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“Liking” Facebook’s Business Model – The #EconomicMoats Remix

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Facebook Employee Sign Hack: Pride 2015 (Image Credit: Facebook)
Facebook Employee Sign Hack: Pride 2015 (Image Credit: Facebook)

Note: I published “Liking” Facebook’s Business Model on December 26, 20011 at Tekedia. This article updates that discussion by incorporating developments since then. It also folds in discussion of the economic moats that Facebook has developed around its business. Large segments of this article are exactly identical to the post that was published by Tekedia in 2011.

Introduction

The primary purpose of this post was to demonstrate how one might apply the Business Model Canvas in trying to understand Facebook’s business model. Assuming we understand the business model, I then apply the Economic Moats framework to thinking about Facebook.

It is a fair critique to accuse me of playing “Monday-Morning Quarterback” since it is easy to pick on an extreme success like Facebook and use it as an example. However, from my perspective as an early stage venture capitalist who is basically teaching himself the trade that critique ignores at least one benefit of this kind of case study – mainly that it is useful for trying to recreate the path I might have followed in thinking about Facebook had I been introduced to Mark Zuckerberg in 2004 when he was raising his first outside capital from investors. Think of this as the self-taught early stage venture capitalists’ version of working in a science laboratory, trying to recreate the experiments and reproduce the results that have brought us the advances of the past. Such work is what lays the groundwork for original scientific discoveries in the future.

Also, I should point out that I do not have direct access to inside-information about Facebook’s early days. This case is constructed on the basis of information, reports, and data that are in the public domain.

Okay, with those disclaimers out of the way . . . On with our case study.

According to Michael Rappa; “In the most basic sense, a business model is the method of doing business by which a company can sustain itself – that is, generate revenue. The business model spells-out how a company makes money by specifying where it is positioned in the value chain.” Alex Osterwalder and Yves Pigneur say that; “A business model describes the rationale of how an organization creates, delivers and captures value.”

What problem does Facebook solve?

My son’s paternal grandparents live in Nigeria, as does his uncle – my younger brother. His aunt – my younger sister lives in Ghana. His grandfather has never met him, nor have his uncle and aunt. His grandmother paid him a visit for three months soon after he was born. He was only two months old when she visited.

I had been asking myself the question; “How can I ensure that his grandparents, his uncle, his aunt and other members of his extended family do not miss out on his childhood entirely?” My desire to answer that question in a comprehensive way helped me to overcome my objections to Facebook. I joined Facebook in November 2009.

Facebook enables its users to connect with one another through the company’s social networking online portal. Users connect socially with their “friends” in a “social-network” to share status updates, articles, videos, music, photographs and other content through Facebook.

Facebook’s users can interact with one another in a number of different ways:

  • Users can connect directly as “friends” – this allows the highest degree and freedom of interaction subject to privacy controls that each user can put in place to govern their activity on Facebook.
  • Users can connect to one another as subscribers/followers – this is a one-way connection. Subscribers will see and can comment on the public posts by the person to whom they have subscribed. This feature was a recent addition when I wrote the original post in 2011.
  • Users can interact with one another through Facebook Messenger, an instant messaging app that has evolved since the function was first introduced to Facebook’s users in 2008. 
  • Facebook acquired Instagram in 2012 Instagram built a social network for sharing photos.
  • Facebook acquired WhatsApp in 2014. WhatsApp is a mobile instant messaging app that is popular in developing markets.
  • Facebook acquired Oculus VR in 2014. Oculus VR is  a virtual reality technology startup. 

Founded in 2004, Facebook’s mission is to give people the power to share and make the world more open and connected. People use Facebook to stay connected with friends and family, to discover what’s going on in the world, and to share and express what matters to them.

– Source: Facebook, as of March 2016

The following list highlights some of Facebook’s features:

  • User profiles and homepages – users post status updates on their homepage or wall.
  • Messages, Chat and Social Hangouts (video chat).
  • Photos + Videos – users can tag one another in photos and videos.
  • Games + Apps – people can play games with one another, or share other information through specialized apps.
  • Groups and Pages – people can form a group or create a page for sharing information around an issue of interest.
  • Events – people can use Facebook to plan events and invite others to participate.
  • Credits – this is the virtual currency for transactions on Facebook.

Reports in the press suggest that Facebook has about 800 million active users around the world. An active user is a user who has returned to Facebook’s website within 30 days.

ComScore reports that 82% of the world’s 1.2 billion online population participates in some form of social networking. Social networking eats up 20% of the time people spend online. Facebook’s users account for 75 percent of the time spent on social networking websites. Facebook’s users also account for more than 14 percent of the time people spend online around the world.

December 2015 Update: 

Monthly Active Users: 1.59 billion monthly active users as of December 31, 2015

Daily Active Users: 1.04 billion daily active users on average for December 2015

Mobile Monthly Active Users: 1.44 billion mobile monthly active users as of December 31, 2015

Mobile Daily Active Users: 934 million mobile daily active users on average for December 2015

How Does Facebook Make Money?

Facebook does not charge its users a sign-up or monthly fee. So, how does Facebook make money if users like me get to use it for free? There are three sources of revenue for Facebook;

  • Advertising – Facebook can deliver targeted ads to its users based on information that they provide during sign-up or as they interact with their friends.
  • Games + Apps – Facebook is paid a 30 percent fee by companies that develop games and applications for its user base. This fee is applied to in-game or in-app sales.
  • Virtual Goods – Facebook earns a slice of revenue from the sale of virtual goods to its users.

Reports in the press speculated that Facebook’s 2011 revenue would be in the neighborhood of $4.5 Billion. Advertising should account for the majority of that amount, followed by revenue from games and apps. Virtual goods account for only a small portion of Facebook’s revenues.

March 2016 Update: Revenue for the full year 2015 was $17.93 billion, representing an increase of 44% over revenue for the full year 2014.

The Business Model Canvas – The Building Blocks of Facebook’s Business Model

Note: Business Model Generation was not published till July 2010, nearly 6 years after thefacebook.com launched. Still, using the business model canvas to analyze Facebook’s business Model is instructive.

Customer Segments

  • Mass market – any one that uses the internet and wants to connect and socialize with family, friends and other people that are online.
  • Advertisers – big, medium and small companies that wish to advertise to the hundreds of millions of people that spend time on social network websites. Reports estimate that people spend about 3 to 4 times as much time on Facebook as they spend on Google.
  • Developers – apps, social games, and virtual goods.

