Just as most of us must rely on relative pitch to discriminate amongst various tones, so too must the vast majority of consumers rely on relative price cues in order to determine what they’re willing to pay. What this means, according to behavioral economist Dan Ariely, is that the price of everything is “up in the air.” That’s where menu engineering comes in.

Gregg Rapp, Menu Engineer

Have you ever gone to a restaurant and found some ridiculously priced item on the menu? Of course you didn’t buy it — you’re no sucker. Or are you? This Today Show piece on Gregg Rapp may surprise you.

Rapp is a menu engineer. He helps restaurants maximize revenue by hacking common flaws in human decision-making. For example, by simply removing “$” signs from prices, people are less intimidated by them. And he advises against listing items from least to most expensive, because that focuses the consumer on price. Instead he mixes up items, making it hard to find their price — thereby encouraging the customer to emotionally commit to something before finding out what it costs. But my favorite strategy of his is that of putting some absurdly expensive item on the menu. Rapp doesn’t expect many consumers to buy it, but having it there makes expensive items appear cheap by comparison. Think about it: How many times have you ordered a bottle of wine in the middle of the price range?

Let’s give the strategy a try. We’ll assume that two companies are offering the same product to the same customer, but are using two different price lists to do so.

Company A: Silicon Valley Pricing Model

Widget — Basic : $10,000

Widget — Premium: $20,000

Company B: Menu Engineer Pricing Model

Widget — Silver: 10,000

Widget — Gold: 20,000

Widget — Platinum: 50,000

See the difference? My hunch is that most companies could increase revenue by simply adding a very high-end offering, even if they never sell a single one of those expensive units.

Arbitrary Coherence

Ariely also talks about something he calls arbitrary coherence. He argues that pricing is, at least at first, arbitrary — that we don’t all have some notion of “absolute price” when it comes to the utility we’ll get from every potential transaction. That’s the arbitrary side of his term. He goes on to argue that once we anchor ourselves to the price of something, our expectations with respect to price going forward is relative to our first impression — that’s the coherence bit.

In a seemingly absurd experiment, he showed that simply asking students if they would pay the last two digits of their Social Security number for various things significantly impacted what they were ultimately willing to pay for them:

“Did the digits from the Social Security numbers serve as anchors? Remarkably, they did: The students with the highest-ending Social Security digits bid highest, while those with the lowest-ending numbers bid lowest…. In the end, students with Social Security numbers ending in the upper 20 percent placed bids that were 216 to 346 percent higher than those of the students with Social Security numbers ending in the lowest 20 percent.”

This explains why having high MSRPs is so important. Conventional wisdom is that high list prices allow sales organizations to engage in price discrimination through discounting. There’s definitely some truth to this assumption, but there’s more to it than that. High list prices can actually increase perceived value — even in businesses in which the marginal cost of production is low.

Of all the levers a business has at its disposal, price is often the most powerful. The vast majority of revenue gains from higher prices result in profits (whereas selling more units often carries a commensurate increase in expenses). And the assumption that higher prices will erode loyalty or decrease demand is often just plain wrong. I’ve never seen a business that’s put too much thought into pricing, but I’ve met plenty that haven’t thought about it enough.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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Most people can only reliably differentiate between six tones on an absolute basis (people with perfect pitch, or roughly 3 percent of the U.S. population, can do so among up to 50-60 pitches, according to Miller), so the rest of us use relative pitch to differentiate amongst a wider range of tones.

Another approach is to connect items through a story. Stories serve as one of mankind’s most efficient compression algorithms, allowing people to dramatically exceed the seven-item limit. If you want to show your boss how hard you’ve worked, pack your presentation with data, charts, and bullet points — but if you want to have an impact, tell a story. The same goes for building great products, effective advertising and selling yourself as a candidate for a job.

What We Can Learn From World Memory Champions

How can someone remember the order of a random deck of cards in 25 seconds? That’s what current World Memory Champion Ben Pridmore has done. The previous champion, Andi Bell, can review 10 packs of cards (520 cards) in 20 minutes and then recall the details of every card by position. They both accomplish these memory feats by turning each card into some unrelated mental image and then linking the images together with a story. For more on Bell’s strategy, check out the BBC video embedded below.

While Pridmore and Bell spend endless hours honing their craft, producers (of any type of content) can deliver value to consumers — be they a boss, customer or interviewer — by packaging content in the form of a story.

Applied Storytelling:  The Pitch, the Resume, and the Product

1.  The Pitch.

After spending many years preparing presentations in which I would ask for resources, try to sell an idea or evangelize to existing customers, I now find myself on the receiving end of several pitches on an almost daily basis. Without exception, the most effective presenters tell a story.

Don’t start your presentation in PowerPoint or Keynote — start with a storyboard. Take a sheet of paper or whiteboard, divide it into a few sections with horizontal and vertical lines, and write the storyline at the top of each slide. After you’ve nailed down your narrative, think about the images, videos and supporting examples that you can use to tell your story. If you do presentations with any frequency, buy “Beyond Bullet Points” or “Presentation Zen.” Your presentations will improve dramatically with a little practice.

2.  The Resume.

If you don’t tell a story about your background, the interviewer will invent her own. So think about the story you want to tell about yourself before you craft your resume. You should obviously be honest, but you can craft the text of your resume and your interview pitch in a way that will leave the interviewer with the key points you want to get across rather than just a general impression of you based on what school you went to or how you dress.

And practice telling your story. First to yourself, then to your friends — over and over. No matter how impressive your accomplishments, your audience will have a hard time remembering them if all you do is offer a laundry list of disparate facts.

3.  The Product.

Disneyland is an incredible place, but as I noticed recently, many of the rides are actually very similar to those found in other amusement parks. Yet despite the trek to get there and the crazy long lines, my kids are devout Disneyland fans. The difference is storytelling. The same can be said, albeit more subtlety, for many of the world’s top brands.

Next time you need to convey a complex idea — whether it’s a new product concept to your boss or a new product to a consumer audience — try telling a story. Doing so will help you organize your thoughts while at the same time, making it much more likely that your audience will listen — and remember — what you have to say.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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Yet in business you often find people who have been doing something for a long time and just aren’t very good at it. Why? Lack of feedback. After all, imagine trying to solve math problems and waiting an entire year to get the answers, or hitting 1,000 serves and getting a summary of your performance at your “annual review” rather than after each serve or at the end of a game. Practice only makes perfect when there is frequent, high-quality feedback so that the right adjustments can be made, be it in math, sports, music — or business.

The Corporation and Feedback Systems

In certain disciplines, like engineering and sales, there is somewhat objective and frequent feedback. Your program compiles without an error and does what it was meant to do. Or you close the deal and make your quota. If, however, you’re in one of the many disciplines in which immediate and objective feedback is not available, practice may not lead to perfection so much as enforce bad habits.

