Network Effects A N U
H A R I H A R A N E T A L

What are network effects? Properties,
terms, and laws of networks Strategies for building network effects What aren’t network effects? Case studies of companies with network effects

Simply put, a network effect*
occurs when a product or a service becomes more valuable to its users as more people use it *also known as: demand-side economies of scale

Because understanding network effects helps
build better products and businesses Especially since network effects are the key dynamic behind many successful software-based companies Why does this matter?

Create barriers to exit for
existing users and barriers to entry for new companies (help build moats) Protect software companies from competitors’ eating away at their margins Can help create or tip winner-take-all markets Network effects

What are network effects? Properties,
terms, and laws of networks Strategies for building network effects What aren’t network effects? Case studies of companies with network effects

Networks, which are basically just
a set of nodes connected by links, have various properties Some of those relevant properties include: 1. Whether the nodes are homogeneous or heterogeneous 2. Their type of clustering and degree of connections 3. Directionality of those connections 4. Whether they have (or are) complements Putting the ‘network’ in network effects

1. Homogeneous or heterogeneous? Homogeneous
Composed of similar types of nodes Heterogeneous Skype is an example of a homogeneous network where most of the value is derived from a single class of users, all interested in placing a phone call Composed of different types of nodes OpenTable is an example of a heterogeneous network with two distinct categories of participants: one side is restaurants, the other side is diners Image source (Skype): http://letsbytecode.com/security/skype-the-phantom-menace/

2. Degree of connections and
type of clustering Source: Albert-László Barabási, Linked: The New Science of Networks Source (original chart): https://griffsgraphs.wordpress.com/tag/clustering/ Degree: Measures number of connections to a single node Clustering coefficient: Measures degree to which nodes in a graph (e.g., social graph, interest graph, intent graph, etc.) cluster together Type of cluster: Can range from hub-and-spoke (star) to connected (clique) Example of Facebook friends connections clustering (high school, college, significant other’s, etc. clusters)

Zooming in a bit further
on those terms Degree Number of connections to a single node Homogeneous Type of Cluster The resulting CC(A) represents the fraction of possible interconnections between the neighbors of A 0 ≤ CC(A) ≤ 1 [For operating systems, Microsoft Windows is regarded as a central hub, with 85% share of the network!] Node A has 4 connections, therefore its degree is 4 Clustering Coefficient (CC) How likely are two nodes that are connected part of a highly connected group of nodes? If A is the node, and d is the degree A = 4, then n is the # of links between neighbors (blue dots) of A = 1 For example: CC(A) = 2*N/D(D-1) = 1/6 In hub-and-spoke or star networks, the fittest node (central hub) grabs all connections, leaving very little for the rest of the nodes In clique networks, node A is connected to its neighbors and all those neighbors are connected to each other cliquehub-and-spoke Source: https://youtu.be/K2WF4pT5pFY See also: Albert-László Barabási, Linked: The New Science of Networks

3. Connections: Unidirectional or Bidirectional?
Friends Facebook, for example, is one place where connections tend to be bidirectional With bidirectional or two-way friending, you are more likely to have balanced connections: Follower Twitter, for example, is one place where connections can more easily be unidirectional Unidirectional or one-way following leads to asymmetrical connections (e.g., the asymmetric follow) Note: You could still have balanced connections here where you both or neither of you follow each other • You are friends • You are not friends • People follow you, but you don’t follow them back • People don’t follow you, but you do follow them

4. Complementary Networks More usage
of the MS Windows operating system More usage of the MS Office suite of applications Increase in usage of one product by a set of users reinforces and increases the value of a complementary (but separate!) product, which in turn, increases the value of the original Source: http://cdixon.org/2009/08/25/six-strategies-for-overcoming-chicken-and-egg-problems/ T W O P R O D U C T S A R E C O M P L E M E N TA RY W H E N T H E Y A R E S E PA R AT E B U T A R E M O R E U S E F U L T O U S E R S T O G E T H E R operating systems have strong network effects of their own (via developers) as do productivity apps (via file formats), but in this case they also reinforce each other

Besides those properties, networks (more
specifically, communication networks) can also exhibit the following laws:

