Silicon Valley Home Prices, Stock Prices & Bitcoin

I’m writing this post with a bit of trepidation, because talking about Silicon Valley home prices these days is a bit dicey. The surge of the last five years has been shocking, and almost no one I know feels good about how difficult it is for people to buy a new home in Silicon Valley in 2017.

So if you need a trigger warning, this is it. Stop reading now.

The truth is, as shocking as the rise in Silicon Valley home prices has been, there has also been an asset boom in other dimensions as well. Total compensation for engineers is up considerably and stock prices at the big tech companies continue to rise.

To visualize this, I thought I’d put together a few charts based on real market data. As a proxy for Silicon Valley, I pulled the last 5 years of home prices from Zillow, and monthly stock price data from Yahoo.

Palo Alto Home Prices

Two days ago, the Mercury News reported that a home in Palo Alto sold for $30 million.  A quick check on Zillow seems to confirm this.

I chose Palo Alto as a proxy for Silicon Valley home prices because it is historically “ground zero” for Silicon Valley tech companies, and it has relatively close proximity to all of the massive tech giants (Apple, Google, Facebook).

I picked June 2012 – June 2017, not only because it is roughly five years, but also it also happens to mirror the time that Facebook has spent as a public company. For many in the local real estate market, correctly or incorrectly, the Facebook IPO still looms as a transformational event.

As you can see, in June 2012 the average Palo Alto home cost $1.38 million. Five years later, the estimate for June 2017 is up 84.6% to $2.55 million.

Apple (AAPL)

Apple is the most valuable company in the world, as measured either by market capitalization ($810B as of 6/7/2017) or by profitability ($45.7B in 2016).  Thanks in part to this exception financial performance, Apple stock (AAPL) has risen 84.5% in the last five years, from $83.43 per share to $153.93 per share.

84.5%? Where have I heard that number before?

That’s right, the increase in Apple stock over the last five years is almost exactly the same increase as the average home price in Palo Alto over the same time period.

In June 2012, it took 16,555 shares of Apple stock to purchase the average Palo Alto home. In June 2017, it took 16,566 shares. (Of course, with dividends, you’re actually doing a little better if you are a shareholder.)

If you look at the chart, the pink line shows clearly the large rise in price for the average Palo Alto home. The blue line is the number of AAPL shares it would take to by the average Palo Alto home in that month. As you can see, AAPL stock is volatile, but five years later, that ratio has ended up in almost the exact same place.

Alphabet / Google (GOOG)

Alphabet, the company formerly known as Google, may not be as large as Apple in market capitalization ($686B), but it has seen far more share appreciation in the past five years. Since June 2012, Alphabet has seen its stock price rise 240.4%, from $288.95 in June 2012 to $983.66 per share.

What does this mean? Well, it means that if you have been fortunate enough to hold Google equity, the rise in Palo Alto home prices doesn’t look as ominous. It took 4,780 shares of Google to purchase the average Palo Alto home in June 2012, but it only took 2,592 to purchase the average Palo Alto home in June 2017.

Facebook (FB)

Facebook, the youngest of the massive tech giants, already has one of the largest market capitalizations in the world. As of today, Facebook is valued at $443B. Facebook stock has risen 394% in the past five years, from $31.10 in June 2012 to $153.63 in June 2017.

To state the obvious, it has been a good five years for owners of Facebook stock. Not many assets could make owning Palo Alto real estate look slow, but 394% growth in five years is unbelievable. In June 2012, you would have needed 44,412 shares to buy the average Palo Alto home. In June 2017, that number had dropped significantly to just 16,598 shares.

Bitcoin (BTC)

While I realize that Bitcoin is not a stock, the original idea for this post came from a joke I made on Twitter recently given all of the buzz about Bitcoin, Ethereum and ICOs over the past few weeks.

I couldn’t resist running the numbers.

For the small number of readers of this blog that haven’t been following the price of Bitcoin, the increase in value over the past five years has been unbelievable.The total value of all Bitcoin outstanding is currently about $44.5B. Since June 2012, Bitcoin has risen approximately 4,257%, from $6.70 per Bitcoin to a current value of $2,858.90.