Let’s say you’re a mid-level executive — a GM or product manager of some sort. More than likely, you’re measured by how well you interact with and present to your manager and senior executives. Consequently, you optimize to managing the bureaucracy (your boss in particular) rather than delivering the right product or service to customers. And so does your boss, and her boss, and so on and so on. Here the only thing that you’re practicing and perfecting are your brown-nosing skills. How can you expect to learn in an organization with that type of feedback and incentive system? How can such an organization, by extension, possibly produce excellence?

Product Development and Continuous Improvement

The organizations that produce excellence are those that continuously improve. The more granular and frequent the improvements, the better. For illustrative purposes, let’s compare two hypothetical companies that are going after the same opportunity with a similar product concept.

Company 1:  Large Web Company

It’s August 2009 and the annual plan has just been completed, a few months late. Regardless, the road map is set through August 2010, so it’s time to get to work. But new features continue to roll in from various executives, and since none of the older features are being dropped, the schedule is continuously moved out. Finally, after a death march, you launch your product — in October 2010. The press doesn’t like it. Millions of expected customers don’t show up. Somebody screwed up. Time to go back to the drawing board and start over again.

Company 2:  Web Startup

It’s August 2009 and you and a few friends are fired up to change the world. You decide that you’re going to launch your product in 90 days. You’re resource-constrained, so you add nothing to the requirements list and launch on time. The press doesn’t like it. Millions of expected customers don’t show up. Investors aren’t interested in what you have to offer. Every week you ship a new feature or two and learn what works and what doesn’t work. After several months of failure, some feature you cranked out late on a Sunday night leads to an uptick in traffic. Over the coming weeks and months you pull that thread. It’s now October 2010 and millions of people are using your product. You raise a hefty Series A and the press declares your company an overnight success.

This comparison in how organizations react to “failure” reveals the key reason why innovation comes from startups more often than large organizations (and why startups are often a very good training ground for product development and business talent). Large organizations generally aim to “get it right” from the get-go — an unreasonable requirement that leads to fear, posturing and endless delays. Startups, on the other hand, are just trying to get it right before they run out of money.

Human Resource Development and Continuous Improvement

Consider as well the feedback cycles in these two organizations. In the large organization, you likely get feedback when you do periodic releases — perhaps every six or 12 months — with the really important feedback saved for your annual review. In the startup, you get feedback after 90 days and then every single week. And there exists both carrot and stick in terms of providing honest and meaningful feedback — the carrot is equity and the rewards of creative ownership while the stick is running out of money.

Overnight successes make for great stories, but that’s largely because they’re so rare (and more often the stuff of fiction). Success, the kind that leads to great products and businesses, is built on the foundation of a huge amount of hard work over many years. Baby steps are the sure path to excellence in everything from product development to managing your own career — continuous adjustments based on frequent feedback lead to cumulative gains.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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Of course, none of this happened in person, but via Facebook and Twitter. The asymmetrical and casual nature of social networks is allowing humans to engage in what Robin Dunbar has termed “social grooming” with increasingly larger groups — without investing increasingly larger amounts of time. The value of these communications improvements is so great that going forward, I expect significant increases in human and capital investments in this space.

Dunbar’s Number and Social Grooming

Dunbar argues that the number of people with whom humans can maintain a relationship is a function of neocortical size. He puts that number at ~150 — what’s known as Dunbar’s Number.  He further argues that language may have emerged as a device to more efficiently maintain those relationships, that while other animals maintain relationships through activities like physical grooming, which limits social productivity, language allows humans to engage in the far more efficient act of “social grooming.” For example, a brief watercooler discussion or a “Happy birthday” wish on Facebook replaces the need to spend a great deal of time physically grooming each other as a way to stay connected. Consequently we can achieve greater coordination and collaboration with comparatively less effort.

Over the past decade I have noticed a dramatic increase in the number of people with whom I stay connected, yet the time I spend on these activities hasn’t increased one bit. It seems like electronic communications and social networks are increasing Dunbar’s Number such that it’s now somewhat divorced from neocortical size.

What’s Next?

This trend will continue and probably even accelerate as a result of global broadband penetration, the proliferation of smartphones and high-bandwidth wireless data, the cheap availability of video and knowledge gains from these first-generation services. In an effort to peek into the future, I retained all communications over the course of a week in an attempt to identify a few opportunities for improvement.

1. Smart Email

Even after removing what was clearly spam, only 25 percent of the email messages I received over the 7-day period were from a real person directed solely to me. Why do I need an email every time Netflix ships me a DVD and every time I return one (asking for a rating)?  There must be a better way for Netflix (and everyone else) to notify and collect feedback. Ditto for billing — phone service, cable, online services and so on.  Why can’t my email client figure out what’s an invite and queue those up in once place?  Can’t the online mail guys figure out what’s public vs. private and somehow how help me auto-filter my mail (e.g. when Apple blasts a message to 10 million people, that’s not a one-one-one communication — file it in a “MyBrands” folder or the like). Shouldn’t it be easier for a consumer to quickly (visually) identify a personal communication from a group conversation from a notification?  And why is voice mail a separate category from email?

2. Next-Generation Collaboration

An even larger category of email messages can be categorized as collaboration. Whether in the form of a blast to a large group of people (and the many subsequent replies) or a discussion amongst just five people, email today is not currently well suited for conversations with multiple people. Nor is instant messaging, SMS, video conferencing, the telephone or voice mail. Why aren’t all conversations threaded? Why can’t I edit something after it’s been “sent” in a server-based computing world? Why is sharing documents and managing collective editing so difficult? How can we merge audio and video bits together the way do with text? When will we be able to approximate the ubiquitous hallway conversation online? Perhaps the recently announced Google Wave will be the answer, or the foundation of one? Sococo may also offer some insight into what the future holds.

3. Location-Based Groups

Whenever I’m stuck in traffic these days, I do a quick search on Twitter to figure out why.  It would be nice to be automatically “subscribed” to my location to find out what’s going on. Speaking of driving, I’d love to be able to broadcast a message to everyone in my area when a car is blocking my garage door. Such dynamic and temporal location-based groups will finally be enabled by pervasive GPS and other location-based techniques. Facebook used domains to do “network” authentication (e.g. harvard.edu); might someone use location to do location-based authentication?

With every improvement it feels like the pace of innovation will slow, but then something new turns up. This will surely be the case in the communications space in the coming years, whether it’s one of these ideas or, more likely, something crazy that nobody has thought of to date.  And Dunbar’s number will continue to grow, only great new communications products will be the factor rather than human evolution.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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Like so many other industries, early attempts at delivering online education have generally consisted of making available the same content that’s found offline. While this is a good start, the key to online education is amplifying the way in which we learn when we’re at school — from our peers.

Modeling Agencies vs. the U.S. Marine Corps

Malcolm Gladwell in 2005 wrote an entertaining piece in The New Yorker entitled “Getting In” that distinguishes between institutions that select and promote (modeling agencies) from those that improve (the Marines):

Social scientists distinguish between what are known as treatment effects and selection effects. The Marine Corps, for instance, is largely a treatment-effect institution. It doesn’t have an enormous admissions office grading applicants along four separate dimensions of toughness and intelligence. It’s confident that the experience of undergoing Marine Corps basic training will turn you into a formidable soldier. A modeling agency, by contrast, is a selection-effect institution. You don’t become beautiful by signing up with an agency. You get signed up by an agency because you’re beautiful.