Value Proposition(s)

  • Enable users to connect and share with family, friends and other people with whom they share a common interest.

Channels

  • Website
  • Mobile App

Customer Relationships

  • Network effects – users will gravitate to the social network where most of their friends are already users.
  • Relatively high switching costs – users are less likely switch to a competitor after sharing a lot of content on Facebook.

Revenue Streams

  • Advertising – fees generated from online display banner ads delivered to users through Facebook.com. There are probably two or three different categories of advertising.
    • Not entirely clear if this will work, but the team has been pitching this to advertisers since it was two months old. Might need to verify this assumption with someone in the advertising industry.
  • Facebook Credits – 30 percent share of in-app and in-game transactions.
  • Virtual goods – straight virtual goods sales not connected to use of an app or a game by the user.

Key Resources

  • People – employees, and Facebook’s more than 800 million active users.
  • Technology – software, servers and other cloud-based services that Facebook must purchase from other companies to support its operations.
  • Brand – people have to trust in what Facebook represents.

Key Activities

  • Developing and improving the Facebook platform – both the frontend user experience and backend data processing capacity. The company was reported to have started working on proprietary server designs to support its operations – reports suggested the company might be worried about the speed at which conventional server designs allow it to serve content to its millions of daily users.

Key Partners

  • Third party developers – apps, games and other features to enable people connect and share with one another using Facebook’s platform.

Cost Structure

  • Employees – Facebook reportedly has between two and three thousand employees spread across offices in 15 countries. The company seems to be preparing for a burst of growth in the size of its workforce.
  • Technology – server maintenance, software latency and optimization issues; this will continue to be a concern as people generate and share more and more content using smartphones.

The company says that more than 50% of its more than 800 million active users log onto Facebook on any given day. Nielsen estimated in a report on social media that American internet users collectively spent more than 53 billion minutes on Facebook in May 2011. The average user has 130 Facebook friends. The company also says people interact with more than 900 million objects on the website and that the average user is connected to 80 community pages, groups and events. On the average day Facebook’s users upload 250 million photos. Facebook is available in 70 languages, and 300,000 users helped to translate the site by using Facebook’s translations app. On the average day, Facebook’s users install apps 20 million times. During the average month, half a billion people use an app on Facebook or experience the Facebook platform on other websites (e.g. to share this story from Tekedia with your friends on Facebook). In all more than 7 million apps and websites are integrated with the Facebook platform. There are 475 mobile operators globally working to promote and deploy Facebook’s mobile products through their mobile networks and on their mobile devices (for example Facebook’s Android, iOS and Blackberry apps). More than 350 million active users currently access Facebook through a mobile device.

Facebook was launched in February 2004. As the preceding paragraph clearly demonstrates, the over-arching elements of Facebook’s business model that we have discussed have led it to unbelievable success. This success has occurred in spite of the fact that Facebook was not the very first social networking company. MySpace launched in August 2003, and before that Friendster was founded in 2002. Classmates.com, SixDegrees.com and Makeoutclub.com preceded Friendster. One may argue that Facebook benefited from technological advancements that its predecessors could not exploit. One may also argue that Facebook launched at a time when millions of people had become accustomed to the concept of social networking. I suspect there’s a lot of truth in both of those arguments. However, I would also argue that Facebook did a better job of understanding the intricacies of its business model better than its predecessors, and then executing that business model more effectively than any of its predecessors. Put those three arguments together and one can see that Facebook’s phenomenal growth is not completely outside the realm of possibility.

Facebook Menlo Park HQ (Image Credit: Facebook)
Facebook Menlo Park HQ (Image Credit: Facebook)

Economic Moats Analysis

I am now going to pretend that I have travelled back in time, to September 2004. As fortune would have it I am a reasonably well-liked early stage VC who invests in startups raising their very first round of capital from institutional investors. Someone I know has introduced me to the founder of The Facebook; and describes it as “a web directory that the college-kids are going crazy about.” I agree to meet in two weeks, when some time opens up on my schedule. In the meantime I start doing some cursory reading about this “thing.”

The issues I am most concerned about are, in order of priority;

First, how do I know that thefacebook.com has proven that its value proposition will hold? Around that time reports in the press highlighted how addictive thefacebook.com had become to its users. Here are some examples:

Even those who don’t know why they love Thefacebook can’t stay away.

“It’s a stupid, stupid website, but I am completely addicted,” freshman Emily Bruckner said. “I just go around and look at all of my friends and see who they’re friends with. It’s like a contest to see who has the most friends.”

Source: Thefacebook.com Opens to Duke Students, Duke Chronicle; April 14, 2004.

Value Proposition – The bottomline: Users love thefacebook.com, and there is plenty room for growth. The are millions of college students around the world that thefacebook could target as users.

Second, do I have a sense of how the team intends to grow the business? Will the team’s ideas about growth work? Based on reports in the press, it appears thefacebook is growing rapidly, and so I have to assume the team has figured what it will take to grow within the market on which it has chosen to focus initially. There may yet be some work to do here, but so far so good. Each user is encouraged to invite some friends upon first signing up for thefacebook, and the website also suggests people that new users might know who are already on the site. Friendster is doing well within the general population, so there’s one example of how thefaceboook too might grow beyond college-campuses . . . when that makes sense.

Growth – The bottomline: The team seems to have figured out a method to accelerate growth on college campuses. That’s a good sign. There may be a few outstanding questions, but this is probably a good point at which to consider making a investment if growth can continue to accelerate.

Third, and finally . . . How does the team believe thefacebook.com will make money? This is a critical question since it speaks to thefacebook.com’s future prospects for becoming a self-sustaining entity. The team has been pitching itself as an online marketing service to advertisers . . . It will be interesting to see how advertisers react to this.

Revenue – The bottomline: Murky. Not clear if this will work. But Google is having success selling ads online through its Content Targeting Advertising. So not out of the realm of possibility. But no definitive answers at hand.

Economic moats help early stage technology startups preserve and enhance the advantages they enjoy over their competitors as time goes on their business model matures. There are five ways in which a startup can build an economic moat; Network Effects, Switching Costs, Efficient Scale or Cost Advantages, Intangibles, and Brand. Note, that I discuss “brand” under the heading of “Intangibles” but it stands alone as one of the 5 sources of an economic moat.