You can see why there has been so much buzz.

In June of 2012, it would have taken 260,149 Bitcoin to buy the average home in Palo Alto. In June of 2017, that number is now down to 892.

Needless to say, anyone who sold Bitcoin to buy a house in 2012 is likely not loving these numbers. But to people who have held Bitcoin for the past five years, Palo Alto is looking cheaper by the day.

Silicon Valley Is Seeing Significant Asset Inflation

To be clear, I’m not attempting to attribute causality to these charts. I believe the real driver of home prices in Silicon Valley is the lack of sufficient building of new supply at pace with the economy, combined with a significant increase in compensation for technology employees and historically low interest rates.

But the fact is, if you are fortunate enough to have equity in one of the tech giants (or in Bitcoin), houses might actually be looking cheaper now relatively than they did five years ago.

I always find it enlightening to look at real data and compare it to intuition. Hope you find this data and these charts as interesting as I did.

Spend Time Thinking About The People Who Don’t Use Your Product


This is an extension to my original three post series on user acquisition.

Today, AirBnB announced that it had reached a settlement with the city of San Francisco on how to effectively register and monitor legal listings in the city. I am a huge fan of the company, and it seems like a positive outcome for both San Francisco and AirBnB.

For many, the issues around many of the sharing economy companies, including AirBnB, are examples of regulators trying to find a way to both control and incorporate rapid, disruptive innovation.  There is, of course, some truth to this point of view.

However, as a product leader, there is another important takeaway that seems to be too often forgotten. Most of us spend too little time thinking carefully about the people who don’t use our products. 

The people who don’t use your product often won’t show up in your core metrics. But if you don’t spend time understanding them, you will eventually feel the negative effects in your growth and your brand.

It’s Natural for Companies to Obsess About Their Users

When a startup launches a new product, it is natural to obsess with every user it touches. Every click, every tap, every piece of data is precious feedback about your features. The data is one of the most objective sources of information about what your users are doing with your product and when they are doing it. In the early days, before finding product/market fit, a huge amount of time tends to be spent on the people you touch but who don’t convert. In fact, that may be where most people at the company spend their time.

As consumer products find product/market fit and hit escape velocity, more and more engineers and designers spend a disproportionate amount of time on users. The people who work on growth & marketing will still often continue to look at the data on leads, trying to find ways of converting those non-users to users. However, as a percentage of the company, fewer and fewer engineers, designers & product managers will be looking at data from non-users.

This makes sense, of course, because as your product grows, almost all feature development is focused on your users. In 2008, when we established the Growth team at LinkedIn, we discovered that of the hundreds of features on, only three features reliably touched non-users. (For those of you who are curious, those features were the guest invitation (email), the public homepage (, and the public profile (in search.))

Customer obsession, of course, is generally a good thing. But as we learned at LinkedIn, if you want to grow a viral product, you have to spend a considerable amount of time thinking about the non-user, where they touch your brand and your service, and find ways to both reach them and convert them to users.

You Have More Non-Users Than Users

Few brands and products could ever claim that their conversion rate for everyone they touch is over 50%. It is even possible that Facebook, with nearly 2 billion users, still has more people in the world who have heard of the company than who use it.

In 2011, I remember talking to the great founders at CardMunch about a new email they were proposing to add to their service. CardMunch was a wonderful app that made it effortless to scan a business card and then have it automatically entered into your address book, with almost no errors. The proposal was to add an email so that the person whose business card you scanned (non-user) received an email from the CardMunch user with their business card in electronic form.

The team was ready to whip something together quickly and test the idea, and the concept was good in principle. But given some of the experience of Plaxo a decade before, it was prudent to ask the simple question: “How many people will see this new email?” Within a few minutes, we figured out that the number of people who would receive this email within the first three months would be 30 to 50 times the total user base of the application.

Some of you are probably thinking, “sounds like a great growth feature!” Others are likely venting about why we have so many emails cluttering our inboxes. Both reactions are fair.