At the heart of the American obsession with the Ivy League is the belief that schools like Harvard provide the social and intellectual equivalent of Marine Corps basic training—that being taught by all those brilliant professors and meeting all those other motivated students and getting a degree with that powerful name on it will confer advantages that no local state university can provide.

Gladwell goes on to suggest that perhaps the Ivy League resembles a modeling agency more than it does The Marine Corps. But rather than take on that debate here and now, let’s instead focus on the objective of making online education improve anyone with the interest to learn — that is, on treatment effects.

How Important Are Great Teachers?

Everyone has had a teacher who’s made a difference in his or her life.  But of all the teachers, professors and tutors you’ve had, how many were great?  In an informal survey of my friends about their collective 80-plus teachers from grade school through college, the average number of “great teachers” was three. That’s 3.8 percent.  Even if I take K-8 out and evaluate high school through college, the great teacher percentage barely breaks 5 percent.  And many of these teachers were considered great because of the personal attention they gave individual students, so bringing them to the masses would likely take away from some of their greatness.

We should absolutely find and reward great teachers, but I suspect that the key to expanding education to everyone is by changing the definition of “teacher.” When I asked those same people how they learned, they all mentioned peers — other students; study group members and project or lab team members; fraternity brothers; tutors; siblings and parents.

The Socratic Method

Harvard Business School teaches nearly every class with the case study method, itself a form of the Socratic Method.  Students are given a case to study prior to class and the professor acts as a moderator for the discussion. I found the strategy to be very effective; the diverse background and experience of students in the class makes the discussion especially rich and interesting.

Each class kicks off with a cold call, and everyone is at risk of being asked to start off the case. If you blow a cold call, your grade suffers significantly and so does your reputation — great incentives to do your work every night. More broadly, that’s one of the things that differentiates being in school vs. watching MIT videos online. In order to bring elite education online, there must be a similarly strong incentive system.

After the chosen student opens the case, the rest are permitted to begin a debate.  Students are rewarded not only for providing well thought-out arguments, but for offering differing opinions. This debate stimulates a student’s brain in a way that a lecture, reading or watching a video simply does not. If you measured the average heart rate of a student in a lecture vs. a Socratic Method alternative, I have no doubt that you would see a significant difference. I don’t know the physiology behind why this may make your brain more likely to engage in learning, but it does. In a big way.

My best teachers have always approached education with a form of the Socratic Method. They understand that telling someone the answer rarely imparts knowledge about anything other than the lecturer’s intellect.

What Does All This Mean for Online Education?

Online communities can be deployed to deliver peer-to-peer education in a way that is far superior to anything else that exists online today. Using some form of community-driven Socratic Method, strong incentives beyond self-edification, and a way to measure and certify knowledge, online education will be able to deliver an Ivy League-quality education to anyone with the desire to learn.

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It wasn’t until years later that I discovered the secret, and it came via a Princeton economist. Understanding the fact that wine is an agricultural product, and as such is dramatically affected by weather, Orley Ashenfelter used decades of weather data and auction prices to come up with this equation for Bordeaux wines:

“Wine quality = 12.145 + 0.00117 winter rainfall + 0.0614 average growing season temperature — 0.00386 harvest rainfall”

Assessing wine is considered an art rather than a science, but oftentimes creativity is about applying a little science to art — as Orley did by taking into account weather and auction data. In an effort to inspire entrepreneurs to also turn ill-practiced art into science, below I share a few other examples.

The Mathematics of War

Sean Gourley, a physicist by training, wanted to gain a deeper understanding of what was happening with the war in Iraq.  So he worked with a cross-functional group to understand “the mathematics of war.” What he found was fascinating, that guerilla wars around the world — in Iraq, Columbia, Peru, Indonesia and Afghanistan — could be reduced to this equation:

Gourley tells the entire story in the video below, including how he arrived at the fact that alpha (the slope of the line) is 2.56. As he explains it, guerilla war has evolved to a state of equilibrium that can be defined by an equation — that there is an optimal organizational structure for fighting an organized military. Guerillas either discover this structure, and implicitly, this formula, or they get killed off.

Mathletics, Sabermetrics and “Moneyball”

The story of Sabermetrics and statistics in baseball has been told many times, so I won’t repeat it here. Suffice to say that if you have any interest in baseball stats, “Moneyball” is a must-read.

A story not as widely told is that of Wayne Winston and the Dallas Mavericks. A few years ago, Winston, a decision sciences professor from Indiana University, consulted the Mavericks on a new rating system aimed at measuring the impact a player has on the entire team. Points or assists don’t offer much information in and of themselves; what’s far more valuable information for a team is answering the question: “When player x is on the court, does our lead grow or shrink?”

From a 2003 New York Times article about the system:

Ignoring every traditional statistic for players, Sagarin and Winston have designed a ranking that is modeled on hockey’s plus-minus system, in which players receive credit for being in the game when their team does well. Whether they actually score points or grab rebounds does not matter.

”Did you make the pass before the assist? Did you tip a ball to someone who made a shot? Did you set a pick? Did you take a charge?” said Winston, a fast-talking former ”Jeopardy” champion who, like Sagarin, grew up outside New York City rooting for the Knicks of the late 1960′s and early 70′s.

”Nobody’s got a stat for these,” Winston said. ”Ninety percent of basketball is made up of things there aren’t stats for.”

I just pre-ordered Winston’s new book, “Mathletics,” due out this fall.  I can’t wait to read it.

How to Develop a “Prediction Function”

Step 1:

Start with some insight about the relation between two things — like the fact that weather determines wine quality.

Step 2:

Identify “output” data to tune your prediction function — for example, historic auction prices as an approximation for wine quality.

Step 3:

Graph the data to examine the best way to extract the function.  Does the data look like it fits a line?  If so, do a simple linear regression (a very simple way to do this is the Regression function in Microsoft Excel’s Data Analysis package – unfortunately, it’s no longer available on Excel for the Mac, but you can do it elswhere).  Does the data look like an exponential curve?  If so, you can do a logarithmic regression (here is an online tool for a simple regression). And you can use much more sophisticated statistics to find the right equation, if one exists.

Step 4:

Profit.

There are so many areas in which having the ability to make dramatically better predictions would enhance our lives — jobs, dating and health, to name a few.  I can’t wait to see what the future has in store.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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Since then I have been fascinated with finding quick and dirty tricks to arrive at answers (or good approximations) to everything from the probability that my beloved Arizona Wildcats basketball team will win a game to market sizes and rates of return. Below are three of my favorite business hacks.

1. Using “Mathemagic” to Do Rapid Mental Calculations

Being able to rapidly do math in your head will allow you to think about things in real time that can change the course of the discussion. It isn’t that hard to do back-of-the-envelope calculations that give you an idea of the reasonableness of key assumptions with the help of a little mathemagic. For more detail on this, check out Arthur Benjamin’s book “Secrets of Mental Math” or the embedded video below.