  • Brand: High. Becoming known as the platform for college students for intra- and inter- college social networking. Highly sought after by students at colleges where it is yet to launch a community.
  • Network Effects: High. Platform gets more useful for users as more of their friends sign-on to become users.
  • Efficient Scale or Cost Advantages: High. Users invite friends. Word-of-mouth seems to be spreading and helping keep costs of acquiring new users relatively low.
  • Switching Costs or Buyer Lock-in: Undetermined. Need more data. But should increase as people interact more and more on thefacebook.com. Described as addictive. Wonder how long that addictive quality will last. Need to get better understanding of user-perception of value.
  • Intangibles:
    • Intellectual Property: None necessary right now, but might be needed in the future to solve technical problems caused by growth. TBD.
    • Research and Development: Need to figure out revenue model. Also, what problems has Friendster run into that might be relevant for thefacebook.com? How is the team thinking about this?
    • Culture and Management: TBD. Young team, college students. Mark has prior experience building social-networking applications.

Conclusion: So, would I have invested? I do not know. There is more that goes into a decision like that one than the preceding analysis. However, at first blush there are no “smoking-gun” reasons not to take a closer look. To avoid saying no at this stage it would help to keep the following lessons in mind – they are adapted from Andrew Chen’s discussion about his decision to pass on making an early stage investment in Facebook when he had the opportunity to do so after he convinced himself that “Facebook would never be a billion dollar company.” Note: I wrote about this in April 2012 in a post that was published at Tekedia. The following discussion is adapted from that post.

  • Lack of experience and lack of knowledge are two distinctly different things. Do not confuse them with one another. Pass on making an investment because of a lack of knowledge, do not pass only because of a lack of experience.
  • Do not take solace in data and statistics without first verifying that such analyses are relevant within the given context. Data and statistics often inspire unjustified confidence, but calculations are useless if what you are calculating is wrong, irrelevant or simply inapplicable to the startup’s situation and future markets.
  • Do your own homework. Treat data and statistics from others with extreme skepticism. At the very least, try to interpret third party data based on your own analyses.
  • By definition, your past experience might be useless in understanding the most promising startups that you will encounter. Start from first-principles. Understand the fundamentals of what the startup is trying to do before you leap to conclusions grounded in your past experience. Don’t let your professional history and learned logic become a hindrance.
  • Business models matter, but execution matters more than the relative attractiveness or unattractiveness of the business model that exists at the time you encounter and early stage startup.
  • Heuristics are useful, but only up to a point. See the point on “past experience” above.
  • Keep an open mind, peel away the layers . . . Lack of conformity with the stereotypes you have become familiar with is an insufficient reason for passing on a startup.
  • Lastly, set all the analyses aside and spend some time thinking about what would happen if the team succeeds in accomplishing what it is setting out to do. If that happened, is that a story you’d want to be part of?

 

 

A Note On Viral Marketing – Part IV: Examining The Widely Used Skok-Reiss Virality Model

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Viral growth in users, over time. The virality formula is attributed to Stan Reiss by David Skok.
Viral growth in users, over time. The virality formula is attributed to Stan Reiss by David Skok.

This post is the fourth in a series I am devoting to the examination of viral marketing. ((Any errors in appropriately citing my sources is entirely mine. Let me know what you object to, and how I might fix the problem. Any data in this post is only as reliable as the sources from which I obtained them.)) I tried to define the term in Part I, and examined how Hotmail and Dropbox each grew, in Part II and Part III respectively.

The formula in the image above is widely used to model the growth of users of a website or an app. It has been popularised through a series of posts by David Skok. ((For example: The Science Behind Viral Marketing, Sep. 15th, 2011 and Lessons Learned – Viral Marketing, Dec. 6th, 2009. Accessed online on Jul. 23rd, 2014. )) Kevin Lawler explained how the formula is derived. ((A Virality Formula, Dec. 29th, 2011. Accessed online on Jul. 23rd, 2014.)) Furthermore, Andrew Chen and others, investors and entrepreneurs alike, have written several blog posts about virality and viral marketing that build on this formula.

In this post I will state the problem that one is trying to solve when one sets out to model viral growth. Then I will examine this formula within that context. Valerie Coffman has already done a great job of examining the flaws in this formula. ((4 Major Mistakes in The Current Understanding of Viral Marketing, Jan. 17th, 2013. Accessed online on Jul. 24th, 2014)) For the most part I will reiterate points that she has already made in her post.

The modeling problem: The fundamental research questions one wants to answer by modeling the viral growth of an app, website or some other digital product are these: ((Adapted from Vynnycky, Emilia; White, Richard (2010-05-13). An Introduction to Infectious Disease Modelling (Kindle Locations 930-931). Oxford University Press, USA. Kindle Edition.)) If one person in a population of potential users adopts a product, how will the average number of users of that product change over time? How large could that user base ultimately become? What factors influence the growth of the number of users over time?

The model above, which I will call the Skok-Reiss Virality Model, uses the following variables in modeling viral growth; t represents time, the function U(t) represents the number of users at a specific time and U(0) is the number of users at the outset, K represents the viral coefficient, p represents the cycle time, the amount of time it takes a new user to try a product and then send out invitations to other potential new users, I represents the number of invitations each new user sends out, and C represents the rate at which people who receive a new invitation convert to become actual new users of the product. The quotient t/p represents the number of invitation cycles that occur within each unit of time. ((For example a monthly unit of time represents 4 cycles if the cycle time is one week.))

A key variable in the Skok-Reiss Virality Model is the viral coefficient, K. The best definition of that variable as it is applied in viral marketing is given by Eric Ries:

Like the other engines of growth, the viral engine is powered by a feedback loop that can be quantified. It is called the viral loop, and its speed is determined by a single mathematical term called the viral coefficient. The higher this coefficient is, the faster the product will spread. The viral coefficient measures how many new customers will use a product as a consequence of each new customer who signs up. Put another way, how many friends will each customer bring with him or her? Since each friend is also a new customer, he or she has an opportunity to recruit yet more friends. ((Ries, Eric (2011-09-13). The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses (Kindle Locations 3008-3012). Random House, Inc.. Kindle Edition. ))

The Skok-Reiss Virality Model has a number of limitations.