The guidance I gave the team, however, was to consider the fact that, once they launch this feature, most people who have ever heard of CardMunch will have only heard of it through this email. The product and the brand. I asked them to spend a bit more time on the design on the email, in that context, to ensure that all of their hard work on a wonderful product wouldn’t be drowned in an avalanche of poor experience.

In the end, Sid Viswanathan & team did a great job brainstorming ways that they could show the value of a connected addressbook in the email, including LinkedIn features like people you know in common. Once framed properly, it was simple to think about what they wanted non-users to think about their brand and their product.

Non-Users Matter

Marketers, of course, have known this for decades. It is a brand marketing staple that it takes at least three touches of a brand before it will stick with a potential customer.

Somewhere along the way, software companies lost touch with the basic idea that every piece of content that contains their brand is a potential touch. It is not just the users of the core product that matter for long term growth.

Market research and customer development are often essential for discovering and understanding new potential users for your product. The case can be made that viral systems can, in fact, spread to these new pockets automatically. However, truly viral products are few and far between, and in most cases these new markets will not be in the data sets that your product & engineering teams are focused on.

Brand will also impact your company well beyond new user acquisition. With AirBnB, we now know the many ways in which their service and brand touch non-users. Neighbors, for example, have natural questions and concerns when a house or a unit near by is available on the platform.

Software companies, especially successful ones, tend to have passionate and talented designers and product leaders who are eager to find clever solutions to real user problems. Given the right data and focus, there is no question that these teams can also design and build features that address non-user concerns.

Tesla spends time thinking both about the feeling a driver has in the car, as well as the experience of a non-Tesla owner who is watching that car drive by.

Spend more time thinking about all of the people who touch your product & your brand, not just your users.


The Future of Social Networking at Singularity U

Last week, I was asked to give a guest lecture at Singularity University on the topic “The Future of Social Networking

To frame the discussion, I chose to walk through the following structure:

  • Web 1.0 vs. Web 2.0
  • Social Networking as a disruptive platform
  • LinkedIn as an example of a social platform
  • Mobile as a disruptive accelerator for social platforms
  • Thoughts on future disruptions

On a personal note, I hadn’t actually been back to visit NASA Ames Research Center since my internship during my senior year in high school (21 years ago).  Back then, I was helping develop simulation software for fluid dynamics simulations in Fortran.  Thankfully, no one asked me to code in Fortran during the Q&A.

The team at Singularity U was incredibly gracious, and I appreciated the opportunity to talk to the class.

User Acquisition: Mobile Applications and the Mobile Web

This is the third post in a three post series on user acquisition.

In the first two posts in this series, we covered the basics of the five sources of traffic to a web-based product and the fundamentals of viral factors.  This final post covers applying these insights to the current edge of product innovation: mobile applications and the mobile web.

Bar Fight: Native Apps vs. Mobile Web

For the last few years, the debate between building native applications vs. mobile web sites has raged.  (In Silicon Valley, bar fights break out over things like this.) Developers love the web as a platform.  As a community, we have spent the last fifteen years on standards, technologies, environments and processes to produce great web-based software.  A vast majority of developers don’t want to go back to the days of desktop application development.

Makes you wonder why we have more than a million native applications out there across platforms.

Native Apps Work

If you are religious about the web as a platform, the most upsetting thing about native applications is that they work.  The fact is, in almost every case, the product manager who pushes to launch a native application is rewarded with metrics that go up and to the right.  As long as that fact is true, we’re going to continue to see a growing number of native applications.

But why do they work?

There are actually quite a few aspects to the native application ecoystem that make it explosively more effective than the desktop application ecosystem of the 1990s.  Covering them all would be a blog post in itself.  But in the context of user acquisition, I’ll posit a dominant, simple insight:

Native applications generate organic traffic, at scale.

Yes, I know this sounds like a contradiction.  In my first blog post on the five sources of traffic, I wrote:

The problem with organic traffic is that no one really knows how to generate more of it.  Put a product manager in charge of “moving organic traffic up” and you’ll see the fear in their eyes.

