of the stack. These investments are in some ways orthogonal to Omar’s original question: they simultaneously help optimize the performance of the algorithms, and they ensure the ability to store and process massive amounts of data.

In short, if I were told I had to either give up all the third-party data I might use, or give up my use of algorithms, I would give up the data in a heartbeat. There is plenty of relevant data captured through the passive activity of consumers interacting with Web advertising — more than enough to drive great performance for the vast majority of clients.

Mark Torrance is CTO of Rocket Fuel, which provides artificial-intelligence advertising solutions.

Chris Dixon will indeed be the latest VC to join Andreessen Horowitz, as I had reported earlier was likely, starting in January.

Dixon is a lively and interesting choice for the high-profile Silicon Valley venture capital firm, having been a successful serial entrepreneur, a savvy angel investor and also a voluble tech scene commenter in blogs and on Twitter.

Perhaps most interesting is that Dixon, who is largely based in New York, will move to California to join what Marc Andreessen describes as a “single-office” firm.

In other words, Chris, get ready to be force-fed at Hobee’s!

In all seriousness, Andreessen said in an interview today that his big criteria for picking Dixon was because, “when the next Mark Zuckerberg walks into the room, we want them to say, ‘I want that person.’”

In addition, he noted that Andreessen Horowitz has a “bias for entrepreneurs … and Chris is the real deal when it comes to being an entrepreneur.”

Indeed, Dixon was CEO and co-founder of SiteAdvisor, which was acquired by McAfee, as well as recommendations engine Hunch, which was bought by eBay a year ago.

He is one of the founding members of Founder Collective, an East Coast-based seed-stage venture firm run by entrepreneurs, and is also an active angel investor, including in Skype, Invite Media and OMGPOP. Previously, he programmed financial algorithms at a high-speed options trading firm and has also worked at Bessemer Venture Partners.

Dixon, whose job at Bessemer was junior, said that he became interested in Andreessen Horowitz once he learned more about them after helping the start-ups he had invested in with fundings. He was especially struck by all the added help the firm provided in areas such as recruiting.

While he said his investing as a partner there would be “non-thematic,” he noted that it was likely he would “tilt more toward consumer” investments.

“I am going to try to pick the special bird,” he said, paraphrasing a quote by famed Sequoia Capital VC Mike Moritz. “And not the flock.”

Here is a blog post on the move by Dixon and one by Andreessen, in which he apparently thinks I leak for a living.

According to sources, well-known New York techie Chris Dixon is in the final stages of discussions to become a partner at the high-profile Silicon Valley venture firm Andreessen Horowitz.

While the deal is not yet complete, Dixon seems likely to become the firm’s latest addition. It’s not clear what his area of specialty will be, but it is likely to center on start-ups, given his experience.

Dixon is one the more voluble and energetic tech players on the New York scene, as well as a serial entrepreneur with several prominent exits. He was CEO and co-founder of SiteAdvisor, which was acquired by McAfee, as well as recommendations engine Hunch, which was bought by eBay a year ago.

He is one of the founding members of Founder Collective, an East Coast-based seed-stage venture firm run by entrepreneurs and is also an active angel investor, including in Skype, Invite Media and OMGPOP. Previous to this, he also programmed financial algorithms at a high-speed options trading firm and has also worked at Bessemer Venture Partners.

In his bio on the Founder Collective site, Dixon said: “I think one of the reasons I can be helpful to entrepreneurs is I’ve worked in the trenches on both the startup side and the investor side.”

His selection by Andreessen Horowitz is an interesting one, since it is the first by the firm of a solidly New York-area techie, although it is not clear if Dixon will move to California or not.

He’s also not been afraid to tangle with VCs in his frequent blogging. In one post in July, titled “Shoehorning Startups into the VC Model,” he noted:

“A startup should raise venture capital (or ‘venture-style’ angel/seed funding) only if: 1) the goal is to build a billion-dollar (valuation) company, and 2) raising millions of dollars is absolutely necessary or will significantly accelerate growth. … Unfortunately, many of these startups graft VC-friendly narratives onto their plans and raise too much money. Short term it might seem like a good idea but long term it won’t.”

Demand Media, the social media and content company, strongly bested Wall Street expectations for the third quarter.