First, the model does not specify the size of the market. Phrased another way, in the long run how many people are favorably predisposed to adopting this product once they have been exposed to it by someone they know? As Valerie Coffman points out this is not an inconsequential question because viral growth quickly leads to market saturation. Market saturation in turn reduces the viral coefficient. In more concrete terms, the more time elapses and the larger the proportion of people who have already heard about a product but have not yet become users of that product, the less likely it is that such people will remain as susceptible to becoming new users of the product as they were at the outset of the process. In a sense, exposure without adoption leads to immunity to future adoption. Intuitively, we would expect that market saturation imposes a limit on how large U(t) can become as t becomes infinitely large. However, as it has been formulated, the Skok-Reiss Virality Model suggests that U(t) becomes infinitely large as t approaches infinity. ((To see this; Assume K > 0, p > 0, and U(0) > 0. Then substitute increasing values of  t into the formula.))

Second, the model assumes that the market is one in which people adopt a product and then use that product for ever. In Valerie Coffman’s words the Skok-Reiss Virality Model assumes that “there’s no churn in the customer base – once a customer, always a customer.” In reality the market is more likely to be one in which certain people might initially become users of the product, but then abandon it at some point in the future. ((Also, certain people might stumble upon the product without an invitation.))

To understand why the Skok-Reiss formulation is problematic in this sense one needs to understand and be able to describe three types of populations. A hypothetical population is one that is completely made up for the purpose of studying a specific research question. A hypothetical population typically does not reflect reality. A closed population is one in which there is no entry or exit. In certain instances a closed population is defined such that changes to the size of the closed population occur only through birth or death. In other instances a closed population is defined such that birth and death do not occur. An open population is one in which population growth is affected by birth, death, immigration and emigration. The concept of churn is important because it makes it possible for a model of viral growth to more closely resemble reality by making assumptions about; birth – an existing user brings in more users, death – existing users who adopted the product through an invitation abandon the product, immigration – new users stumble upon the product and adopt it without an invitation from an existing user, and emigration – existing users who adopted the product without an invitation abandon the product.

These two problems with the Skok-Reiss Virality Model make it unlikely that the model produces sufficiently reliable answers to the first two research questions that people modeling viral growth seek to answer.

Third, the model assumes that each user sends out a single batch of invitations after a period of time p. The assumption that each user sends out a single batch of invitations is suspect. Rather, when a user first encounters the product and enjoys the initial first few interactions with the product that user will probably send the first batch of invitations to only a few close friends and  relatives. As time progresses and the user becomes more trusting of the product’s developer the user might then send a subsequent batch of invitations to a wider circle of friends and social acquaintances. Eventually the circle of people that the user sends invitations to might grow to include professional and business associates. Finally, it will get to the point where that specific product or others like it are so widely known that the average user does not send out invitations. This is the point of market saturation, at which the researcher would expect to start seeing a decline in the viral coefficient. It is not also clear that the first invitation as well as subsequent invitations, if the model accounted for them, happen at the same frequency. ((Some models of how infectious diseases spread within a population often account for an incubation period, an infectious period, and a pre-infectious or latent period.))

Last, the Skok-Reiss model makes the error of assuming that two very different processes that form the basis for viral growth happen on a similar timescale. To use Valerie Coffman’s words, the model assumes synchronicity when it should not. The first process is that by which individual users of the product attract new users by word of mouth and through in-product invitation mechanics. The variable in the Skok-Reiss model that reflects this phenomenon is the cycle time. Though as we have pointed out, the way it is formulated falls short of adequately reflecting what one might intuitively expect to observe in reality. The second process is that by which the product’s total user base experiences significant jumps in size. Over time the nature of the growth that this process leads to is seen to resemble exponential  or compound growth. This process is driven by actions of the product developer that differ from, but complement, the actions of individual users in the first process. ((For example; marketing, PR, press related to product updates, content marketing with calls to action directed at potential new users who might want to sign up for the product without the benefit of an invitation from an existing user, presentations at conferences etc.)) The Skok-Reiss model does not adequately differentiate between these two different but complementary processes.

As a result discussions about tactics for achieving viral growth might be flawed, and could lead to disappointing results if they are based on a naive understanding of the Skok-Reiss Virality Model. Indeed, it is often suggested that cycle time is the most important lever that one should focus on in order to achieve viral growth. In David Skok’s words;

Shortening the cycle time has a far bigger effect than increasing the viral coefficient!

Let’s examine that statement with some algebra.

First, what would we expect to happen to U(t) if we let p become infinitesimally small and hold everything else constant? As the back of the envelope analysis below suggests we expect the number of users at any given time to become infinitely large as we make the cycle time infinitesimally small. So far so good.

What happens if we make cycle time infinitessimally small?
What happens if we make cycle time infinitessimally small?

Second, what would we expect to happen to U(t) if we let K become infinitely large and hold everything else constant? As the back of the envelope analysis below suggest we expect the number of users at any given time to become infinitely large as we make the viral coefficient infinitely large.

What happens if we make the viral coefficient infinitely large?
What happens if we make the viral coefficient infinitely large?

This bears repeating. There are at least two ways to make the number of users at any given point in time infinitely large. One approach focuses on cycle time and tries to make that as small as possible. The other approach focuses on the viral coefficient and tries to make that as large as possible. Which one should the product developer focus on? That depends. Certain products lend themselves to the approach that focuses on cycle time as the lever. Youtube is a great example, one that David Skok himself uses to make his argument for focusing on cycle time as the driver of viral growth. ((Messaging apps as a family might fall within this camp as well. Examples; WhatsApp, Viber, Kik, KaKaoTalk, Line, WeChat, Momo etc.)) Other products lend themselves more to the approach that focuses on the viral coefficient. Dropbox comes to mind as a product for which it would make much more sense to focus on the viral coefficient as the lever that drives user growth. ((It is important to reiterate that neither cycle time nor viral coefficient need to remain constant over a product’s lifetime. In fact, one would argue that there ought to be a team of people whose sole focus is designing ways to reduce the cycle time and increase the viral coefficient.))