3 common laws for assessing
the value of communication networks Sarnoff’s law Value of a network is proportional to the number of viewers Broadcast Yahoo 1 Value of a network is proportional to square of number of connected users Peer to Peer Facebook Metcalfe’s law 2 Value of a group-forming network is proportional to number and ease with which groups form within it (subgroups grow faster than sheer number of P2P participants) Group Forming Slack or WhatsApp groups Reed’s law 3 Sources: Andrew Odlyzko et al http://www.dtc.umn.edu/~odlyzko/doc/metcalfe.pdf

Facebook is a classic example
of Metcalfe’s law Every new user connecting to other peers in the network (peer- to-peer) non-linearly increases the number of connections Source: Bob Metcalfe/ IEEE Computer 2013, via Bill Krause

And finally, let’s define some
commonly used terms in the context of network effects

Terms and definitions for our
purposes Network is a group of interconnected people (social network) or system of things (telephones, printers to computers) Marketplace is a network where money/transactions flow between two or more sides with distinct (i.e., heterogeneous) groups of users on each side; a successful marketplace is where supply and demand are attracted to the same place Platform is a network of users and developers; the multi-sided feedback loop between those users, developers, and the platform itself creates a flywheel effect increasing value for each of those groups. It can also be thought of as a network that can be programmed, customized, and extended by outside users—often meets needs and creates niches not defined by its original developers at the outset

Examples Platform Operating systems Messaging
app like WeChat Marketplace Online auction marketplace Dating sites (can be heterogeneous or homogeneous!) Network Social network Telephone network Office printer & computer network

So why do any of
these details—topologies, other properties, precision of terms—matter to startup founders?

Because these details suggest what
questions to ask (e.g., is this network defensible?) and what the corresponding entrepreneurial strategy should be

For example, if it’s X,
then ask Y: Platform Will the market we’re eyeing eventually be served by a single platform and will it be shared (Ethernet) or will it be a fight for proprietary control (MS vs Apple)? Marketplace How do we build liquidity in the marketplace/solve the chicken-and-egg (which side comes first) problem? Which is the money side vs subsidy side of the marketplace? Network What should the entry point be (to build a network effect)? What are the growth levers/tactics/hacks to get to critical mass? What’s the critical mass inflection point (at which a network effect occurs)? How do you drive engagement? How do we take advantage of irregular topologies to find clusters and sub clusters?

Most of these questions really
boil down to What’s the initial growth lever or tactic to help us get to scale? Another way of thinking about this is: What’s the deterministic (not-so-random) solution to the bootstrapping problem? These questions help counter the wishful thinking and sometimes faulty assumption behind the belief that if we build it, they will come

We’ll share specific strategies for
attacking all these questions First, let’s look at some examples

But there’s one more question/definition
to know before doing so…

Is the product single or
multiplayer mode? Source: http://cdixon.org/2010/06/12/designing-products-for-single-and-multiplayer-modes/ Single Player Mode The product has immediate utility for a single user Examples Multiplayer Mode The product has no utility for a single player (especially true for communication products—a phone is useless without someone at other end) Examples (early days): tool to store private photos (early days): bookmark restaurants you’ve been to need connections to other users to make calls messaging for teams

Note: You can sometimes have
both single and multiplayer mode for a single product Single player mode is more powerful when accompanied by an initial ‘hack’ or other bootstrapping of early network growth. (e.g., Instagram’s cool photo filters was a way to post photos on Twitter before there was enough critical mass) Single player mode can also help with adoption in the early stages of a product, when network effects aren’t strong enough yet come for the tool, stay for the network. (e.g., Medium offering a beautiful publishing tool before it built its network of people and ideas)

What are network effects? Case
studies of companies with network effects Strategies for building network effects What aren’t network effects? Properties, terms and laws of networks

Facebook T H E U
LT I M AT E C A S E S T U D Y I N N E T W O R K E F F E C T S Started off as a social network (peer to peer connections) Became a platform (with developers) Has elements of a marketplace (users/advertisers, Instant Articles)