Revenue for the quarter was $98.1 million, up 20 percent from the same period a year ago, and well above the $91.4 million expected. Earnings per share were four cents, compared to a net loss of five cents last year.

This is the fifth straight revenue increase for the Santa Monica, Calif.-based Demand, which had struggled last year as Google changed its algorithms and impacted many publishers across the Web.

Shares of Demand, which rose just above six percent today, were up another 6.4 percent in after-hours trading to $9.30 on the news.

Still, here are some interesting stats to note:

Page views on its owned-and-operated sites, such as eHow, were up 33 percent, although revenue per thousand page views was down 11 percent. It’s due to lower revenue from mobile growth.

Demand’s domain registrar business was also up 11 percent, although revenue was down 2 percent.

Here’s the whole release to peruse before the company’s 2 pm PT call with analysts — I will add more if Demand execs say something juicy:

DMD 9 30 2012 EX 99 1 Earnings ReleaseFINALBUSINESSWIRE.pdf

Why is this happening now? Smartphones, which will make up nearly half of mobile phones in the U.S. by the end of 2012, enable “app-cessories” like the larklife or the Fitbit to do a lot with a little: The phone provides processing power, display and cloud connectivity. Smartphones like the iPhone 5 and many Android phones, as well as all Windows 8 devices, have native support for low-energy Bluetooth (they’re “Bluetooth Smart Ready”), which allows sensor-laden devices to transmit data to the phone using very little battery. It’s not just the hardware that’s evolving, it’s also the software: Programmers are getting more sophisticated at creating algorithms that interpret the sensor-collected data and deliver advice. For example, start-ups Lark Technologies, Live!y, and Wallet.AI all consult with cognitive science experts to create products that will actually change people’s behavior.

Consumers seem receptive when there’s a clear benefit that they get in exchange for their data. Progressive Casualty Insurance, for example, has more than a million users of its Snapshot device, which tracks real-time driving behavior (acceleration, breaking, mileage, time of day) and adjusts consumers’ auto insurance policies based on their actual risk; on average, users save $150 per year on their policies, according to the company. BodyMedia reports that its Fit armbands, which track 5,000 data points per minute via four sensors, have an average length of use of six months and climbing, and are used not only for losing weight but also for managing chronic diseases like diabetes.

Moving sensor-laden devices from niche to mainstream requires solving data-related problems, such as unlocking and sharing data from multiple sources, including hard-to-get data from healthcare providers. Products also need to improve data reliability: For example, Nike+ FuelBand users have noted that they get more “fuel” points for arm-intensive (but not fitness-inducing) activities such as eating pizza than they do for walking up a flight of stairs. Add to that the privacy and security challenges of managing personal data. Putting users in control of their own data will be a key success factor for any Smart Body, Smart World product. Consumers may enjoy sharing their activities with their social networks (or subsets of their social networks) some of the time, but also will want modes that act more like private journals than public broadcasts.

Smart Body, Smart World products will reorder the value chain of consumer electronics. The primary beneficiaries will be the algorithm owners; hardware design is important, but the core IP is in the algorithms that interpret the sensor-collected data and deliver advice. The platform owners, namely Apple and Google, will be the secondary beneficiaries; Google is developing first-party hardware (Google Glass and, reportedly, an Android smartwatch) while Apple is cultivating an ecosystem of apps and accessories. Carriers will accrue value through increased use of smartphones via sensor-laden accessories, and to a lesser extent via standalone device connections. Retailers also stand to benefit from smart product growth — but not traditional consumer electronics retailers. Sensor-laden baby mattresses would be an easier sell at Diapers.com or your local baby goods store than at Best Buy — consumers will look for these devices at the retailers that solve the greater lifestyle challenge, not the retailers that specialize in electronics.

It’s hard to overstate how different out lives will be when the Smart Body, Smart World paradigm is in full force. For consumers, the effects are likely to be both positive and negative: With more information and guidance, humans can make more “rational” decisions, but if we are always listening to our devices, we may experience less spontaneity, too. And what happens to our nervous systems when devices surround us, even when we sleep? Those questions are beyond the purview of the type of strategy research we do at Forrester, but answering them will be crucial to living in a Smart Body, Smart World paradigm.