A third driver of viral growth exists, and it is not given enough emphasis in the Skok-Reiss framework. Churn. There are two types of churn. The first type is the instance of the user who signs up for the product, but uses it so infrequently that ultimately that user’s contribution to the growth in total users is negligible. Tactics should be devised to increase that user’s engagement with the product. The second type is the instance of the user who abandons the product altogether soon after adopting it. Efforts should be made to minimize this occurrence. Managing churn is critical because it gives the team of people working on tactics to minimize cycle time or maximize viral coefficient room to run experiments and determine which tactics will work best in accelerating growth in the user base, ultimately compensating for a product’s initially unfavorable cycle time and viral coefficient if that is the situation in which a product finds itself after it has been been launched. Pinterest is often cited as a product that started out with a small viral coefficient and a small user base. ((I have actually heard the argument “Our viral coefficient is higher than Pinterest’s at this stage in their development.” in two or three pitches. An example of discussions about Pinterest are; Steve Cheney, How To Make Your Startup Go Viral The Pinterest Way. Accessed at Techcrunch on Jul. 27th, 2014. You can examine the raw data here. There’s also this discussion on Quora: Why Did It Take Pinterest Such A Long Time To Go Viral? Accessed Jul. 27th, 2014.))

The Skok-Reiss Virality Model is most frequently discussed amongst investors and startups interested in the topic of viral marketing and viral growth, but it is by no means the only one.  In my next blog post on this topic I will examine an approach discussed by Rahul Vohra in a series of posts on LinkedIn, and I will compare his approach to the Skok-Reiss model. After that I will delve into the Bass Model of Technology Diffusion. I’ll wrap up this series on viral marketing by following Valerie Coffman’s footsteps once more by looking to infectious disease modeling for some pointers regarding how one might fix the flaws in the Skok-Reiss model.

Model’s are useful as a guide to the researcher’s thought process, but it is the researcher’s responsibility to examine each model for flaws and weaknesses and then to devise ways to compensate for them in order to reduce the possibility of forecasts that contain large errors.

A Note On Viral Marketing – Part III: How Dropbox Grew

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Dropbox is another example of a product that has experienced remarkable growth since its launch. In this case study I will explore how Dropbox has achieved such rapid growth and try to identify strategic themes that other startups might consider for experiments centered around user acquisition and revenue growth. ((Any errors in appropriately citing my sources is entirely mine. Let me know what you object to, and how I might fix the problem. Any data in this post is only as reliable as the sources from which I obtained them.))

What is Dropbox? Dropbox is a personal cloud storage service. It provides users with a mechanism for storing files in a folder on the Internet and accessing that folder through an installed client, a website, or a mobile app, on different computing devices. It was founded by Drew Houston and Arash Ferdowsi in 2007. A number of articles online suggest that Dropbox started with an initial base of about 2,000 users. It launched to the public in September 2008 ((According to this presentation by Drew Houston Dropbox had 100 thousand users by the time it launched to the public in September 2008.)). Here are some indications of how much Dropbox has grown since then:

  1. It had about 200 million worldwide users in September 2013 ((See: http://www.cnet.com/news/dropbox-is-like-microsoft-in-the-90s-says-startups-ceo/. Accessed on March 25th, 2014)), and
  2. By February 2013 its users were saving about 1 billion files every day to Dropbox ((See: http://www.cnet.com/news/dropbox-clears-1-billion-file-uploads-per-day/. Accessed on March 25th, 2014.))

How does Dropbox make money? Dropbox operates a freemium business model. The Basic plan is targeted at individuals, and provides 2 gigabytes of cloud storage for free. One can get more storage by inviting one’s friends to Dropbox. The Pro plan provides 100 gigabytes of storage for a monthly subscription of $9.99. The Business plan is designed for 5 or more users, comes with as much storage as needed, and includes other features that are not part of the Basic or Pro plan. According to reports in the press Dropbox started out with about 2,000 users or so.

How did Dropbox grow its user base? ((KISSmetrics discusses this topic here: http://blog.kissmetrics.com/dropbox-hacked-growth/. Accessed on March 26th, 2014.))

  1. Explaining With Video: In the summer of 2009, Dropbox worked with Common Craft to create an explainer video ((You can watch a version of that video here: http://www.commoncraft.com/dropbox-case-study-explanation. Accessed on March 25th, 2014.)) that played a central role in the redesign of Dropbox.com. After the redesign a visitor to the front page of Dropbox.com could watch the video, and sign up. That’s it. At this stage, Dropbox had about 2 million users. ((Dropbox closed a Series A round of financing in November 2009. Accel Partners and Sequoia Capital invested in that round.)) By April 2011 its user base had grown to 25 million. ((This video discussion emphasizes the key role demo videos played in helping Dropbox grow its number of users early in its life: http://techcrunch.com/2011/11/01/founder-storie-how-dropbox-got-its-first-10-million-users/. Accessed on March 26th, 2014.))
  2. Getting Started: Dropbox has a very simple signup process, and an easy user interface that makes it easy for new users to become familiar with the product and how to use it. New users also get an extra 250MB of storage for taking a tour of Dropbox in order to learn about its basic features.
  3. Encouraging Word of Mouth Virality: Dropbox gives users an incentive, and better tools to spread news about the product through word of mouth. Users are rewarded with extra storage capacity when their friends sign up using the referral link that Dropbox gives for email referrals. According to Drew Houston referrals led to a permanent 60% increase in signups. The referral program has a two-sided incentive. The user gets 500MB of storage if a friend signs up, and the user’s friend also gets 500MB of storage for signing up. The program was put in place in April 2010. Dropbox users sent 2.8 million direct referral invitations in the 30 days after the program was implemented. ((Drew Houston, Dropbox Startup Lessons Learned. Accessed at: http://www.slideshare.net/gueste94e4c/dropbox-startup-lessons-learned-3836587 on March 26th, 2014.))
  4. Tying in Social Media: Users are also incentivized to connect their social media accounts – 125MB for connecting a Facebook account, another 125MB for connecting a Twitter account, and an extra 125MB for following Dropbox on Twitter. Users also get 125MB of extra storage for communicating with Dropbox about “why you love Dropbox.” ((See: https://www.dropbox.com/getspace for a list of the incentives Dropbox offers its users.))
  5. Focusing The Message – Simplicity: Dropbox has emphasized simplicity above all else in its communication with existing users, potential users, and in the design of its user experience. That focus has helped it succeed in a very crowded space that includes some large players like Google Drive, Microsoft OneDrive (formerly SkyDrive), Apple iCloud, and other competitors like Box ((Box just filed an S-1 with the SEC for an IPO later this year. You can read the prospectus here: http://www.sec.gov/Archives/edgar/data/1372612/000119312514112417/d642425ds1.htm#toc642425_4. Accessed on March 26th, 2014.)), SugarSync, Evernote, SendThisFile, Carbonite and many others.
  6. Generating PR Through User Engagement: Dropbox engaged with its existing users and potential new users through Dropquest, a scavenger hunt and series of puzzles that culminate with winners earning various prizes from Dropbox. The prizes include free storage and other items from Dropbox. In 2012 everyone who completed the challenge won at least 1GB of free space. Dropbox recommended that participants in Dropquest download and install the desktop application. ((I could not find an announcement about a 2013 version of Dropquest. Perhaps it has been discontinued.))