So what led to network
effects for Facebook? Mode/product value Growth tactic Engagement trigger Network effects Began as online student directory with information that was immediately useful even to a single player (user) Became a way for college students in courses and clubs to connect with other (multiplayer mode) Accessed the entire Harvard directory early on; critical in driving early adoption And because the product had inherent virality, it spread from one user to another as an organic consequence of use Identified early on that connecting a new user to 10 friends within 14 days of sign up was critical to improving retention So they used email contact imports, suggested friends and embedded widgets to drive that engagement Continued tweaking product (relationship status, timelines, etc.) to get everybody to join and stay on board So made sure there was an increase in usage even as the number of their users grew C O R R E S P O N D I N G Q U E S T I O N S : W H AT ’ S T H E E N T RY P O I N T ? W H AT A R E T H E G R O W T H L E V E R S ? W H AT ’ S T H E C R I T I C A L M A S S I N F L E C T I O N P O I N T ? H O W D O W E D R I V E E N G A G E M E N T ? Sources: Mark Zuckerberg interview with James Breyer, https://youtu.be/WA_ma359Meg Chamath Palihapitiya interview https://youtu.be/raIUQP71SBU

By the numbers: Early growth
as key (but not sole) indicator 1 6 12 58 145 360 608 845 0 100 200 300 400 500 600 700 800 900 2004 2005 2006 2007 2008 2009 2010 2011 (millions,year-end) MAUs Founded at Harvard 800+ college networks FB mobile and share on partner sites Launched platform with developers and apps Introduced chat Introduced Like button and payments Introduced Graph API for easy integration Introduced Timeline Source: Facebook S-1

C O N S TA
N T LY R O L L E D O U T N E W F E AT U R E S A N D O P T I M I Z E D T H E S I T E T O I N C R E A S E E N G A G E M E N T, D A I LY 0 200 400 600 800 1000 Mar-09 Jun-09 Sep-09 Dec-09 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11 Dec-11 Inmillions Hyper growth in DAUs and MAUs pre-IPO MAUs DAUs By the numbers: Focus on daily usage to help grow network Source: Facebook S-1

By the numbers: A sign
of network effects I N C R E A S E I N U S A G E E V E N A S N U M B E R O F U S E R S G R E W 45% 47% 51% 54% 53% 53% 54% 55% 56% 57% 57% 0% 20% 40% 60% 80% 100% Mar-09 Jun-09 Sep-09 Dec-09 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11 Dec-11 DAUS/MAUs(%) Source: public company data

While many social networks today
start off launching to everyone, Facebook’s entry strategy was taking a clustered approach (get Harvard) before rolling it out to other clusters (Stanford, etc.) More importantly, they were focused on engagement, not just growth Contrary to popular belief, Facebook kicked off offering immediate utility in single player mode (the online school directory), but people started connecting with each other (multiplayer mode) right away too Some takeaways

Airbnb T W O S
I D E D M A R K E T P L A C E W I T H O V E R L A P I N B O T H S I D E S More guests More hosts Network effect from both sides of the network More hosts attract more guests and vice versa More hosts = more availability for guests More guests = more business/$ for hosts

A unique aspect of some
peer-to-peer marketplaces like Airbnb is overlap between supply (hosts) and demand (guests) In other words, guests also become hosts and hosts also become guests!

How did Airbnb achieve its
network effects? Critical mass on both sides Network effects Airbnb capitalized on an existing problem/need— very limited or expensive hotel space Turning homes to lodging provided immediate value to users: 30%-80% cheaper than hotels and highly differentiated type of inventory (less sterile and more personal/social than hotel rooms) As more guests stayed in more places (demand), more hosts got more business and more hosts offered their places which in turn created more supply for guests As measured by number of room nights Mode/product value Airbnb targeted cities with sold-out events and constrained hotel supply (such as during the Democratic Party national campaign or World’s Cup) with traditional marketing and other methods to advertise its alternative Growth tactics Launched photography services to make offerings more appealing to guests Also added ability for mutual social connections to see who else had stayed to help build trust in the marketplace Critical mass Network effects C O R R E S P O N D I N G Q U E S T I O N S : H O W T O B U I L D L I Q U I D I T Y / S O LV E T H E C H I C K E N - E G G P R O B L E M ?