That was true… until recently.  On the web, no one knows how to grow organic traffic in an effective, measurable way.  However, launch a native application, and suddenly you start seeing a large number of organic visits.  Organic traffic is often the most engaged traffic.  Organic traffic has strong intent.  On the web, they typed in your domain for a reason.  They want you to give them something to do.  They are open to suggestions.  They care about your service enough to engage voluntarily.  It’s not completely apples-to-apples, but from a metrics standpoint, the usage you get when someone taps your application icon behaves like organic traffic.

Giving a great product designer organic traffic on tap is like giving a hamster a little pedal that delivers pure bliss.  And the metrics don’t lie.

Revenge of the Web: Viral Distribution

OK. So despite fifteen years of innovation, we as a greater web community failed to deliver a mechanism that reliably generates the most engaged and valuable source of traffic to an application.  No need to despair and pack up quite yet, because the web community has delivered on something equally (if not more) valuable.

Viral distribution favors the web.

Web pages can be optimized across all screens – desktop, tablet, phone.  When there are viral loops that include the television, you can bet the web will work there too.

We describe content using URLs, and universally, when you open a URL they go to the web.  We know how to carry metadata in links, allowing experiences to be optimized based on the content, the mechanism that it was shared, who shared it, and who received it.  We can multivariate test it in ways that border on the supernatural.

To be honest, after years of conversations with different mobile platform providers, I’m still somewhat shocked that in 2012 the user experience for designing a seamless way for URLs to appropriately resolve to either the web or a native application are as poor as they are.  (Ironically, Apple solved this issue in 2007 for Youtube and Google Maps, and yet for some reason has failed to open up that registry of domains to the developer community.)  Facebook is taking the best crack at solving this problem today, but it’s limited to their channel.

The simple truth is that the people out there that you need to grow do not have your application.  They have the web.  That’s how you’re going to reach them at scale.

Focus on Experience, Not Technology

In the last blog post on viral factors, I pointed out that growth is based on features that let a user of your product reach out and connect with a non-user.

In the mobile world of 2012, that may largely look like highly engaged organic users (app) pushing content out that leads to a mobile web experience (links).

As a product designer, you need to think carefully about the end-to-end experience across your native application and the mobile web.  Most likely, a potential user’s first experience with your product or service will be a transactional web page, delivered through a viral channel.  They may open that URL on a desktop computer, a tablet, or a phone.  That will be your opportunity not only to convert them over to an engaged user, in many cases by encouraging them to download your native application.

You need to design a delightful and optimized experience across that entire flow if you want to see maximized self-distribution of your product and service.

Think carefully about how Instagram exploded in such a short time period, and you can see the power of even just one optimized experience that cuts across a native application and a web-based vector.

Now go build a billion dollar company.

User Acquisition: Viral Factor Basics

This is the second post in a three post series on user acquisition.

In the first post in this series, we covered the basics of the five sources of traffic to a web-based product.  This next post covers one of the most important, albeit trendy, aspects of user acquisition: virality.


It’s About Users Touching Non-Users

Look at your product and ask yourself a simple question: which features actually let a user of your product reach out and connect with a non-user?   The answer might surprise you.

At LinkedIn, we did this simple evaluation and discovered that out of thousands of features on the site, only about a half-dozen would actually let a user create content that would reach a non-user. (In fact, only a couple of these were used in high volume.)

I continue to be surprised at how many sites and applications are launched without having given careful thought to this exactproblem.  Virality cannot easily be grafted onto a service – outsized results tend to be reserved for products that design it into the core of the experience.

Useful questions to ask, from a product & design perspective:

  • How can a user create content that reaches another user?
  • How does a users experience get better the more people they are connected to on it?
  • How does a user benefit from reaching out to a non-user?

Understanding Viral Factors

One of the most useful types of metrics to come out of the last five years of social software is the viral factor.  Popularized by the boom of development on the Facebook platform in 2007, a viral factor is a number, typically between 0.0 and 1.0.  It describes a basic business problem that affects literally every business in the world:

“Given that I get a new customer today, how many new customers will they bring in over the next N days?”