There’s a debate about the growth Dropbox has experienced? Is it viral or not? ((See for example: http://www.bullethq.com/blog/dropbox-the-viral-lie-sold-to-every-statup/. Accessed on March 26th, 2014.)). There’s also a concurrent debate about “growth hacking” and whether it is as useful as its proponents would have us believe. ((See, for example: http://techcrunch.com/2014/03/22/the-real-engines-of-growth-on-the-internet/. Accessed on March 26th, 2014.)) Does it really matter? I think these are dogmatic positions adopted by the protagonists in the debates taking place about how Internet startups achieve growth. Whatever your position, there’s one observation that no one can argue with; It’s hard to devise a strategy to grow the number of users for a product that none wants to use.

In the next set of posts in this series I will examine a number of mathematical models related to viral marketing – we’ll start with the model most commonly used when people speak about viral marketing.

 

A Note on Viral Marketing – Part II: How Hotmail Grew

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Hotmail is one example of a product that spread through the use of viral marketing techniques. This case study will cover the early days of hotmail, explore some of the underlying factors that led to its spread, and examine one model that has been used to model growth of its number of users. ((Any errors in appropriately citing my sources is entirely mine. Let me know what you object to, and how I might fix the problem. Any data in this post is only as reliable as the sources from which I obtained them.))

Ray Tomlinson is credited with inventing email as we know it today. Before 1972, email could only be sent between users of the same computer. The problem became more complex once different computers were connected to one another to form a network, and a user on one computer wanted to send email to users on a different computer. Important contributions to the evolution of email were made by others, and commercial email packages began to appear in 1976. ((Ian Peter, The History of Email. Accessed at http://www.nethistory.info/History%20of%20the%20Internet/email.html on Jan 17, 2013.))

Sabeer Bhatia and Jack Smith met at Apple Computer in the early 1990s, and later joined a startup called Firepower Systems. In 1995 they started discussing the idea of building a startup themselves. Their first idea was to build a database on Sun’s Java technology. They called it JavaSoft. Venture capitalists turned them down. During the period when they were working on JavaSoft, they encountered a number of obstacles that prevented them from communicating freely with each other. Jack Smith developed a system that allowed them to have their email displayed on a web page. This became the basis for Hotmail. They soon obtaind $300,000 in funding from DFJ and rounded up an additional $100,000 in additional capital. This was in early 1996. The funding terms ascribed Hotmail an implied valuation of $2,000,000. ((Oliver A. Hugo and Elizabeth W. Garnsey, Hotmail: Delivering E-mail to the World, http://doczine.com/bigdata/1/1370291311_60c0e3de77/4e7-hotmailcase26apr02.pdf. Accessed on Jan. 26th, 2014.))

At the urging of the venture capitalist’s backing Hotmail, Bhatia and Smith did two things. First they struck a strategic relationship with Four11, another DFJ portfolio startup which ran “the most comprehensive ‘people finder’ on the Internet” at that time according to PC Magazine. Second, they automatically included the text “P.S. I love you. Get your own free Hotmail at www.hotmail.com” at the end of every email that was sent by a Hotmail user. ((There seem to be variants of the exact message that was appended to the end of each email, but it is consistently reported that a message was included with every email sent from Hotmail.))

Hotmail launched in July 1996, with 100 signing up in the first hour. By September it boasted 100,000 subscribers. That number rose to 1,000,000 by January 1997, and 8,000,000 by October. Though Hotmail had ran out of cash before it launched its email service to the public, it went on to raise additional capital from venture capitalists. By August 1996 it was valued at $20,000,000, up 10x from the $2,000,000 at which it had been valued just 8 months earlier.

To model the growth of Hotmail’s subscriber base we’ll turn to a model called the Bass Model, after Professor Frank M. Bass who first published it in 1963 as a section of another paper. ((http://www.bassbasement.org/BassModel/)) The Bass Model states that the probability of adoption by those who have not yet adopted is a linear function of those who have previously adopted. The mathematical expression for the model is given below. ((Frank M. Bass, A New Product Growth for Model Consumer Durables, January 1969. Available at http://www.bassbasement.org/F/N/FMB/Pubs/Bass%201969%20New%20Prod%20Growth%20Model.pdf. Accessed on Jan. 26th, 2014))

$latex \frac{f(t)}{1-F(t)}=p+\frac{q}{M}\left[ A\left( t \right) \right]$

In the equation above:

  • t represents time, and the first full time interval of sales is t = 1,
  • p represents coefficient of innovation,
  • q represents the coefficient of imitation,
  • M is a constant, and represents the potential market or the number of purchasers of the product,
  • f(t) represents the fraction of the potential market that adopts a product at time t, and
  • F(t) represents the portion of the potential market that has adopted the product up to and including time t, and
  • f(t) is the first derivative of F(t) wrt t.

Alan Montgomery uses the Bass Model to fit the model’s results to actual data from Hotmail’s first year and reports a very good fit. ((Alan L. Montgomery, Applying Quantitative Marketing Techniques to the Internet, available at http://www.andrew.cmu.edu/user/alm3/papers/internet%20marketing.pdf, July 2000. Accessed Jan. 26th, 2014)) He uses estimates of 0.0012 for p, 0.008 for q, and 9,670,000 for M. I will tackle models like the Bass Model in later posts.