By the numbers: There was
no viral growth in the early days. launched photography program launched social connections # of new listings (early days of Airbnb: March 2008 to May 2011) # of transacting users (early days of Airbnb) traditional marketing around targeted events Note: Y-axes masked for confidentiality

But then there was a
sign of network effects I N C R E A S E I N N U M B E R O F G U E S T S T H AT S TAY E D E A C H Y E A R , C R E AT I N G M O R E S U P P LY A N D M O R E D E M A N D - 2 4 6 8 10 12 14 16 18 2008 2009 2010 2011 2012 2013 2014 Millions took nearly 36 months to build sufficient liquidity and to start seeing signs of network effects Source: Company data

Airbnb focused early features on
building the demand side and in a marketplace, supply will always go to where the demand is (and will stay if you help grow their business) Note: a product or service does not necessarily have to have viral growth to lead to network effects Traditional marketing methods—branding, design, targeting, direct advertising—can help Trust and safety is paramount in all marketplaces Some takeaways Source: Jeff Jordan in http://a16z.com/2015/02/24/managing-tensions-in-online-marketplaces/ See also http://www.forbes.com/sites/valleyvoices/2015/10/21/how-to-guard-your-marketplace-against-fraudsters/

Medium T W O -
S I D E D N E T W O R K W I T H C R O S S A N D S A M E - S I D E N E T W O R K E F F E C T S More writers More readers Network effect from both sides of the network More writers = more time readers spend on Medium More readers = More writers begin to write But can be on the same side of the network, too! When readers invite other readers (via highlights, mentions, replies, and annotations), the overall value of the entire network increases as more ideas are shared in that network itself

What is leading to network
effects for Medium? Critical mass on both sides Network effects Provided immediate, single-player utility—in the form of an elegant and easy-to-use publishing tool Often described as the “best web editor I’ve ever used” for both experienced and inexperienced writers More writers writing directly on Medium and more readers spending more time reading directly on Medium Becoming a network of people and ideas Mode/product value Curated special content collections/star contributors to create perceived exclusivity and as a beachhead to attract other influencers Used the 1-9-90 internet rule—where 1% users actively write, 9% participants edit, 90% read—to invite those who engaged to also become writers Growth tactic As they built critical mass, Medium designed the platform itself to optimize for engagement—through “in-content interactor” features such as highlights, recommends, responds, and mentions Used taxonomy of collections and publications to cluster highly engaged community around topics of interest Engagement trigger Network effects C O R R E S P O N D I N G Q U E S T I O N S : H O W T O B U I L D L I Q U I D I T Y ? H O W T O D R I V E E N G A G E M E N T ? Source: Ev Williams https://medium.com/the-story/medium-is-not-a-publishing-tool-4c3c63fa41d2

By the numbers: Early signs
of network effects N O N - V I R A L S T O R I E S G E T A M A J O R I T Y O F T R A F F I C F R O M W I T H I N M E D I U M 12% 30% 48% 54% 35% 23% 34% 35% 29% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Top Middle Tail Medium Social OtherTTR (total time reading) by referrer increasingly coming from Medium for long tail and middle— represents the audience that writers can’t easily reach on their own without Medium Source: https://medium.com/data-lab/quantifying-network-effects-7e6bc167aea5 , company data

Some takeaways Reminder that single
player mode can help get to multiplayer mode. The appeal of the tool attracts users initially to help build enough critical mass, and then getting those users to participate over time creates the network come for the tool, stay for the network They didn’t just built the tool and wait for users to come; a lot of up-front work went into curating and editing early content and community See also: http://cdixon.org/2015/01/31/come-for-the-tool-stay-for-the-network/

WhatsApp A N E V
E N M O R E H I G H LY C L U S T E R E D N E T W O R K T H A N FA C E B O O K 1 WhatsApp user had ≈20 connections compared to ≈980 friends on Facebook While this is fewer connections, they were highly clustered among close family and friends or WhatsApp Groups and therefore led to more engagement