In the meantime, here are a few of my favorite tricks.

Square any 2 digit number ending in 5.

For example, let’s square 25.

Step 1: Take the first digit and multiply it by itself plus 1. So 2 x 3 = 6.

Step 2: Take the product from Step 1 and concatenate it with 25 on the right to get 625.

Let’s try again. Square 65.

Step 1:  6 x (6+1) = 42.

Step 2: Append 25 to the right of 42 and you get 4225.

Multiply any two digit number by 11.

For example, let’s multiply 78 x 11.

Step 1: Take the number being multiplied by 11 and add the digits together.  So 7 + 8 = 15.

Step 2: Insert a zero between the same two digits.  708.

Step 3: Add the result from Step 1 to the zero of Step 2. In this case we have 15, so we need to carry the 1. An easy way to think about it is 70 + 15 = 85 with the 8 from 708 added at the end for 858.

2. The Rule of 72 (or 69.3 or 70)

If you invest in the stock market and expect your money to earn a return of 5 percent per year, how long will it take for your money to double? If a VC is managing $100 million and limited partners expect a 25 percent annual return, how often do they need to double their money?

The Rule of 72 is a linear approximation — one of the coolest applications of derivatives for the pragmatic businessman. Here is a simple derivation for those of you who care.  For everyone else, simply divide the expected (compounding) return into 69, 70 or 72 to find the number of periods it takes to double. So it will take us roughly 14 years to double our money at a 5 percent expected annual return and the VC will need to double his fund every three years or so in order to hit the target return of his limited partners.

Understand that this is an approximation and varies with the size of the return — see the table below from Wikipedia to get a better idea of the precision of the approximation.

3. Solving Problems With Microsoft Excel’s Goal Seek

Goal Seek is the ultimate business hack. When you know the desired result of a formula, but you don’t know the input value the formula requires to yield the result, Goal Seek can help you get your answer. For example, let’s say that you want to figure out the required annual return it takes to double a $100 million fund over three years.

Step 1: Setup the problem.

Step 2: Use goal seek (Tools -> Goal Seek).

“Set cell” = the value you want set, here the value of the fund in Period 3. “To value” = the value you want the cell to be changed to, here we want $100 million x 2 or $200 million. And “By changing cell” = the thing you are trying to determine, here the annual expected rate of return.  Hit “OK.”

Step 3. Profit! We need a 26 percent annual return to double every three years. Of course, we could have used the Rule of 72 and a little mathemagic to arrive at this answer without Excel. But Goal Seek works in all types of scenarios.

Hopefully you will find one, or several, of these hacks useful in your job or life. If nothing else, you can use them to impress your friends or to inspire your children.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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Evolutionary Biology and Conformity

Imagine our ancient ancestors out on the savanna in search of food. Chasing a large group of hunters who were running after something out of view was probably a better survival strategy than pursuing animal tracks that may or may not have led to food. Gregory Berns argues that mankind’s propensity to follow the crowd is at least partially a result of evolutionary biology.

Such a propensity is so ingrained in human nature that we will go to ridiculous lengths in order to adjust our beliefs to those of a group, as proven in the series of conformity experiments run by Solomon Asch in the 1950s. According to Wikipedia:

In the basic Asch paradigm, the participants — the real subject and the confederates — were all seated in a classroom. They were asked a variety of questions about the lines (which line was longer than the other, which lines were the same length, etc.) The group was told to announce their answers to each question out loud and the confederates always provided their answers before the study participant. The confederates always gave the same answer as each other. They answered a few questions correctly but eventually began providing incorrect responses. In a control group, with no pressure to conform to an erroneous view, only one subject out of 35 ever gave an incorrect answer. However, when surrounded by individuals all voicing an incorrect answer, participants provided incorrect responses on a high proportion of the questions (36.8%). 75% of the participants gave an incorrect answer to at least one question.”

It’s very challenging to make decisions based on your own information and logic when everyone disagrees with your point of view. We have an urge to conform, as we learn again with each economic boom and bust. Unfortunately, as David Hirshleifer describes in ”The Blind Leading the Blind:  Social Influence, Fads, and Informational Cascades,” “If there are many individuals, then…with virtual certainty a point in the chain of decisions will be reached where an individual ignores his private information and bases his decision solely upon what he sees his predecessors do.”

Weird Ideas That Work

The inspiration for the title of this post came from the book “Weird Ideas That Work,” in which Robert Sutton suggests hiring “people who make you uncomfortable.” He argues that employers typically hire people like themselves and that most interviews are more about the social fit between the candidate and interviewer rather than the candidate and the job.

So what can you do to embrace those who make you uncomfortable?

1.  Identify your heroes.

Chances are that the historical figures you hold in high esteem made those around them uncomfortable in their day. Einstein did. Gandhi did. Jefferson did. Apple’s “Think Different” campaign was as much about communicating what the company stood for to its employees as it was about selling Apple products to its customers. An organization that embraces unconventional thinkers has an unfair competitive advantage in a world governed by conformity.

2.  Adjust your hiring process to focus on what really matters.

Jeff Cordova, a former Yahoo colleague of mine, puts all engineering candidates through a code test before he determines cultural fit or the like. He literally sits down in a room with a candidate and spends a few hours coding up an application with them. At the end of the test, he has a very good idea of their software engineering skills and often asks other members of his team to drill down in a particular area of expertise. It’s only after qualifying their skills as an engineer that he allows his team to determine their “fit” within the organization.

While software engineering is relatively easy to test, you can apply a similar type of testing process for just about any role to reduce the impact of social bias in hiring.  Microsoft notoriously put candidates through case study interviews (I don’t know if they still do), as documented by William Poundstone in “How Would You Move Mount Fuji?”

Spend more time thinking about interview-based experiments that you can run on candidates to test what really matters for the role and you might find yourself hiring a different type of person.

3.  If you have a negative reaction to an idea, use the 5 Whys.

The 5 Whys is a method to get at the root cause of a problem. When you hear an idea, before you immediately respond, try to understand the underlying reason for your knee-jerk reaction. You may find that your reaction is more about protecting existing orthodoxy or the source of the idea than it is about the merits of the particular approach at hand.

4.  Consider increasing organizational diversity.

The true benefit of diversity is that it has the potential to produce better results.  Diversity along the lines of age, gender, race, religion and sexual orientation has the potential to make an organization more resilient to conformity. Different people from different backgrounds bring in different biases. And groups that have experienced greater prejudice may have a membership inoculated from group think as a matter of self-preservation — that is, when everyone hates your group, you tend to hold a differing opinion.