“N” is a placeholder for a cycle time that makes sense for your business.  Some companies literally track this in hours, others 3 days, or even 30.  Let’s assume for now that 7 is a good number, since it tells you given a new customer today, how many new customers will they bring in over the next week.

Basic Viral Math

The good news is, once you identify the specific product flows that allow users to reach non-users, it’s fairly easy to instrument and calculate a viral factor for a feature or even a site.  But what does the number really mean?

Let’s assume a viral factor of 0.5, and an N of 7.  If I get a new user today, then my user acquisition will look like this over the next few weeks:

1 + 0.5 + 0.25 + 0.125 ….

It’s an infinite series that adds up to 2.  By getting a new user, the virality of this feature will generate a second user over time.

Two obvious epiphanies here:

  • A viral factor is a multiplier for existing sources of user acquisition.  0.5 is a 2x, 0.66 is a 3x, etc.
  • Anything below 0.5 looks like a percentage multiplier at best.

What about a viral factor of 1.1?

One of the memes that started to circulate broadly in 2008 was getting your viral factor to “1.1”.  This was just a proxy for saying that your product or service would explode.  If you do the math, you can easily see that any viral factor or 1.0 or higher will lead to exponential growth resulting in quickly having every human on the planet on your service.

I don’t want to get into a Warp 10 debate, but products can in fact have viral factors above 1.0 for short periods of time, particularly when coming off a small base.

Learning from Rabbits

The key to understanding viral math is to remember a basic truth about rabbits.  Rabbits don’t have a lot of rabbits  because they have big litters.  Rabbits have a lot of rabbits because they breed frequently.

When trying to “spread” to other users, most developers just focus on branching factor – how many people they can get invited into their new system.  However, cycle time can be much more important than branching factor.

Think of a basic exponential equation: X to the Y power.

  • X is the branching factor, in each cycle how many new people do you spread to.
  • Y is the number of cycles you can execute in a given time period.

If you have a cycle that spreads to 10 people, but takes 7 days to replicate, in 4 weeks you’ll have something that looks like 10^3.  However, if you have a cycle that takes a day to replicate, even with a branching factor of 3 you’ll have 3^27.  Which would you rather have?

In real life, there is decay of different viral messages.  Branching factors can drop below 1.  The path to success is typically the combination of a high branching factor combined with a fast cycle time.

As per the last blog post, different platforms and traffic channels have different engagement patterns and implicit cycle times.  The fact that people check email and social feeds multiple times per day makes them excellent vectors for viral messages.  Unfortunately, the channels with the fastest cycle times also tend to have the fastest decay rates.  Fast cycle times plus temporary viral factors above 1 are how sites and features explode out of no where.

Executing on Product Virality

To design virality into your product, there really is a three step process:

  1. Clearly articulate and design out the features where members can touch non-members.  Wireframes and flows are sufficient.  Personally, I also recommend producing a simple mathematical model with some assumptions at each conversion point to sanity check that your product will produce a strong viral factor, layered over other traffic sources (the multiplier).
  2. Instrument those flows with the detailed metrics necessary for each step of the viral cycle to match your model.
  3. Develop, release, measure, iterate.  You may hit success your first time, but it’s not unusual to have to iterate 6-8 times to really get a strong viral factor under the best of conditions.  This is the place where the length of your product cycles matter.  Release an iteration every 2 days, and you might have success in 2 weeks.  Take 3-4 weeks per iteration, and it could be half a year before you nail your cycle.  Speed matters.

You don’t need hundreds of viral features to succeed.  In fact, most great social products only have a few that matter.

What about mobile?

Now that we’ve covered the five scalable sources of web traffic and the basics of viral factors, we’ll conclude next week with an analysis of what this framework implies for driving distribution for mobile web sites vs. native applications.

User Acquisition: The Five Sources of Traffic

This is the first post in a three post series on user acquisition.