Sarah Rotman Epps is a Senior Analyst serving Consumer Product Strategy professionals at Forrester Research. To learn more about this research, visit the full report here.

Binary code illustration by Matej Pavla

According to this line of thinking, Google proved this same lesson years ago when it showed that PageRank could outperform keyword extraction techniques (used by other search engines at that time) by leveraging data from outside the page itself (i.e., the votes that page creators made by choosing their outbound links, which defined the network topology of the Web). History is repeating itself now with Facebook, which is using detailed data about friendships (which defines the social network topology of the real world) to give it a leg up over other media companies. It is this same underlying notion that led Alex Rampell, the CEO of TrialPay, to say, “Payment data is more valuable than payment fees.” According to Alex, “Connecting the bank accounts of buyers and sellers will never be as valuable, nor defensible, as connecting buyers and sellers.”

Is this love of more data really well-founded? When do you know enough? In a world in which the amount of existing data is doubling every year, when do you shift your focus from yet another incremental attribute to writing a better algorithm to help you handle the deluge? How do you avoid being overwhelmed by the noise? Is there a tipping point at which more is less?

To better illustrate the problem, we need only remember Solomon Shereshevsky — a man with an unusual mind, who remembered in great detail everything that happened to him. His problem was that even though he remembered every detail, his brain failed to create high-level connections between the details. For example, if he saw a face, he remembered it exactly, but he failed to connect that memory to a memory of the same face with a different expression. Therefore, he had trouble connecting these faces with the person they belonged to. Similarly, when you spoke to him, he could memorize every word you said, but he would have trouble understanding your point. (A.R. Luria, “The Mind of a Mnemonist: A Little Book about a Vast Memory.”)

A healthy mind, on the other hand, has a more successful strategy for dealing with what could otherwise be data deluge. According to Leonard Mlodinow, the author of “Subliminal,” our brain processes 11 million inputs a second. It does so gracefully, because when we experience something in the real world, we aren’t just adding an isolated memory into our minds. The brain quickly connects the salient features of the memory to an entire lifetime of connected memories, including images, smells, sounds, touch and emotions. Think about how many times a smell has helped you retrieve a memory about an event. A simple fact can be transformed instantly just by being associated with other facts. That is why someone who records every fact but fails to extract the meaningful connections is at a severe disadvantage.

This leads us to our central point. Algorithms shouldn’t be one-way filters that take data out and put them to use outside of the system. Rather, the algorithm output is itself data which enhances the data asset. Even though BlueKai processes one trillion data transactions a month, we believe that the real value isn’t in the raw volume, it is in the degree of connectedness that is analytically overlaid onto the data to make it more interrelated. For example, the addition of data on sport water bottle purchase intent doesn’t just enhance the water bottle category — that might be rather uninteresting. By analyzing the behavior, for instance, of people who purchase water bottles for biking, we learn that these same people tend to own high-end vehicles. Apparently, people who like biking for sport tend to have the drive and money to enjoy a more expensive vehicle. These bicyclists also tend to take more island vacations, and, not surprisingly, so do their friends. Therefore, an isolated behavior, when evaluated and connected, can produce unexpected value.

This brings us back to the original question. If you have to choose, having more data does indeed trump a better algorithm. However, what is better than just having more data on its own is also having an algorithm that annotates the data with new linkages and statistics which alter the underlying data asset. That way, the addition of each new algorithm radically improves the underlying data asset, just like the addition of a sensory input improves the way we experience the world around us.

Are you living in a world in which more data provides diminishing returns (like Solomon Shereshevsky), or are you living in a world in which more data truly is better?

Omar is the co-founder and CEO of BlueKai, the industry’s leading data activation system that supplies both Fortune 100 companies and leading publishers with solutions for managing and activating first- and third-party data for creating highly effective customer and marketing campaigns. Omar’s previous roles include Chief Advertising Officer for mobile search and advertising solution Medio and Chief Marketing Officer for early behavioral data leader Revenue Science.

Although the circumstances of his death — considered a suicide, due to persecution over his being gay, although that conclusion has recently been disputed — were tragic, there is no question that computing owes a great deal to Alan Turing.

Britain’s math whiz and famed codebreaker would have celebrated his 100 birthday today. His theories that helped crack Nazi Germany’s Enigma code in World War II might be enough, but Turing pushed the boundaries on machine intelligence and algorithms to levels that changed technology.