It is reported that Bhatia sent a message to a friend in India using Hotmail, and three weeks after that Hotmail had 100,000 users there. ((Willix Halim, My Top Five “Growth Hacking” Techniques, http://e27.co/my-top-five-growth-hacking-techniques/. Accessed on Jan. 27th, 2014.)) Hotmail was eventually bought by Microsoft in 1998, a year and a half after it launched to the public. The value of the deal was not made public but is rumored to be as high as $400,000,000. ((Jeff Peline, Microsoft Buys Hotmail, January 3rd, 1998, http://news.cnet.com/2100-1033-206717.html. Accessed on Jan. 27th, 2014.))

What ever you call it, “Growth Hacking” or “Viral Marketing”, it works. Hotmail spent a fraction of the capital that its rivals spent on marketing and advertising, but experienced significantly more growth.

In the next post on this topic I will study the tactics Dropbox used to grow its user base.

A Note on Viral Marketing – Part I: What is it?

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Recently, I have had to immerse myself into studying about viral marketing in order to understand some challenges faced by startups I am studying or assisting as part of my responsibilities at work. This series of posts on Viral Marketing is my attempt to document some of what I have learned. ((Any errors in appropriately quoting my sources is entirely mine. Let me know what you object to, and how I might fix the problem. The data in this post is only as reliable as the sources from which I obtained them.))

The term “Viral Marketing” was popularized by some combination of Tim Draper, Steve Jurvetson and Jeffrey Rayport. Jeffrey Rayport used the term “v-marketing” in an article in Fast Company in December 1996/January 1997. ((Jeffrey Rayport, The Virus of Marketing, Fast Company, Dec 96/Jan 97. Accessed at FastCompany.com on Nov 28, 2013.)) Tim Draper and Steve Jurvetson of DFJ claim to have used the term “viral marketing” in their 1997 newsletter to investors in which they discussed DFJ’s investment in Netscape. Steve Jurvetson published a blog post discussing the term in May 2000. ((Steve Jurvetson, Recent Developments in The Evolution of Viral Marketing, May 1, 2000. Accessed at DFJ.com on Nov 28, 2013))

Viral marketing is a collection of marketing, advertising, and sales techniques designed to exploit the power of dense social networks in order to increase product or brand awareness, increase user adoption, and drive revenue growth. This practice is not new, evidence exists to suggest that it has been in use at least since the 1800s. ((See for example Infectious Texts: Modeling Text Reuse in Nineteenth-Century Newspapers. Also, Here’s How Memes Went Viral – In the 1800s.))

According to Steve Jurvetson viral marketing is:

  • Network-enhanced word of mouth,
  • Usage affiliated marketing – friends marketing to one another.

As a result of their belief in the power of viral marketing DFJ invested in several startups that used viral marketing techniques in order to grow; Hotmail, NetZero, Skype, eVite, SeeUthere, Keen/Inforocket, Homestead, Mimeo, and NetMind/Palm are startups he mentions in his blog post.

According to Jeffrey Rayport viral marketing is most successful when it:

  • Is stealthy in the way it approaches potential targets,
  • Offers something free but valuable upfront,
  • Exploits the natural behavior of members of its target communities,
  • Does not look like a virus – avoids eliciting initial negative reactions from its targets,
  • Exploits nodes with many weak ties in social networks rather than nodes with few strong ties,
  • Reaches the tipping point – the point at which future growth mimics the behavior of an epidemic.

So, for a startup short on time, cash, people and resources, how does one go about developing a product with viral characteristics? What makes products go viral? What mechanics enhance social transmission?

Recent research ((Berger, Jonah A. and Milkman, Katherine L., What Makes Online Content Viral? (December 25, 2009). Available at SSRN: http://ssrn.com/abstract=1528077 or http://dx.doi.org/10.2139/ssrn.1528077. Published in Journal of Marketing Research, Volume 49, Number 2, April 2012. Accessed Nov 28, 2013.)) suggests that digital content goes viral for a simple reason, when people care they share. More specifically, digital content that evokes positive emotions like amusement, awe, or excitement, is shared more than digital content that evokes negative emotions like anger, anxiety, frustration, or sadness. Social transmission and diffusion is governed by individual-level psychological and macro-level sociological factors. What does this mean? I am unlikely to share news about a digital product ((I am mainly interested in the application of this research to digital products, so I will use “content” and “product” interchangeably.)) that makes me happy, but that I feel is irrelevant to my community of friends. ((For example, language barriers could dissuade me from transmitting a particular product widely among my community of friends. Later we will see examples of viral marketing that involves targeting specific markets by LINE, a Japanese messaging app.)) People share in order to entertain their friends, but entertaining content that is surprising and interesting is more viral than content that is merely entertaining. People share in order to inform and educate others, but content that is also practical and positive is more viral than similar content that is merely informative.  The authors make some suggestions for people developing products that might benefit from viral growth:

  • Viral marketing campaigns designed to spread adoption of a digital product should evoke high-arousal emotions – content or relaxed consumers will share less than amused consumers. The controversies that surrounded Facemash helped to propel Facebook’s adoption. ((See for example; Katherine A. Kaplan, Facemash Creator Survives Ad Board, Harvard Crimson, November 19, 2003. Accessed on Nov 28, 2013. Note that the article attracted 216 comments. Facemash was Facebook’s predecessor.))

After creating the website, Zuckerberg forwarded the link to a few friends for advice. But the link was sent out on several campus group list-serves, and traffic skyrocketed. In the course of one day, the number of visitors quadrupled – by 10 p.m., the site had been visited by 450 people, who voted at least 22,000 times. 

– Extract from 2003 Harvard Crimson article about Facemash and Mark Zukerberg.

  • Viral marketing campaigns should focus less on targeting “influentials” and more on crafting content and creating features that will cause contagious social transmission.

Why do consumers talk about some products more than others, and what drives those conversations immediately and over time? Jonah Berger and Eric M. Schwartz have studied that question. ((Berger, Jonah A. and Schwartz, Eric M., What Do People Talk About? Drivers of Immediate and Ongoing Word-of-Mouth (April 25, 2011). Available at SSRN: http://ssrn.com/abstract=1822246. Published in Journal of Marketing Research, Volume 48, Number 5, October 2011. Accessed Nov 28, 2013.)) Their research suggests that:

  • Digital products that have higher public visibility with consumers, or that send consumers more frequent cues gain greater volumes of immediate, ongoing and overall word of mouth. ((It is not an accident that you generally open the apps on your smartphone or tablet that send you periodic prompts more frequently than those that do not.))
  • Promotional giveaways may boost word of mouth, and it is better to giveaway the product itself, or to giveaway non-product items like t-shirts. ((A brand-new, unread email, chat, or status update could be considered a reward that fits in this category.))
  • Simply being “more interesting” is not a sufficient quality to maintain word of mouth.
  • There is a difference between the factors that lead to online word of mouth and offline word of mouth sharing.