What led to WhatsApp’s network
effects? Critical mass on both sides Network effects Single-player utility: Initial product enabled “what’s up” status updates of phone contacts that were useful even without interaction Multi-player utility: Provided instant messaging—essentially better, simpler, sand free SMS in international markets (now 50% bigger than global SMS) WhatsApp didn’t just have growth, it had more engagement (as indicated by % DAU x % MAU growth)—in other words, more users added more value for other users (that engagement is high at over 70%) Mode/product value Was one of the early apps to leverage the phone book as social graph: Each user invited users from their phone contacts (“closest family and friends”) Started small with close- knit Russian community in West San Jose to build initial critical mass before spreading to other subclusters Growth tactic Group Chat feature helped it go beyond pairwise connections Multimedia (MMS) helped it be used like Facebook (family photo sharing, etc.) in India and other places where people didn’t use web-based apps as much Engagement triggers Network effects C O R R E S P O N D I N G Q U E S T I O N S : W H AT S H O U L D T H E E N T RY P O I N T / S T R AT E G Y B E ? H O W D O W E TA K E A D VA N TA G E O F I R R E G U L A R T O P O L O G I E S T O F I N D C L U S T E R S A N D S U B C L U S T E R S ? Sources: http://www.forbes.com/sites/parmyolson/2014/02/19/exclusive-inside-story-how-jan-koum-built-whatsapp-into-facebooks-new-19-billion-baby/ http://www.businessinsider.com/whatsapp-engagement-chart-2014-2 https://growthhackers.com/growth-studies/whatsapp

WhatsApp Growth vs Other Popular
Platforms Source (WhatsApp): http://www.forbes.com/sites/parmyolson/2014/02/19/exclusive-inside-story-how-jan-koum-built-whatsapp-into-facebooks-new-19-billion-baby/ FA S T G R O W T H : H I T 6 7 M M A U S I N 2 Y E A R S ( 5 . 5 X B I G G E R T H A N FA C E B O O K A N D 1 7 X B I G G E R T H A N T W I T T E R Y E A R T W O ) 0 50 100 150 200 250 300 350 400 450 Year 0 Year 1 Year 2 Year 3 Year 4 (millions) MAUs Facebook: 145m WhatsApp: 419m (800m+ today) Gmail: 123m(1) Twitter: 54m(2) Skype: 52m(3)

By the numbers: Sign of
network effects P E O P L E A R E N ’ T J U S T R E A D I N G / R E C E I V I N G M E S S A G E S B U T W R I T I N G / S E N D I NG M E S S A G E S 0 5 10 15 20 25 30 35 Sep-11 Mar-12 Sep-12 Mar-13 13-Sep Mar-14 Sep-14 Mar-15 Mar-15 WhatsApp outgoing messages/day (bn) Sources: WhatsApp, a16z See also: http://ben-evans.com/benedictevans/2015/1/11/whatsapp-sails-past-sms-but-where-does-messaging-go-next

Some takeaways Remember, usage—not just
growth—is what helps indicate network effects Unlike Facebook, WhatsApp launched globally at outset but still pursued a clustered approach by making sure product was working in one subcluster first...Product continued to grow in clusters, not just peer to peer “No ads, no gimmicks, no games”—focused on simplicity first which tends to viral before adding extra features Also, phone as login provided a very low barrier to entry for users (especially internationally, where more people have phone numbers than email addresses)

What are network effects? Strategies
for building network effects What aren’t network effects? Properties, terms and laws of networks Case studies of companies with network effects

How do you build—and maintain—network
effects? Product should provide inherent value, whether in single or multiplayer mode Growth tactics to drive adoption Engagement trigger Sustain network effects Viral growth note that viral growth, while very helpful, is not necessary for critical mass

Some strategies for building network
effects What is your entry strategy? Bowling pin strategy 1 What are the growth levers to drive adoption? Growth strategy 2 What is your critical mass inflection point? Critical mass goals 3 What are the engagement triggers? Engagement strategy 4 How can you leverage an irregular network? Irregular networks 5

1. Bowling pin strategy to
overcome chicken-egg problem Segment Segment Segment Segment Segment Segment Facebook did this well by starting with Harvard before moving to other schools and then opening up to everyone Should I build supply first or demand first And how much of each do I need? Where do I start? One way to overcome that tension is to use Geoffrey Moore’s (Crossing the Chasm) Bowling Pin strategy: Start with a niche segment where the chicken and egg can both be easily overcome, then eventually move to other niches and the broader market