The development of the modern computer — including work on the stored program concept — was born from many of his key insights.

No surprise, the doodle today on Google — a company that knows a thing or two about algorithms — today is in tribute to him, a version of Turing’s hypothetical computing machine.

Here it is:

And, if you want to learn more about this tech legend, visit the Web site of Bletchley Park, the location of the secret British codebreaking activities during WWII. Turing worked there, along with 10,000 others — including 5,000 women.

Here, in keeping with the Ten Things tradition of sharing more than 10 things, are 11 things about Rosenblatt:

What qualities do you like in a person?
I admire people who are curious, innovative, respectful to everyone and eternally optimistic. I firmly believe that the best days are in front of us and we must always try new things while learning from others, in order to continually grow as people.

What qualities do you dislike?
People who are constantly negative rather than being constructive, or those who are jealous of another’s success. Being positive is a state of mind and all about how you perceive a situation — is it a permanent fall or a chance to learn and get back up? I’m also disillusioned by how negatively people react to another’s success when it’s not a zero sum game.

What’s the single most important issue in the world today?
Education. No question. Whether it’s formal or informal. There is unlimited knowledge available via the Internet, and we need to find an effective way to spread this knowledge globally to people who can benefit from it.

Do you still buy CDs or rent DVDs?
No, I download all media on my iPad or stream it through services such as Spotify or Netflix.

What would you be doing if you were not in your current job?
I’d be looking for the next opportunity to innovate in the online media landscape and increase my lecturing opportunities at UCLA and USC.

What is your greatest achievement to date?
Raising three respectful kids who are curious, innovative and optimistic.

iPhone, Android or BlackBerry?
All iPhone all the time.

What site/app do you check first when you wake up?
Twitter to get some news and tweet some info. Then eHow.com, Livestrong.com and Cracked.com to see what’s new and exciting on our properties. And then I’m tweeting again.

What was the last thing you fixed?
Boy, that’s a tough one. I’d say (and my wife would punctuate the point) that “handiness” is not one of my strengths. But, I inflated a flat tire on my son’s bike last weekend, and, as of now, it’s holding air.

If you could have any superpower, what would you choose?
Freeze time, so we would all have more time in the day!

Name your favorite guilty pleasure.
Wine, paddle tennis and pizza (without cheese). The perfect day would be enjoying all three at once!

Earlier today, Demand Media turned a Q1 performance that beat expectations and also raised its outlook for the year ahead.

Revenue was $82.9 million, up 9 percent, compared to an estimate of $79.6 million. Earnings per share, adjusted for one-time costs, were seven cents, above the expected five cents.

You can read it all here in these lovely Demand slides:

Demand_Media_Q1 Supplemental Data FINAL

Not bad, given many investors have been down on Demand, for a variety of reasons, mostly centered on traffic pressure due to search algorithm changes on the all-important Google. The low stock price resulted in a deal, in which Demand recently almost went private.

So what does CEO Richard Rosenblatt have to say for himself about all this? Here’s a liveblog of the conference call with Wall Street analysts, taking place at 2 pm PT.

Earlier:

2:05 pm: The call started a little late, but Rosenblatt was quick at it, noting the news was a lot better than was thought.

Always a jaunty dude, he was jauntier than usual, if possible, throwing in a lot of terms like “growth,” “momentum” and “Those fiddling geeks from Google did not kill us, after all!” Okay, not the last one.

It was a strong performance, indeed, which is a cause for happiness, I suppose, and especially since Demand’s stock has been in the doldrums since it went public earlier this year.

Not Groupon bad, but that’s a moon crater.

“It was a strong performance,” said Rosenblatt. He gets to say that.

More on the specifics, which showed growth in pages views, engagement and such things on its flagship sites, such as Cracked and LiveStrong. Its juggernaut, eHow, is still recovering from the Google search changes, in an effort deceptively called Panda.

This was not a cuddly panda, but Panda Gone Wild.

2:18 pm: The CFO guy who just repeats the numbers in the press release. (This is the part where I go to the bathroom.)

2:28 pm: Back! Sorry I took so long, but I was getting a computer science degree. (You knew that Yahoo joke was coming!)