It’s not easy working with one of the rare people who is deeply nonconformist. But if your goal is to be innovative, to create something great and to make a difference in the world, you should be prepared to make those around you uncomfortable and recruit others who do the same to you.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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Priceline, Hotwire and vacation packages from offline and online travel agencies can offer prices that are dramatically lower than published rates without cannibalizing revenue because they are opaque selling channels. Opaque selling makes some part of a purchase non-transparent to the consumer (such as which hotel, what time the flight will leave, what products you are buying) so that the probability of revenue cannibalization is dramatically reduced. While opaque selling creates incremental revenue for airlines, hotels and car rental companies, can it work in other industries?

Fukubukuro — “Lucky Bag”

Fukubukuro, a Japanese New Year’s Day tradition, puts opaque selling to work in shops across the country each year with retailers packaging an unknown collection of things into “lucky bags” and offering them at various prices. The idea is that the retail value of the bag is much greater than the price, but the catch is that you don’t know if you want what’s in it.

Just about every retailer in Japan participates in this tradition. While it started as a way to clear out last year’s inventory, it’s now as much about promoting retail shops as it is about opaque selling. Japanese friends of mine describe Fukubukuro much in the same way eBay fanatics describe bidding for an item — as a game-like addiction.

When Apple opened its Union Square retail store in San Francisco in 2004, it offered lucky bags for $250 with the chance to win the then-new iPod mini. The bags were said to contain 7-8 items with a retail value of $600-$1,000.

Why Opaque Selling Works

While the seller of a product or service would ideally like to charge the maximum price a buyer is willing to pay — the goal of price discrimination — the seller doesn’t actually know what that maximum is. And the buyer has no incentive to tell, as anyone who has haggled with a car salesman well knows.

So sellers create segmented offerings as a way to get at least some customers to pay more. For example, airlines offer first-class seats at a dramatically higher price per unit of space consumed — the buyer gets more space and the prestige of flying in first class and the airline gets an order of magnitude higher revenue per customer for the same flight — often 10x more.

Other techniques include charging a high starting price and then lowering price through age-based discounts (movie tickets for kids and senior citizens), channel-based discounts (online vs. offline), volume discounts (frequent flyer programs) and geography-based pricing differences (enterprise software).

Even with all of these techniques, the market clearing price for products often leaves a seller with excess inventory — open seats on a flight, for example. The marginal cost of that inventory is often so low that it is usually possible to sell it for a profit, but doing so means that people who would have bought the product at a higher price will now pay less and aggregate revenue will decrease. By selling a hotel room through a bundled vacation package a seller dramatically decreases the likelihood that she is cannibalizing her revenue — especially in fragmented markets.

The Opportunity

At the end of each day, many shops toss perishable items. Why not offer lucky bags?  At the end of a season, many clothing retailers push their inventory to outlet stores under the assumption that forcing consumers to drive out of the way will prevent the cannibalization of sales, even though such outlets are increasingly located near major metropolitan areas. Why not find an opaque channel instead?  The same thing goes for consumer electronics, video games, and so on. The Amazon.com Gold Box has daily deals — why not turn the Gold Box into the Amazon.com “lucky” box?  It would offer consumers killer deals, it would be fun, and it would offer Amazon and its suppliers an opaque selling channel.

Fukubukuro aside, would opaque selling work outside of travel? I believe that it can. After all, the fundamental economic drivers of opaque selling in travel exist in many other industries. Less transparency isn’t always such a bad thing.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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When in doubt, diversify. That’s the underlying logic behind diversifying everything from your stock portfolio to the number and types of businesses in your company, and often implicitly drives product development organizations. The argument behind diversification is that there is too much randomness in the world to have an edge based on skill.

While there is unquestionably some truth to the idea that the world is often too random to literally make just one bet, the widely held assumption that diversification is a free lunch is just plain wrong.  Just as there is benefit to be derived from diversification with respect to risk, there is a cost, too –  that of losing whatever edge you might have been able to gain from skill. Diversification is a strategy to regress to the mean — that is, to be average. For those pursuing excellence, focus is a far better strategy.

Regression towards mediocrity

The notion of regression comes from Sir Francis Galton‘s “Regression Towards Mediocrity in Hereditary Structure.” Over time, regression towards mediocrity came to be known as regression to the mean. I prefer Galton’s description: Diversification usually leads to mediocrity.

Figure 1 shows the tension between the “edge” gained by focus (y axis) and the gains from making numerous “bets” (x axis). Conventional wisdom has it that any particular bet you make may earn returns greater than or less than the mean — as the number of bets approaches 100 percent of possible bets, you end up with the mean by definition. Many investment strategies explicitly seek to be average; index funds do so by algorithmically approximating entire indices. If you lack the knowledge or time to do the work yourself, and your goal is simply wealth preservation, such a strategy may in fact make sense.

Figure 1:  Investment Edge vs. Number of Bets:  Regression to the Mean

However, if you’re trying to earn excess returns or build a great product, diversification is the enemy. In Figure 2 I have offered an alternative visual of how a knowledgeable investor might perform. I believe an investor can leverage knowledge he has about a particular industry or company to beat the average on a risk-adjusted basis. There is still uncontrollable risk, so I’m not arguing that an investor should invest in just one stock. On the other hand, there is a cost that comes with the security of diversification; you pay for that insurance. Too much, in fact.

Please note that my belief, even though it squares with that of Warren Buffett’s, flies in the face of conventional economic wisdom. And that I am not a professional investment adviser.

Figure 2:  Investment Edge vs. Number of Bets:  Excess Returns Through Focus

Focus increases your ability to understand what matters

The problem with diversification is that the effort required to master something is so great that every spare neuron spent on something else gives the person with focus an upper hand. Diversification is attractive because it’s safe and requires little effort.

Warren Buffet and many of the best investors I know favor making a few very well-informed bets rather than opting for significant diversification. Diversification strategies like funds of funds are responsible for allowing Bernie Madoff to exist. Good limited partners (LPs) do a great deal of work to pick a relatively small number of investment vehicles, which involves significant research before and oversight after an investment (both of which can quickly uncover Madoff-type scams). Good LPs put their money behind investors who do the same, and good VCs put their money behind entrepreneurs who have a point of view, domain expertise and conviction to realize the impossible.

Focus is not inconsistent with intellectual honesty. It does not mean ignoring feedback. It simply means that your bets are well selected and that your conviction to find a way to make something work is high enough to overcome the inevitable hurdles of building a company, product or investment portfolio.

Focus forces brutal prioritization

Making few bets forces you to make hard decisions. It’s extremely hard to measure the value of something against some abstract and absolute notion of value.

Proponents of diversification argue that it takes the edge off of making a mistake. That would be a good argument if people acted the same way independent of their ownership in an outcome, but human beings do alter their behavior based on how much skin they have in the game. When costs and benefits are divided amongst too many people, accountability is lost. Excessive diversification makes participants passive, dependent on the actions of others who are dependent on the actions of others, and so on. It turns them into, at best, free riders, and at worst, suckers.

Focus brings clarity

While everyone else is chasing diversification, those who make a few well-placed bets learn at a faster pace. They have clarity as to what matters in an investment, company or product. This clarity attracts others and makes things clear for them, too.