2. Bootstrapping growth to drive
adoption early on Source: https://www.quora.com/What-are-some-growth-strategies-used-by-Reddit See also: https://medium.com/@nishrocks/why-we-created-the-yelp-elite-squad-b8fa7dd2bead Accessed the entire Harvard directory early on to provide immediate utility for early adoption Canvassed friends and family for early feedback and reviews; also found and nurtured the top 100 super users as tastemakers Made it “the” place to find and discuss all things hip and cool related to a particular city Created several accounts on their own and submitted a lot of interesting content link to make the site feel alive for new users and thus quickly helped create a community

3. Setting goals to help
attain ‘critical mass’ more quickly Connect a new user to 10 friends within 14 days of sign up Tag at least 3 friends to each campaign Facebook realized early on that it was important to connect each new user to at least 10 friends for them to stay engaged on the platform (repeat usage) This focus from Facebook on repeat engagement is what drove network effects Similarly, Tilt observed in the early days that campaigns were 75% more likely to tilt if at least 3 friends were tagged

4. Having specific triggers to
sustain engagement in network Constantly rolled out new features (Like button, news feed, chat) to keep engagement on the platform First of its kind to leverage “phone book contacts”; the stickiest cluster coupled with the high utility of the product (free SMS in international markets) helped keep engagement high

5. Leverage irregular network topologies
B Y F I N D I N G C L U S T E R S , C O M PA N I E S C A N R E A C H C R I T I C A L M A S S W I T H I N T H O S E S U B C L U S T E R S A N D E X PA N D B E Y O N D Real life networks are often very different from the uniform distributed networks pictured in textbooks WhatsApp took advantage of the fact that social connections are highly clustered in your phonebook and used that as a “beachhead” to launch groups They also targeted the international communities (e.g., Russian community in bay area) that found WhatsApp a cheaper alternative to expensive SMS

Strategies for creating network effects
How do you attract the harder side of the marketplace? 6 Subsidizing strategy M A R K E T P L A C E S Via cdixon.org

6. Attracting the harder side
of the marketplace In almost every two-sided market, one side is harder to acquire than the other Common way to attract the harder side is to subsidize that harder side For example, single bars often have special ladies’ nights promotions on slower nights offering women discounts on drinks More men More womenBars

Reducing prices for the hard
side of the market (e.g., Adobe Flash and PDF for end users) can help build critical mass But solving the chicken-egg problem only by subsidizing has to be considered carefully in the context of the overall business model—i.e., there’s a difference between building initial critical mass and building a sustainable business (can’t ignore overall unit economics) This is why understanding which is the money side of the marketplace and the side of the marketplace where the most value is coming from matters so much because then you know which side to carefully subsidize See also: Thomas Eisenmann https://hbr.org/2006/10/strategies-for-two-sided-markets

However, this dynamic may play
out a little differently in so-called “sharing economy marketplaces Because such marketplaces can be supply-constrained due to unfamiliarity with the sharing economy model So those marketplaces have to work harder to get more supply as well, and hence may also subsidize that side or build other features to address these issues in other ways

Strategies for creating network effects
Show long-term commitment to platform 7 Provide stand alone value of the base 8 Vertically integrate when supply uncertain 9 P L AT F O R M S Via cdixon.org

7. Showing a long-term commitment
to the platform W H E N FA C E B O O K A C Q U I R E D O C U L U S , T H E Y S I G N A L E D T H E I R L O N G - T E R M C O M M I T M E N T T O H E L P D R I V E P L AT F O R M S U C C E S S ; O C U L U S A L S O A N N O U N C E D I T W I L L P U M P $ 1 0 M I N T O I N D E P E N D E N T G A M E - D E V E L O P M E N T E F F O R T S Getting to critical mass in indirect networks can be challenging Because you are a platform you are dependent on 3rd party developers to remain engaged and grow your platform See also: http://cdixon.org/2009/08/25/six-strategies-for-overcoming-chicken-and-egg-problems/ W H E N T H E Y L A U N C H E D T H E X B O X , M I C R O S O F T D I D S O M E T H I N G S I M I L A R I N P R O M O T I N G T H E I R P L AT F O R M A N D S I G N A L I N G T H E I R C O M M I T M E N T