The CFO is still chitchatting, still positive about the year ahead and saying “growth” a lot.

2:30 pm: Time for Q&A!

The first is about the mobile arena and monetization there.

Important, but no one is anywhere on this, due to the fact that it does not monetize very well at this point in time.

Everyone has a smartphone now, but the advertising part of it is still a wee baby.

“The monetization will come later,” said Rosenblatt, pointing out the obvious, although he did note that Demand was focusing in it.

Whee! Get on it, Chief of Revenue Joanne Bradford (I know you work hard, but work harder)!

Now to video. More monetizable, obviously, and Demand has a lot of videos to offer, being one of the bigger content providers to YouTube.

I am now officially tired of this question. Next!

2:35 pm: Ooh, a future and direction question about eHow.

“How should we think about its utility in the world?,” asks the analyst.

You should think about it as a place to learn to boil an egg, which is what Rosenblatt mentions. Who doesn’t want to know how to boil an egg? And who doesn’t forget how long to do it, resulting in rubber balls or drippy messes?

These are the great queries of our day, people!

The next question is about “bullish” YouTube upfront last week, in which Google pretended to go Hollywood.

Google will never go Hollywood, by the way, since it requires charm and glamour.

Demand was not there, but it is all up on the YouTube channel thing.

And, why not, since Google is giving away dough to become Hollywood. (Not … gonna … happen … ever, but take the money, all you celebs!)

2:40 pm: Next question is about mobile, again. Quantify that, and when is it going to be big?

No one can say yet, but why not ask!

Still, Rosenblatt makes a college try (I wonder if he has a CS degree?), noting it is “happening fast.”

Another question on YouTube channels and then on registar business.

Rosenblatt and Google are apparently going to see if they can make a go of this thing! Like kids putting on a show in the barn! Let’s see if we can make something of this dang talented group of kids!

The questions never end. Wait, the analyst asks about All Things Digital and my story on their private equity deal!

I can check another one off my bucket list: Being mentioned on a Demand Media earnings call!

Rosenblatt cannot say much, but notes, “We have a duty as a public company to look at anything that comes across.”

Which means it’s true, though he might have taken a moment to compliment my scooptastic skills.

Sigh.

2:51 pm: I am bereft by the slight, but shall recover.

And now it is over, so it is back to boiling my egg.

According to sources close to the situation, Demand Media was deep into discussions with a private equity firm to complete a deal that would have taken the online content company private, nearing a price that was double its current value.

But Demand abandoned the effort this past week — which was born from an aggressive attempt by Boston-based Thomas H. Lee Partners to purchase the company for a price of up to $1.2 billion. That was due to a number of challenges, including complications related to its financing and the ability to retain executives in its aftermath.

The move on Demand by private investors is perhaps no surprise, and is part of a wider trend related to some Internet companies whose stocks have a depressed value relative to the worth of their assets.

Among companies having been and also being evaluated by private equity firms, whose business it is to turned the undervalued into a goldmine: Yahoo and AOL.

And also Demand, which is now worth only $605 million, a market cap that is off 65 percent since it went public in February 2011. Shares now trade at $7.25 each.

That depressed share price has been due to a number of issues, most especially changes to Google’s search algorithm to improve results. Called Panda, the changes at the search giant — a critical partner of Demand’s — has cut traffic to its major content sites and also called into question its ability to monetize its scaled editorial efforts.

Such a situation is nearly irresistible to PE firms — in this case, Lee, which approached Demand.

Several sources said that the board threw out a hefty number that it assumed would shut down any interest and the pair began talking with an initial offer to take the company private at $11.28 a share.

That equals close to $1 billion for Demand, which also has more than $100 million in cash. But sources said Lee and Demand also discussed the addition of a large loan as part of the ongoing discussions, for possible acquisitions related to a content roll-up strategy it had, which would bring the total up to $1.2 billion.

One source underscored that the board of the Santa Monica, Calif., company had no interest or intention to sell the business, but that the premium was large enough that it engaged.

The deal from Lee, which also included a strategy of splitting up the content arm from Demand’s lucrative domain-registar business.

There were also large cash-out provisions for major shareholders, as well as for CEO and co-founder Richard Rosenblatt.

Thus, the two sides engaged intensely in the last several weeks in crafting an agreement, although the devil would prove to be in the details.