The topic of this blog post may seem simplistic to those of you who have been in the trenches, working hard to grow visits and visitors to your site or application.  As basic as it sounds, however, it’s always surprising to me how valuable it is to think critically about exactly how people will discover your product.

In fact, it’s really quite simple.  There are only really five ways that people will visit your site on the web.

The Five Sources of Traffic

With all due apologies to Michael Porter, knowing the five sources of traffic to your site will likely be more important to your survival than the traditional five forces.  They are:

  1. Organic
  2. Email
  3. Search (SEO)
  4. Ads / Partnerships (SEM)
  5. Social (Feeds)

That’s  it.  If someone found your site, you can bet it happened in those five ways.

The fact that there are so few ways for traffic to reach your site at scale is both terrifying and exhilarating.  It’s terrifying because it makes you realize how few bullets there really are in your gun.  It’s exhilarating, however, because it can focus a small team on exactly which battles they need to win the war.

Organic Traffic

Organic traffic is generally the most valuable type of traffic you can acquire.  It is defined as visits that come straight to your site, with full intent.  Literally, people have bookmarked you or type your domain into their browser.  That full intent comes through in almost every produto metric.  They do more, click more, buy more, visit more, etc.  This traffic has the fewest dependencies on other sites or services?

The problem with organic traffic is that no one really knows how to generate more of it.  Put a product manager in charge of “moving organic traffic up” and you’ll see the fear in their eyes.  The truth is, organic traffic is a mix of brand, exposure, repetition, and precious space in the very limited space called “top of mind”.  I love word of mouth, and it’s amazing when it happens, but Don Draper has been convincing people that he knows how to generate it for half a century.

(I will note that native mobile applications have changed this dynamic, but will leave the detail for the third post in this series.)

Email Traffic

Everyone complains about the flood of email, but unfortunately, it seems unlikely to get better anytime soon.  Why?  Because it works.

One of the most scalable ways for traffic to find your site is through email.  Please note, I’m not talking about direct marketing emails.  I’m referring to product emails, email built into the interaction of a site.  A great example is the original “You’ve been outbid!” email that brought (and still brings) millions back to the eBay site every day.

Email scales, and it’s inherently personal in its best form.  It’s asynchronous, it can support rich content, and it can be rapidly A/B tested and optimized across an amazing number of dimensions.  The best product emails get excellent conversion rates, in fact, the social web has led to the discovery that person to person communication gets conversion person over 10x higher than traditional product emails.  The Year In Review email at LinkedIn actually received clickthroughs so high, it was better described as clicks-per-email!

The problem with email traffic generally is that it’s highly transactional, so converting that visit to something more than a one-action stop is significant. However, because you control the user experience of the origination the visit, you have a lot of opportunity to make it great.

Search Traffic

The realization that natural search can drive traffic to a website dates back to the 90s.  However, it really has been in the past decade in the shadow of Google that search engine optimization scaled to its massive current footprint.

Search clearly scales.  The problem really is that everyone figured this out a long time ago.  First, that means that you are competing with trillions of web pages across billions of queries.  You need to have unique, valuable content measured in the millions of pages to reach scale.  SEO has become a product and technical discipline all it’s own. Second, the platform you are optimizing for (Google, Microsoft) is unstable, as they constantly are in an arms race with the thousands of businesses trying to hijack that traffic. (I’m not even going to get into their own conflicts of interest.)

Search is big, and when you hit it, it will put an inflection point in your curve.  But there is rarely anysuch thing as “low hanging fruit” in this domain.

Advertising (SEM)

The fourth source of traffic is paid traffic, most commonly now ads purchased on Google or Facebook.  Companies spend billions every year on these ads, and those dollars drive billions of visits.  When I left eBay, they were spending nearly $250M a year on search advertising, so you can’t say it doesn’t scale.

The problem with advertising is really around two key economic negatives.  The first is cash flow.  In most cases, you’ll be forced to pay for your ads long before you realize the economic gains on your site.  Take something cash flow negative and scale it, and you will have problems.  Second, you have solid economics.  Most sites conjure a “lifetime value of a user” long before they have definitive proof of that value, let alone evidence that users acquired through advertising will behave the same way. It’s a hyper-competitive market, armed with weapons of mass destruction.  A dangerous cocktail, indeed.