8. Providing standalone value of
the base T H I S C O M P L E M E N TA RY N E T W O R K E F F E C T I M P R O V E D T H E VA L U E A N D I N C R E A S E D S A L E S V E L O C I T Y O F B O T H T H E B A S E P R O D U C T ( V C R ) A N D C O M P L E M E N T ( V I D E O C A S S E T T E S ) A N D R E M A I N E D T H E S TA N D A R D F O R Q U I T E A L O N G T I M E ! The standalone value of the VCR—“time shifting” of TV programming”— was strong enough to get >1M people to purchase one early on This installed base enticed entrepreneurs to develop a market for pre- recorded videocassettes, creating an indirect network effect improving the value of the VCR and protecting it from incremental alternatives Source: http://cdixon.org/2009/08/25/six-strategies-for-overcoming-chicken-and-egg-problems/

9. Integrate vertically into critical
complements By vertically integrating the complement product (game) as well as the base product console), a company can attempt to ensure adequate supply of both goods For example, Nintendo is the leading developer of games for its own consoles and Microsoft and Sony also fund many of the most popular games on their platforms as well In platforms, one doesn’t necessarily have to be dependent only on outside developers— companies can ensure critical complements are built by themselves as well Source: http://cdixon.org/2009/08/25/six-strategies-for-overcoming-chicken-and-egg-problems/

What are network effects? What
aren’t network effects? Properties, terms and laws of networks Case studies of companies with network effects Strategies for building network effects

Debunking some common misconceptions Network
effects and virality are NOT the same thing 1 Viral growth is NOT necessary for network effects 2 Just because a platform has scale does NOT mean you have network effects 3

1. Network effects and virality
are not the same thing! Network effects increases value as more users join a network, whereas viral growth increases just the speed of adoption (of a particular network’s product/service) These two concepts are often co-occurring so are sometimes conflated, but they’re not the same thing

What’s the difference? D E
F I N I T I O N S Network effects Product becomes more valuable as more users use it Network effects help build a moat for the business, leading to high engagement/ repeat rates and higher margins Represented by Metcalfe’s Law: value of telecom network is proportional to square of number of connected users of system (n2) Value Virality Product that spreads from one user to another through direct customer to customer contact Viral growth implies low CAC (customer acquisition cost) Often measured by viral coefficient (K factor): [average number of invitations sent by each existing user] * [conversion rate of invitation to new user] Speed See also: Sangeet Chaudhary http://platformed.info/virality-viral-growth-network-effects/

This is something that spreads
without financial or other sharing incentive due to being exclusive, invite-only, or other Example: Gmail created buzz (the hot thing with 1GB storage that was available only to a few) and encouraged existing customers to send invites slowly Distinguishing between various flavors of viral growth Referrals with no incentivesNetwork effects A product that has inherent virality—i.e., spreads from one user to another as an organic consequence of use—will have a network effect (referred to as a ‘direct network effect’ in academic literature) Example: Facebook without friend connections is not useful Word-of-mouth This is where customers recommend the product to other customers or distribute it via other platforms (like Facebook and Twitter) due to a positive experience with it Example: games like Angry Birds; BuzzFeed ‘The Dress’ Casual contact This is where a product spreads virally via customer to customer contact (not via users intentionally inviting other users) Example: Hotmail acquired users by including footers for free accounts at bottom of every email; DocSend acquires customers when users email links to view/download files TRADITIONAL VIRALITY‘PRODUCT VIRALITY’ See also: Thomas Eisenmann http://platformsandnetworks.blogspot.com/2011/07/business-model-analysis-part-5-virality.html

So why do those distinctions
matter? Because product virality (a product that is inherently viral) leads to network effects But traditional virality does not always lead to network effects

By the numbers: Product virality
leads to network effects FA C E B O O K I S T H E C L A S S I C E X A M P L E O F T H I S Product spread from one user to another as an organic consequence of its use, allowing Facebook to acquire users at $0 CAC The platform became more valuable as more users joined The signpost of network effect in this case is high engagement even as number of users increased 1 6 12 58 145 360 608 845 0 100 200 300 400 500 600 700 800 900 2004 2005 2006 2007 2008 2009 2010 2011 (millions,year-end) MAUs MAUs CAGR ‘04-’11: 162% 45% 47% 51% 54% 53% 53% 54% 55% 56% 57% 57% 0% 20% 40% 60% 80% 100% DAUS/MAUs(%) Engagement (DAUs/MAUs) Product virality = Connections with friends Network effects Source: public company data