One big issue is that taking Demand private was still a big financial commitment for Lee — which tried to engage some of its limited partners in the transaction — as well as other investors, including Silicon Valley’s Marc Andreessen.

That proved harder than Lee thought, said sources, with some balking at the firm’s ability to make a big enough score on the possible turnaround.

“It was hoped it would be a Skype situation, but there were worries,” said one source, referring to the blockbuster sale of the Internet telephony company by private investors to Microsoft for $8.5 billion last year. That deal was widely considered a PE home run, given the excessive premium paid for it.

Demand’s challenges increasingly worried the firm as it moved forward, sources said, causing it to reevaluate its earlier bid several times.

Also a worry: Retaining major talent, including Rosenblatt and others, after they sold large chunks of their equity.

After Lee asked for more time to complete the financing, Demand ended the talks last week.

Another source, as is typical in these endings, said it was the Lee that walked away (who knows and, really, who cares — both sides were engaged seriously).

One thing was true: “Demand was definitely at the altar, but it did not get to the vows,” said one source.

Another source noted that the board also determined that Demand’s situation was improving, and that new trends are showing that the bottom might be been reached. The company reports its first-quarter earnings on May 8, which is expected to show some traction related to its many challenges.

“There is nothing Lee could do that Demand could not do for itself,” said one person. “So throwing in the towel seemed premature for now.”

Lee declined to comment, as did Demand.

Here are the actual 10 patents that Facebook is using in its just-filed counter-claim against Yahoo’s patent infringement lawsuit.

Several are held by Facebook CEO and co-founder Mark Zuckerberg, as well as other employees of the social networking giant, in areas such as personalized news feeds, tagging digital media and headline posting algorithms.

The patent ball, as irritating lawyers say, is in Yahoo’s court.

Enjoy:

Exhibit A – US7827208

Exhibit B – US7945653

Exhibit C – US6288717

Exhibit D – US6216133

Exhibit E – US6411949

Exhibit F – US6236978

Exhibit G – US7603331

Exhibit H – US8103611

Exhibit I – US8005896

Exhibit J- US8150913

Prabhakar Raghavan, the well-respected head of Yahoo’s Labs unit and also recently its head of strategy, is leaving the company to take a job at Google.

The departure comes ahead of what will be very deep cuts in his division, which is in charge of long-term research at the Silicon Valley Internet giant, said sources, and is spread all over the country. More researchers at Yahoo — which is a very well-respected group — are also expected to go too and will be the subject of fervent recruiting interest by companies such as Google and Facebook.

Yahoo confirmed the move after I made an inquiry about it this morning.

In a statement, the company said:

“Yahoo! thanks Prabhakar Raghavan for his dedication and contributions to Yahoo! for the past 7 years. We wish him well in his next endeavor. Ash Munshi, CTO, will assume leadership for Y! Labs.”

It is unclear what Raghavan’s new role at Google is.

But, as head of Yahoo Labs, Raghavan’s research arena has been extensive, encompassing everything from data mining to algorithms to search.

He is also a consulting professor of computer science at Stanford University. According to his bio, Raghavan has “co-authored two textbooks, on randomized algorithms and on information retrieval.”

The Berkeley PhD had been CTO at Verity and had held a number of jobs at IBM Research.

More to the point, he was very well respected within the company, which seems to be curtailing its commitment to research as it attempts to turn itself around under the new leadership of CEO Scott Thompson.

Raghavan had been made head of strategy under former CEO Carol Bartz, who was fired.

He has been at Yahoo seven years.

A little more than a week ago, Morgan Stanley kneecapped Demand Media’s stock by downgrading the Santa Monica, Calif., online content company’s prospects.

Some worries: Pressure from algorithm-tweaking Google, upon which Demand relies heavily for traffic; worrisome drops in said traffic due to said tweaking; and not enough uptake on higher-quality, premium stories the company had promised in the fall.

Demand shares fell 6.5 percent that day, to close at $5.93.

It has recovered a bit to $6.13 today, but is still off almost 8 percent since the beginning of the year, and 71 percent from a year ago.

One irony to the tough call by Scott Devitt, of course, was that Morgan Stanley was the investment bank that had taken Demand public in January of 2011, at $17 a share.