While ads are generally the wrong way to source traffic for a modern social service, there are exceptions when the economics are solid and a certain volume of traffic is needed in a short time span to catalyze a network effect.  Zynga exemplified this thinking best when it used Facebook ads to turbocharge adoption and virality of their earlier games like FarmVille.

Social Traffic

The newest source of scalable traffic, social platforms like Facebook, LinkedIn and Twitter can be great way to reach users.  Each platform is different in content expectations, clickthrough and intent, but there is no question that social platforms are massively valuable as potential sources of traffic.

Social feeds have a number of elements in common with email, when done properly.  However, there are two key differences that make social still very difficult for most product teams to effectively use at scale.  The first is permission.  On social platforms, your application is always speaking through a user.  As a result, their intent, their voice, and their identity on the platform is incredibly important.  Unlike email, scaling social feed interactions means hitting a mixture of emotion and timing.  The second issue is one of conversion.  With email, you control an incredible number of variables: content, timing, frequency.  You also have a relatively high metrics around open rates, conversion, etc.  With social feeds, the dynamics around timing and graph density really matter, and in general it always feels harder to control.

The Power of Five

Eventually, at scale, your site will likely need to leverage all of the above traffic sources to hit its potential.  However, in the beginning, it’s often a thoughtful, deep success with just one of these that will represent your first inflection point.

The key to exponential, scalable distribution across these sources of traffic is often linked to virality, which is why that will be the topic of my next post.

Why Zynga is a Great Business

With the Zynga IPO filing rumored to be hours away, I thought a light hearted blog post might be in order.

There are many aspects to economics behind video games that have been largely unchanged over the past two decades.  Fundamentally, Zynga lept to an opportunity to take advantage of a social platform (Facebook) to challenge some of the fundamental limitations of distribution and monetization that plagued the software giants who dominated desktop and platform gaming.If you need a new gaming mouse check out the best gaming mouse for small hands that might fit you perfectly.

Obviously, I am a fan of the company.  The number of blog posts here about Zynga games should tell you that.  But when people ask me in real life why I’m such a big fan of Zynga, I give them a simple tongue-in-cheek thesis.

Selling Things You Don’t Need

It’s a well know fact that selling people things they don’t need is a great business.   Some might say it’s when retailers and/or products rise higher in the Maslow hierarchy of needs.  By definition, when items rise up that motivation chain, more powerful emotions come into play.  Fundamentally, no one needs a cotton candy tree.  But Zynga gets to the emotions of why you might want one.

In the end, the willingness to pay for things you don’t need is shockingly high in an economy where people have disposable income.

Selling Things You Don’t Need that Don’t Exist

Hundreds of years ago, this was what selling “snake oil” was all about.  Selling something that you don’t need, and that doesn’t exist has always been a great way to make money.  Unfortunately, it also used to be a sure fire path to getting run out of town (and perhaps tarred & feathered in the process).

A little computer icon of a purple cow does not exist, and you don’t need it.  But that doesn’t change the fact that Zynga has found a way not only to make you want it, but deliver it to you with an effective cost of goods sold of approximately zero.

So now we have a high willingness to pay, combined with low friction and low cost of goods sold.

Selling Things You Don’t Need, That Don’t Exist, and That Are Addictive

This might be called the holy trinity of virtual goods, but in the end, this is the most amazing part of the Zynga model.  Certain types of social interaction are clearly pleasurable to people at a fundamental level.  We love the inherent stimulation in getting a response, recognition or even just insight into another human being.  Once we find a path for these interactions, we want more of it.  By leveraging a social platform for its games, Zynga has integrated social stimulation into their economics with outstanding results.

So now we have a high willingness to pay, combined with low friction and low cost of goods sold, with relatively low distribution costs and a high propensity for repeat activity.

Any wonder that I wish I owned Zynga stock?

Congratulations (in advance) to all of my great friends on the Zynga team.