When Steve Jobs took over Apple again in the late 1990s, he first pruned the organizational ranks. He then pruned Apple’s product line down to just four. He communicated Apple’s culture to employees, partners and customers with the Think Different campaign. From that clarity came the iPod and the iPhone.

Making fewer bets requires conviction. It requires the courage to stay the course. And it requires the support and resources to take the long view. If you do these things, odds are that you’ll do something worthwhile.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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When I was 8 years old, my father explained to me the secret to American prosperity.

Immigrants come to the United States and take menial jobs so that their children have a chance at a better future, he told me. While the jobs they take are below their intrinsic capabilities, they’re focused on giving their children a better life, not personal job satisfaction. Second-generation children, seeing how hard their parents work to give them an opportunity, in turn work hard at school, where, he noted, they often focus on mathematics and science in pursuit of the economic returns promised by careers in engineering and medicine. Third-generation kids figure the economic return on effort expended is better for business and legal professionals and pursue those professions instead of technical ones.  By the fourth generation, any immigration-related incentives to work hard are largely nonexistent.

It was a gross generalization used to explain to a child the importance of immigration, but one that I have since found to be generally accurate.

On this 233rd celebration of U.S. Independence Day, in the midst of the worst economic recession in at least a lifetime, there is a national debate taking place as to the direction of the country. And while I’m confident that we will preserve our democracy and capitalism, I’m concerned about the tone and tenure of the discussion around immigration. Smart immigration policies will do more for American innovation and productivity than better math and science education, more spending on basic research and additional venture capital combined. If we get strategic about immigration, I believe the U.S. can preserve its economic leadership position in the world far longer than anyone currently expects.

Why immigration is more important to innovation than broad-based science education

Shortly after President Obama was elected, The New York Times published an article by Ian Ayres in which he expressed support for appointing Larry Summers as Treasury Secretary. The article quotes Dr. Summers on his assumption that top physics researchers are 3-4 standard deviations above the mean in terms of I.Q. While I don’t have evidence to support his assumption, my intuition is that he’s right, including when he notes what a small group of people these great thinkers represent. Dr. Summers states:

“If…one is talking about physicists at a top-25 research university, one is not talking about people who are two standard deviations above the mean. And perhaps it’s not even talking about somebody who is three standard deviations above the mean. But it’s talking about people who are three-and-a-half, [or] four standard deviations above the mean in the 1 in 5,000, [or] 1 in 10,000 class.”

If we assume that talent is evenly distributed throughout the planet, that the U.S. population is around 300 million, that the global population is 6.7 billion, and that 1/5,000 people are the top candidates to push U.S. innovation forward, that gives us a pool of 60,000 people in the U.S. and 1.28 million outside of it.

Innovation will not be spurred solely by giving those 60,000 Americans access to math or science education, but by providing the right incentives for them to enter the scientific and technical professions. More importantly, we could radically increase the number of innovation candidates through targeted immigration of the 1.28 million people that hail from elsewhere.

The government cannot mandate desire

If the first benefit of immigration is importing talent, the second is that of importing “hunger.” Many countries lack a way to identify and reward their brightest citizens, while that has been the allure of the U.S. since our inception. So I would argue further that the “innovation probability” of a high I.Q. individual whose family has been in the U.S. for many generations is less than that of someone who’s new to our nation and has a comparable intellect, but far more desire.

The time for a strategic approach to immigration is now

Broad-based mathematics education will strengthen our nation by improving our workforce, but that is not best path to innovation. Basic research may create jobs and openings at universities to lay the foundation for innovation in certain areas, but the ROI on such investments is uncertain and sometimes misplaced. And the pool of available venture capital is not the constraining factor in new startups — lack of talent is.

It’s time for a more strategic and aggressive immigration policy, one that targets the best and brightest around the globe and makes it easy for them to become permanent residents. We should be recruiting the world’s best talent the same way top companies recruit the best talent. Talk to anyone who’s tried to become a resident here lately and you’ll quickly realize the process is long and often highly random — in other words, very discouraging.

Strategic immigration, together with our strong democracy, capitalistic system and melting-pot culture, will deliver a better standard of living for many generations of Americans to come. I am grateful to all of the immigrants in the U.S. on this Fourth of July. To them, I say thank you — for everything you do.

Mike Speiser is a Managing Director at Sutter Hill Ventures.

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One of the key concepts at the core of traditional marketing is the 80-20 rule — that some 80 percent of the effects (or in this case, profits) are the result of 20 percent of the causes (here, customers). Indeed, if you’re able to target just a small group of people in order to successfully yield most of your profits, there are all sorts of benefits — it’s easier to reach a small group, it’s easier to build them the right product, and so on. Most large technology sales and marketing organizations have taken the 80-20 rule to heart and focus on some small fraction of the tens of millions of businesses on the globe, often targeting just the Global 2000.

While the 80-20 rule can be very powerful, the reality is that many of the costs associated with building, supporting, distributing and selling technology products have dropped dramatically in the past decade. Yet many enterprise technology executives are operating as though the cost of distribution hasn’t changed since the early 1990s. In the coming years, I expect startups to increasingly target the massively underserved small- and medium-sized business (SMB) segment by taking advantage of the arbitrage between actual and assumed costs of sales. Self-service sales models will be a key element of these startups that will forever change the face of the enterprise technology business.

U.S. Business Statistics | The Economics of Sales

A quick look at Census Bureau statistics on business size reveals some startling information:

• Of the 25 million U.S. businesses, only 5.9 million employ one or more people.
• Of the 5.9 million employer firms, only 17,047 have 500 or more employees, yet those firms account for 49 percent of U.S. employment and 61 percent of revenue. That translates into well under 1 percent of U.S. firms accounting for 61 percent of revenue
• Of the 17,047 firms with more than 500 employees, the 890 with 10,000 or more employees account for 26 percent of U.S. employment and 37 percent of revenue.

Let’s say you’re an enterprise software company and that your economic model requires each sales professional to earn $2 million-$3 million in revenue a year to cover the costs of your operation, including the cost of the sales themselves (salaries, commissions, travel and entertainment, sales support and operations, etc.). Each one of these representatives can only do so many deals every quarter, so the revenue per deal must be fairly high — let’s say that the average deal size per customer, per year, must be around $100,000. And that’s just your part of the solution. There are likely other software, hardware and consulting costs associated with the complete solution.

If we assume that the average pre-tax margin of a small business is 10 percent and we take revenue for the 98 percent of U.S. firms with 99 or fewer employees, divide their aggregate revenue by the number of firms to get average revenue per firm and then take 10 percent of that number to derive pre-tax profit per firm, we get $103,962. In other words, the direct sales model would require taking roughly 100 percent of pre-tax profit from firms with fewer than 99 people for just your component of the solution. These are back-of-the-envelope assumptions and are for illustrative purposes only — the point is that direct sales organizations cannot serve the vast majority of businesses on the planet.