In addition, the recent departure of three of the company’s six founders has also worried investors.

All this will presumably be on display today when Demand reports its fourth-quarter earnings. Wall Street analysts are expecting Demand to earn seven cents a share on an adjusted basis on an expected $81.95 million in revenue.

We’ll see after the market closes, as well as what its execs have to say about the key metrics of costs and traffic.

“Advertising is based on one thing: Happiness. And you know what happiness is? Happiness is the smell of a new car. It’s freedom from fear. It’s a billboard on the side of the road that screams with reassurance that whatever you’re doing … It’s okay. You are okay.”

Don Draper, “Mad Men,” Season 1, “Smoke Gets In Your Eyes”

I wonder what Don Draper would think today when the 23-year-old digital media buying whiz quips back, “Maybe, but let’s load it up into the system, along with 5,000 other versions of copy, and measure how many Facebook ‘Likes’ it drives within our target demo.”

I love the “Mad Men” version of the ad business. The storytelling. The simplicity. The glasses of scotch at 10 am. But these days in digital, it feels like the Math Men media buyers (with their terabytes of data) are taking over for the Mad Men creatives. It may not make for great TV drama, but they’ve got the performance data to prove that it’s their turn in the driver’s seat.

For years, digital ads were bought and sold by young media buyers from ad agencies and smooth salesmen from online publishers and networks, sealed over the modern version of the “three-martini lunch.” But with the steady advancement in online advertising technology over the last ten years, the geeks — I mean the Math Men — have gained the upper hand in determining how to spend these digital marketing dollars. Today, ad buying and selling is automated across nearly every digital channel, driven by complex algorithms crunching terabytes of data, all employed to meet rigorous ROI objectives — typically measured by new customer acquisition, profit margin, or revenues.

It all started in search, where Overture introduced (and Google perfected) a keyword ad marketplace for search pages. We take that marketer proposition for granted now, but it was heretical at the time — only pay us when a user clicks on your ad (versus every time we show your ad), and you decide how much to pay for that click (versus the same price for every advertiser). And sophisticated marketers took full advantage by leveraging technology platforms from Math Men companies like Efficient Frontier to maximize the efficiency of their search ad spend across millions of keywords, bids and text ad copy.

Since then, several major advances in advertising technology have further enabled the Math Men:

• Six years ago, Right Media introduced the first ad exchange for display ads, enabling the Math Men and their algorithms to buy and sell banner ads and skyscrapers across the Web. Google subsequently perfected the display exchange via their DoubleClick acquisition as well.
• Three years ago, Blue Kai introduced the first ad targeting-data marketplace, enabling the Math Men to leverage anonymous audience targeting data to further enhance marketers’ campaign performance.
• A year ago, Facebook launched its own ad platform API to enable Math Men and their algorithms to bid for Facebook ads based on user attributes. It seems likely that Facebook will eventually extend its monetization platform to third-party publishers, similar to what Google did with AdSense, as Facebook already has a strong distribution foothold via Facebook Connect.

It feels like we are witnessing the tipping point in digital media buying. Measured by dollars or by impressions, greater than 50 percent of online advertising is bought via APIs today (granted, most of this is still search). In a few years, I believe that 90 percent of all digital ad impressions, and more than 75 percent of digital ad dollars, will be bought and sold programmatically.

As we witnessed with search marketing, once a) marketers get a taste of the increased spend efficiency offered by these emerging platforms, and b) these platforms (and the associated marketer tools) become sufficiently easy to use, the dollars will flow, and quickly. The Math Men at Efficient Frontier are leveraging these display, data and social platforms to deliver superior ad spend performance for marketers across all digital channels today. It’s no longer just about search.

And the Mad Men are taking note. In the last few years, the ad agency holding companies have rolled out their own technology-driven digital ad “trading desks” to help their clients take advantage of these ad trading platforms. I wonder if they’ve replaced the scotch in the mini bars with the Math Men’s drink of choice, Red Bull.

Chris Moore is a partner with Redpoint Ventures and has been enabling the digital Math Men with investments in Efficient Frontier, Right Media, Blue Kai, Auditude, Inadco, Extole, Intent Media and eBureau. Follow him on Twitter @Redpointvc and @Moorski.

Two of the more interesting online media properties are apparently joining up for a year-end online idea festival.