Here are some examples of products that have benefited from the power of viral marketing. ((I am gathering data in this Google Spreadsheets Document. If you have historical data you want to include in this effort please send it to me and I’ll add it to this document.))

  • Hotmail was introduced to the public in July 1996. By the end of the month it had 20,000 users. By the fourth month it had exceeded 100,000 users. Hotmail gained its 1,000,000th user in January 1997. It crossed the 12,000,000 user mark within 18 months, after spending only $500,000 on marketing. ((Data from Tony Lloyd, Are You Using The Dynamic Power of Viral Marketing? Accessed on Nov. 28, 2013.)) Hotmail gained 30 million users in 30 months. ((MSN Hotmail: From Zero to 30 Million Member in 30 Months, February 8th, 1999. Accessed at Microsoft PressPass on Nov. 28, 2013.  )) How did this growth occur? Tim Draper convinced Sabeer Bhatia and Jack Smith to include “Get your free email at Hotmail.” at the end of each email message sent through Hotmail. ((I found at least one source that claims that Hotmail had 400,000,000 users by December 1997.))
  • Napster was introduced to a group of 30 people in June 1999 by Shawn Fanning and Sean Parker. Within a week Napster had 15,000 users. Within a year it had acquired 25,000,000 users. It peaked at 80,000,000 users within 2 years of its release, and then started to lose users because of all the legal trouble it had started to attract. ((Alex Laird, The Napster Revolution. Accessed on Nov. 28, 2013))
  • Pinterest ((Unless noted monthly unique users are estimates developed by Steve Cheney, How To Make Your Startup Go Viral The Pinterest Way, TechCrunch, November 26, 2011. Accessed on Nov. 28, 2013. Click on the link to the Google Spreadsheet document.)) was launched to the public in March 2010 by Ben Silberman, Evan Sharp and Paul Sciarra. It had 2000 unique visitors in April 2010. By August 2010 it had 11,000 unique visitors. Pinterest’s monthly unique users grew at an average monthly rate of 54% in 2010, exceeding 80,000 in January 2011. The number of monthly unique users grew by an average monthly rate of 45% in 2011, and Pinterest crossed the 1,000,000 monthly unique users threshold in August 2011. It had 3,300,000 monthly unique visitors in November 2011. Monthly unique users grew to 27,323,489 in November 2012. ((Data for 2012 and 2013 is obtained from Compete.com’s Pinterest analytics dashboard.))  In May 2012, Mashable reported that Pinterest was beginning to focus more on international expansion. ((Lauren Indvik, Confirmed: Pinterest Raises $100 Million to Fund International Expansion,Mashable, May 16, 2012. Accessed on November 29, 2013.)) Pinterest’s US only unique users had risen to 31,549,541 for January 2013, and 32,835,276 in October. Semiocast reported that Pinterest has 70,000,000 global users worldwide in July 2013. ((Semiocast press release: Pinterest, July 10th, 2013.))
  • Facebook launched as theFacebook in February 2004. It had more than 650 registered users within the first week or so. Originally it was limited to students of Harvard. ((Alan J. Tabak, Hundreds Register for New Facebook Website, Facemash Creator Seeks New Reputation With Latest Online Project, Harvard Crimson, February 9th, 2004. Accessed on Nov. 28th, 2013.)) In May 2004 it expanded access to include Harvard, Columbia, Stanford and Yale. It hit the 1,000,000 user mark in December 2004. By May 2005 it had expanded to include more than 800 colleges, and in September 2005 it dropped “the” from theFacebook. Also in September 2005 it added high school networks, with addition of international schools following in October. Around that time it also launched Facebook Photos. Facebook’s user count hit 6,0000,000 in December 2005. It launched a product for mobile users in April 2006 and expanded to include work networks the following month. September 2006 was another big month for Facebook – it launched its news feed and mini-feed that month. It also expanded access so that anyone could become a registered user. Its registered users increased to 12,000,000 in December 2006. One year later it had 58,000,000 users. In March 2008 Facebook launched in German. Later that year it launched Facebook Chat, and also opened an office in Dublin. The “Like” button was introduced in February 2009. Facebook had 360,000,000 users in December 2009. A year later it had 608,000,000 users. In November 2011 Facebook had 845,000,000 users. By October 2012 Facebook had more than 1,000,000,000 registered, active users ((Data obtained from Facebook Timeline, accessed on Nov. 30, 2013)). The most recent update available Facebook has 1,267,191,915 users. ((Ellis Hamburger, 1.26 Billion Facebook Profiles Become A Clickable Monument To Humanity, The Verge, September 29, 2013. Accessed Nov. 29, 2013.))
  • This chart shows growth in users for LINE, the Japanese chat app. LINE is a particularly interesting case study because it has developed an international strategy ((Eric Pfanner, A Japanese Social App Contacts New Shores, New York Times, September 5, 2013. Accessed on Nov. 30, 2013)) that involves deliberate and targeted international launches. ((See for example; Japanese Messaging App LINE Comes To India. Launches TVCs Promoting Free Voice Chat And Stickers. and Social App Line targets UK after signing 260m users (including Sir Paul McCartney). There are other reports about LINE’s activities related to a launch in Spain.))

LINE app: number of registered users as of November 2013
You will find more statistics at Statista

  • The next chart shows growth in users for KakaoTalk, the Korean messaging app.

KakaoTalk: number of registered users 2010-2013
You will find more statistics at Statista

  • This last chart shows the quarterly user count for Linked in from Q1 2009 to Q3 2013.

Numbers of LinkedIn members as of 3rd quarter 2013
You will find more statistics at Statista

In the next post in this series I will examine a few case studies in a bit of detail to try to understand the specific techniques that have worked, and if possible to try to identify techniques that lead to failure.