Currently the most effective way to reach the SMB segment is via bundling, for example to have your software or hardware bundled (that is, OEM agreements) with some big-ticket item like a server; that vendor then distributes the combined product through a single or two-tier distribution channel. A very small number of products have the import to be sold through the channel on a standalone basis (such as backup hardware and software). If you compare what the large enterprise has in terms of technology vs. the small business, there is a massive gap. Most small businesses hack together solutions for many of their problems with Microsoft Excel, pen and paper, or nothing at all.

Enter the Web

In Figure 1, I’ve depicted the universe of potential customers in the U.S., from the roughly 300 million consumers at the base of the pyramid to the 17,000 largest enterprises at the top. Large enterprise IT companies have been created by primarily selling to the largest customers. And large consumer web companies have been created by targeting a large percentage of the total population. Far fewer businesses have been created, at least in high technology, by serving the small- and medium-size business segment as they were hard to reach. But that’s all changing rapidly.

Figure 1:  The Customer Segment Pyramid

In a web-based software firm, there isn’t a significant marginal cost of research and development (R&D). Cost of goods sold are often very small (especially in software), and CAPEX on a unit amortized basis is trivial. The biggest marginal expenditure is cost of sales. If a company can figure out how to simplify sales delivery and service by leveraging a self-service model, it can reach a large market segment hungry for better products and services.

Using techniques perfected by consumer web companies, a generation of enterprise IT companies will emerge that deliver a vastly superior user experience to IT professionals and employees alike. Improvements in ease of use will liberate employs from terrible software, server-side software development will increase the pace of product improvements and lower support costs, and the self-service model will change the economics of enterprise IT sales forever. Google’s success is as much about AdWords offering advertising services to the SMB segment as it is about serving consumers search services.

The dominant player in the SMB segment of the IT market today is Microsoft, as it’s been able to drive significant sales by riding Windows OEM agreements. If I were Microsoft, I would be more concerned about software-as-a-service solutions eroding my market share in the SMB segment than the threat of consumer web search, but I digress…

While the marginal cost of direct sales is very significant, the marginal cost of self-service looks more like the marginal cost of R&D — that is, very small.  With a low marginal cost of sales, the 99 percent of firms that are poorly served today will be served much better in the coming years. And over time, these solutions will likely work their way up the pyramid to penetrate the small group of very large firms that are currently being served by direct sales professionals. If you’re looking for opportunities to disrupt enterprise technology businesses, you would be well-served to start with the sales model.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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During World War II, the Allies brought significant quantities of food, medicine and other supplies to previously isolated islands in the Pacific. Natives of these islands were distraught when the Allies left and the cargo vanished, and in noting the events that had occurred prior to its arrival assumed they had caused the cargo to drop from the sky. So in an attempt to bring back the supplies, the islanders created structures that visually resembled control towers, carved headphones out of wood and made large-scale model airplanes. It was one example of what are known as cargo cults, religious practices that are brought on by the interaction of advanced societies and remote tribal people.

In his 1974 Caltech commencement address, Richard Feynman compared examples of modern “science” to cargo cults, and called the phenomenon cargo cult science. As I re-read the text of Feynman’s address recently, I couldn’t help but think of how applicable it was to modern business management.

Causation vs. Correlation

Why are most of the meetings you attend so worthless? Perhaps those holding them are simply replicating the behavior of their own superiors. “It works for him and he is the CxO, so if I do it maybe I’Il be CxO someday.” Ditto for why so many presentations contain so much content yet so little information. In my experience, it’s a result of the presenter trying to follow the company’s “proven” formula, whatever that might be (e.g., executive summary, lots of detailed text and charts to show you how smart you are, and a conclusion). Trust me on this, the formula — any kind of formula — sucks. Try telling a story instead.

The list of cargo cult management examples goes on and on. The business section of your local bookstore will provide further insight into why cargo cult management runs so rampant — it’s hard to even find authors that understand the difference between causation and correlation.

Jim Collins’ “Built to Last” and “Good to Great” are two examples of cargo cult management (oh, and bestsellers). In ”Good to Great,” Collins and his team of “researchers” examined historic stock returns of potentially “great” companies relative to a market index. The research team took the top historic performers and then narrowed the list down further by looking for only those companies that did much better than their industry peers (e.g., if an entire industry did exceedingly well, the company was dropped). They compared these “great” companies to those with lower stock returns, which they deemed as merely “good.” How did they compare the two? By reading news articles and doing interviews and “systematically” coding the results into “strategy, technology, and so forth.” They then used all of this research to identify patterns they could use to tell a story about how any manager could take a company from good to great. With that formula, such a story should be relegated to the fiction section.

What if aerospace engineers employed a similar analytical approach, that of looking at historic data and use induction to find the answers in patterns gleaned from the data? Our airplanes would fly about as well as the one in the photo above.

Entrepreneurship

There are examples of cargo cult entrepreneurship as well, and startups run this way have a dramatically higher probability of running out of money. Of course, as startups that survive earn the air cover of a business with momentum they often allow inefficiency and bad management to creep into the system.

I believe that the biggest advantage a startup has over a big competitor is intellectual honesty. Most of the entrepreneurs I know start their own companies or join startups in order to do what they wanted to do at their larger [previous] employer — to do something worthwhile. They finally reach the conclusion that the only way to avoid cargo cult management is to start from the ground up. This is particularly true of technical professionals, who view intellectual honesty as core to their job. How often do you hear business people poke holes in their own arguments the way engineers do?

Which brings us back to the last line of Feynman’s 1974 address:

“So I have just one wish for you — the good luck to be somewhere where you are free to maintain the kind of integrity I have described, and where you do not feel forced by a need to maintain your position in the organization, or financial support, or so on, to lose your integrity. May you have that freedom.”

Well said.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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The marginal cost of delivering web-based content is approaching zero, so while some publishers continue to charge for it, they are the exception and not the rule. This has led to the reliance on advertising as a revenue model by most consumer web publishers. But if the marginal cost of a page of content is zero, isn’t the marginal cost of a page of ads zero, too? This assumption, coupled with the financial crisis, has many people questioning the viability of advertising as a long-term revenue strategy. So is advertising dead?

Most web publishers have optimized their products to produce page views because that’s what they think they’re paid to provide. Given that the online advertising pricing model is still largely based on the cost per thousand page views (CPM), such a focus is easy to understand. But since the number of people on the planet and their consumption is finite, advertising should work as the primary revenue model on the consumer web. The key is to focus on the right objective — driving measurable revenue for your advertisers.

Economics 101: Allocative Efficiency

Figure 1 shows the model of perfect competition — the point at which the supply and demand curves intersect and yield maximum aggregate surplus (consumer + producer surplus). Economists define a consumer surplus as the value consumers would pay above actual market price (the blue area under the demand curve). Producer surplus is the value above the marginal cost of production (the pink area above the supply curve). The point at which these two curves intersect is the equilibrium point, which is also known as “perfect competition” and is generally held up — by economists, anyway — as the goal for all markets.

Figure 1: Traditional Supply and Demand Curve

Moore’s Law and “The Audience” Supply Curve

The canonical example of a supply curve illustrated in Figure 1 assumes that the marginal cost per unit of supply increases with respect to quantity.