This distinction also explains cases
where things that (seemed to) have viral growth did not lead to network effects In most cases, that viral growth was really word-of-mouth

By the numbers: Word of
mouth ≠ Network effects A N G RY B I R D S I S A N E X A M P L E O F T H I S Product spread from one user to another via word of mouth referrals/ brand popularity as people started playing on their own—did not spread as an organic consequence of its use Users do not get incremental value when other users download and play the game So Angry Birds does NOT have network effects (and has a weak competitive moat as a result) The key difference: Organic consequence of use Key question: Does value increase for users? 30 100 225 350 400 500 0 100 200 300 400 500 600 Angry Birds (est. downloads in millions) Chart source: https://www.macstories.net/news/angry-birds-reaches-half-a-billion-downloads/

And remember, you can have
network effects without (product or traditional) virality

A I R B N
B I S A N E X A M P L E O F T H I S Early days required traditional marketing and numerous growth hacks to build liquidity on both sides of the marketplace # of guests that stayed at Airbnb saw hyper growth 3 years after launch Leads to more money for hosts and more availability for guests No virality…. …yet strong network effects over time # of new listings between 2008 and 2011 # of transacting users between 2008 and 2011 - 5 10 15 20 2008 2009 2010 2011 2012 2013 2014 Millions By the numbers: Network effects without virality Source: Company data

To clarify one final term/misconception:
Just because a platform has scale does NOT mean it has network effects

What’s the difference? Economies of
scale Product becomes cheaper to produce as business increases in size and output Increasing scale leads to lower cost per unit of output (cost per unit decreases as fixed costs are spread out over more units) Network effects Product becomes more valuable as more users use it Network effects help build a moat—leading to high/repeat rates of engagement, higher margins ValueCost D E F I N I T I O N S See also: Sangeet Chaudhary http://platformed.info/virality-viral-growth-network-effects/

To sum up: Network effects
vs Virality vs Economies of scale Network effects Virality Economies of scale 1P

SUPPLY SIDE Economies of scale
(also referred to as just ‘economies of scale’) is a function of production size; so scale leads to lower cost per unit of output (unit economic efficiency) DEMAND SIDE Economies of scale (also referred to as network effects) is a function of users, so with scale leads to more utility for users They’re both competitive moats, but network effects tend to be stronger—users have higher barriers to exit Zooming in on economies of scale

Amazon’s ecommerce site (1P) has
supply-side economies of scale—shared warehouse facilities, cheaper shipping options, etc.— that benefit Amazon in the form of purchasing power and buyers in the form of lower costs Amazon’s peer-to-peer marketplace (3P) has demand-side economies of scale—aka network effects—which help make it the winner-take-all… is growing much faster than the ecommerce aspect of the site Example: amazon.com

Strategies: Don’t forget these less
obvious, but no less important, sources of network effects Hardware Infrastructure 1 2 Data 3

“ MAX LEVCHIN: The defensibility
of these businesses lies in their ability to build…a network effect of data. MATT TURCK: Data network effects occur when your product, generally powered by machine learning, becomes smarter as it gets more data from your users. In other words: the more users use your product, the more data they contribute; the more data they contribute, the smarter your product becomes (which can mean anything from core performance improvements to predictions, recommendations, personalization, etc.); the smarter your product is, the better it serves your users and the more likely they are to come back often and contribute more data—and so on and so forth. Simply put, a data network effect is a network effect that results from data Sources: http://max.levch.in/post/41116802381/dld13-keynote http://mattturck.com/2016/01/04/the-power-of-data-network-effects/

But simply having a lot
of data does not a data network effect make! The data needs to not only benefit from/extract that data, but also add value back to the network of users

A missing graph we’d love
to see in pitches that posit data network effects is something that answers this: How much data do you have over time? How much does the value of the product or service increase as a result of the data?

Chris Dixon Jeff Jordan Sonal
Chokshi Kathy Wang A D D I T I O N A L A C K N O W L E D G E M E N T S ( N O T I N C L U D I N G E X P E R T S & A R T I C L E S C I T E D A S S O U R C E S T H R O U G H O U T )