The AOL-owned Huffington Post, and TED, the massive conference organization and online site dedicated to its offerings, will jointly feature 18 of the best onstage speeches from TED’s excellent year-round global events.

The idea-thon will be called “Best of TED 2011: A Countdown of 18 Groundbreaking Ideas to Reshape the World in 2012.” A post on the Huffington Post site noted that it will feature the popular TEDTalks and combine them with “new blog posts written by the people who delivered them, examining how their ideas were impacted by being shared with a global audience.”

Out of a total of 300 possible choices, the number of speeches has been narrowed down to 18, because TEDTalks are limited to no more than 18 minutes. The talks range over a wide array of topic areas, including science, art, music, tech and more.

In an interview today, HuffPost majordomo Arianna Huffington said that the aim was to spur thinking around big problems the world faces.

“We are trying to be people to rethink everything in a super engaging way,” she said. “That is what TED is famous for and we wanted to shed a lot of light on it.”

Here’s today’s, with game developer Kevin Slavin on “How Algorithms Shape Our World”:

The application, called Shopycat, makes product recommendations based on the items people have liked or talked about in their news feed.

It is not particularly flashy. The logo is a picture of a cat sticking its head out of a shopping bag. The tagline reads “the right gift every time.” But the app’s rudimentary design fits nicely with the experimental nature of social commerce. Retailers are just starting to figure out ways to leverage social networks. Wal-Mart is no exception.

Wal-Mart promises that while it looks simple, the technology running behind the scenes is more complex than you might imagine.

The product was built by @WalmartLabs, which was created after Wal-Mart acquired Kosmix, a Bay Area start-up that was tracking social networks to determine people’s interests.

Venky Harinarayan, founder of Kosmix and co-founder of @WalmartLabs, said developing the application was surprisingly difficult. First, they had to find the relevant information on a person’s Facebook page. Then, they had to find products that best matched those interests.

For example, if someone likes Lady Gaga, the most obvious product to recommend is her albums. But a fan would likely already own those. A better gift is something more special — a collector’s item or a limited edition. That’s a more complex problem.

Since gifting is a practice humans naturally struggle with, maybe algorithms can do a better job.

After using Shopycat, Harinarayan learned his wife was a fan of “Game of Thrones,” the TV series on HBO. She has posted several times on Facebook about the show, but he hadn’t noticed. “Facebook is so transient and things flow by. Here’s a way to aggregate it all and put it in one place,” he said.

The technology also sometimes fails. When Harinarayan viewed gift ideas for me, it recommended a number of Sony products. Months earlier, I liked Sony’s fan page in order to gather information for a story — not because of any deep admiration I had for the company’s products. An algorithm would have a hard time knowing that.

“I think it’s a good first pass,” Harinarayan admits. “But there’s areas we should get better at. Right now, it is our goal to give one good gift idea per person. If we do that, then we are successful.”

The application is available to Wal-Mart’s 10 million fans on Facebook if they give permission to install it.

Harinarayan said Shopycat is the mega-retailer’s first foray into using social and promises that more is coming soon, including social experiences in the store and on its Web site.

Just last week, it seemed as if the dangerous riptide had finally turned for Demand Media, the social content company whose stock for the quarter bottomed out in mid-October, in the $5-a-share range.

It has now rebounded to close Friday at $7.76, with a market valuation of just over $651 million — still a far cry from a high of over $27 a share in the last year, but well below the target price of upward of $14 from Wall Street analysts.

That’s why there will be plenty of questions for CEO Richard Rosenblatt in a conference call scheduled for after the Santa Monica, Calif.-based Demand reports its third-quarter earnings, following the close of markets this afternoon.

Analysts are expecting Demand to lose four to six cents a share. Revenue is expected to be up.

One issue sure to be on the docket will be the traffic problems at its flagship eHow site, which stymied Demand in the quarter. The situation caused it to release a statement about the issue, “which the Company believes is temporary and was the result of an internal technical issue. The technical issue has recently been remediated.”

Assume you will hear more on that and other topics, including updates on the cost of its content and the continued impact on Demand of search-algorithm changes at Google, as well as how it is faring in attracting more lucrative advertising.

I will be covering the earnings and the analyst call, so tune in later today for answers.