What if marginal cost approaches zero? That is, what if the supply curve approaches zero, as I have depicted in Figure 2? While the variable cost of software distribution is clearly near zero, people are often quick to point out the economic maxim of “In the long run, all costs are variable.” In other words, fixed costs (like buying and running servers and storage, hiring people to write code and owning buildings where employees to do their work) should really be amortized and added to variable costs, in which case marginal cost is far from zero. But Moore’s Law, open-source software, and globalization are driving the fixed costs of operating and capital expenses (on a unit-amortized basis) to zero, too. In the race between mankind’s ability to consume online content and Moore’s Law, Moore’s Law wins hands-down.

Figure 2: The Audience Supply and Demand Curve

In Figure 2, the supply curve favors gravity over traditional economic logic and approaches zero as quantity approaches infinity. This explains not only why it’s so hard to charge a consumer for web services, but the dominance of advertising-supported services for information-based web properties — and why it appears as if 100 percent of the surplus accrues to consumers (blue area under the demand curve).

What About the Advertising Supply Curve?

Isn’t the logical conclusion, then, that advertising pricing will also approach zero? If advertisers pay based on page views and the marginal cost of an ad view is near zero, how can companies charge $10, $20 or $50 for 1,000 views?

Advertisers ultimately seek profit and use advertising to accomplish the following objectives:

1. Drive near-term purchases.

2. Drive “considered” purchases, like the purchase of a car, by influencing the consumer’s perception with repeated messages over long periods of time.

3. Increase a consumer’s willingness to pay a premium above marginal cost to improve profit margins.

It’s extremely difficult to measure goals two and three. Brand advertisers often aim to achieve all three goals and have various (unscientific) ways to measure performance. The majority of firms that can afford the luxury of spending without actually knowing whether there is a positive ROI are very large firms with large discretionary marketing budgets, a very small percentage of the roughly 40 million businesses out there.

Direct advertisers wouldn’t mind achieving goals two and three, but focus the vast majority of their measurement and optimization on the first objective. These firms can reinvest profits from marketing in more marketing until acquisition costs equal profit because their spend is measurable. Consequently, performance-based advertising is self-funding, which helps to explain the rise of search marketing (and Google).

Until there is a better way to measure the ROI of all three objectives articulated above, the big winners in online advertising will be those that have the capacity to generate a massive quantity of high-quality leads at a low cost.

Display vs. Search Advertising

In an effort to compare the efficiency of lead generation, let’s compare Google’s price and cost per lead to a very rough guesstimate of Yahoo’s non-search business. Please note, however, that I can’t find the data I need to do this well, so this is a back-of-the-envelope analysis that is surely way off. It should only be used for illustrative purposes — nothing more.

According to a recent Morgan Stanley research report, Google generated 11.3 billion paid leads in the first quarter of 2009; according to a recent blog post from Yahoo, that company generates 180 billion page views per month. Let’s assume that all of these pages have one display ad (excludes the very efficient Yahoo Search), that the average click-through rate for Yahoo display is 0.10 percent, and that all costs go against display.

In Table 1, I’ve estimated Google’s cost per lead vs. Yahoo’s non-search cost per lead. Note that Google earns revenue of only 49 cents per lead vs. my estimate of $2.93 for Yahoo’s display advertising. More importantly, the fully amortized cost to Google of providing a lead is 30 cents vs. roughly $2.17 for Yahoo. As the supply of leads increases, prices will likely experience a dramatic aggregate decline. When that happens, anyone who can’t produce supply for a cost under market price will obviously find himself in a bad place. The low-cost provider will expand market share as volume increases and high-cost providers fade away.

Table 1: Back-of-the-Envelope Comparison of Direct vs. Display Price and Cost Position

One of these days someone will figure out how to measure the impact online advertising has on offline purchases, considered purchases, and a consumer’s willingness to pay. When that happens, some publishers will earn an order of magnitude more from advertising, while others will experience massive price deterioration. In the meantime, my money is on lead generation as the best way to build a consumer web content business.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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In 1964, Justice Potter Stewart, who was having difficulty explaining what exactly he meant by “hard-core” pornography, famously said, “I shall not today attempt further to define the kinds of material I understand to be embraced…but I know it when I see it.” When entrepreneurs ask investors what they’re looking for in a startup, the response they get is often something along the lines of “traction.” And when asked to describe traction, most investors channel Justice Stewart, saying only, “I know it when I see it.”

Investors seek to take on the risks they can control and minimize the rest. The biggest unknown variable in the risk equation is usually the market, as in, “Is there even a market for your product?” This is especially challenging when it comes to products aimed at consumers, as you cannot possibly speak with several million people to get their feedback on a product before it’s even launched. So investors want to see enough usage of your product to give them confidence that there is both a big enough market to generate a large exit, and that you have nailed the product/market fit. That is what we mean by traction.

Misunderstanding the objective of traction, a number of Silicon Valley entrepreneurs have mastered the art of driving millions of people to a site or application regardless of whether the product solves a real problem or not. At best, this approach skews the data used to discover if a product is getting the kind of usage that would indicate there’s a big enough market for it. At worst, it’s spam.

Hackers hack everything, not just code

That things would go down this path should have been obvious. I’m not sure who kicked off this hacking traction phenomenon, but a small group of extremely bright entrepreneurs now use a very sophisticated form of multivariate testing. For example, they simultaneously test an extremely large number of variables on a registration page in order to maximize registration (the copy, size, color, font and placement of data fields; the format and text of buttons; the flow of pages), then do the same thing on the “invite friends” page (the text of the body and subject of the invite email), and so on.

This method takes advantage of latent human behaviors to drive registrations and visits — and in terms of those goals, it’s unbelievably effective. While this knowledge is still relatively well guarded, it is slowly making its way through Silicon Valley.

The insight that other pages say more about the pages they point to than such pages say about themselves (PageRank) was a revolutionary idea that led to Google Search. Machine learning has done a great deal to tune search, but machine learning without PageRank would not have resulted in Google.

Similarly, using very sophisticated optimization routines to maximize desired behaviors in a social application is a great way to make a good thing better. However, using these techniques too early can muddy the waters so that you don’t know if what you have is any good or not. If you were going down the wrong path, wouldn’t you want to know that as soon as possible?

Now that these entrepreneurs are returning to investors with “traction,” some are having trouble raising money. They are, understandably, confused. “You asked for traction — here’s your traction. Show me the money!“

Too much of a good thing

Multivariate testing and other optimization schemes can be a great way to make a good product even better, and they are underutilized by many companies. But too many startups have begun misusing such traction techniques as a strategy rather than as a tactic, inadvertently destroying the feedback needed to build a great product.

As for me, I shall not today attempt further to define the kinds of businesses I hope to invest in…but I know them when I see them.

Mike Speiser is a Managing Director at Sutter Hill Ventures. His thoughts on technology, economics and entrepreneurship will appear at this time every week.

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