A phenomenon known as Network Effect happens when a product’s, a service’s or a platform’s value rises as more people use it.
In other words, the network effect describes any circumstance in which the value of a product, service, or platform is determined by the number of buyers, sellers, or users who utilize it. The bigger the network effect—and the larger the value generated by the offering—the more customers, sellers, or users there are.
Assume you and your best buddy have the planet’s only two phones. You could have a great time, but there wasn’t much more you could do.
Now, imagine if your entire village had phones!
When more people obtain phones, the value of your phone (and the phone network) rises since you can then contact more friends.
Metcalfe’s Law and Social Media
In Telecommunications Engineering, this phenomenon is formalized through Metcalfe’s Law – a model first formulated by George Gilder in 1993 and later attributed to Robert Metcalfe, the American engineer who coinvented Ethernet.
Metcalfe’s Law states that the value of a network is proportional to the square of the number of its nodes:
In our day-to-day life, we are very familiar with Network Effect, although many of us may never really think about it.
Social networks are in fact a prominent example of platforms subject to network effects: the more users join a network, the more valuable the network becomes.
Your incentive to use Facebook, Twitter, Instagram, YouTube or any similar service increases with the number of people you can distribute your message to. This is why social networks are free to use – in order to maximize their service potential, they need to acquire as many users as possible.
This 5-month old video by YouTube creator Mr Beast was seen by 123 million people as of today. By comparison, the Academy Awards event of 2021 (popularly known as “the Oscars”) has racked up an all-time-low of 10.7 million viewers. That’s an ~11x difference. I find this mind-blowing!
When platforms are successful in achieving network effect, they create winner-take-all or winner-take-most type of markets. Once you’re ahead, you tend to stay ahead. Perhaps counterintuitively, your demand keeps growing even more rapidly as you get bigger.
Greater scale means more value for users… which attracts more users and expands scale even further.
Once network effects kick in, this rich-becomes-richer dynamic enables networks to expand quickly.
That is exactly how:
- Apple achieved supremacy in apps
- Google achieved supremacy in search
- Amazon achieved supremacy in e-commerce
- YouTube achieved supremacy in video sharing
- Facebook achieved supremacy in social media
- LinkedIn achieved supremacy in professional networking
How value is created through Network Effect
In practice, we can distinguish two types of network effect:
- Direct network effects occur when the value of a product, service, or platform increases simply because the number of users increases, causing the network itself to grow.
- Indirect network effects occur when a platform or service depends on two or more user groups, such as producers and consumers, buyers and sellers, or users and developers. As more people from one group join the platform, the other group receives a greater value amount (for example, e-commerce platforms like eBay or AliBaba and ridesharing platforms like Uber or Ola)
In this type of platforms, value is mainly generated through Connection and Content. Let’s briefly review.
- Value generated by connection: the simplest form of network effect generated value. Because more and more users are joining a network, each user receives a higher value from the network itself. LinkedIn is a good example.
- Value generated by content: as more users join a network and create content on it, the higher the value that the users put on the network. And because more and more users join a network, more content is generated on the platform which in turn attracts even more users. Quora and YouTube are two good examples.
To recap, value is generated on a network in the form of:
- Higher opportunity for connections for the users;
- A wider collection of content for the userbase.
How value is destroyed through Network Effect
As networks scale, we need to do some considerations around relative value increase. It could happen that, as networks get bigger, the value per user actually gets smaller.
Going back to to our Connection/Content framework, this may happen because:
- Connection: the newest users may lower the quality of the userbase and attempt to create connections that are not in line with the network philosophy (eg spamming other users)
- Content: as more creators join the network, the quality of the content may go down as well as the level of curation of new content and the accuracy of delivery to relevant users (a user may start to see low quality content that she’s not interested in).
The power of network effect manifests as a positive but also as a negative. The same law that allows a platform to scale at exponential speed when certain conditions are met, is responsible for a rapid decrease of the value of the network when those conditions stop being fulfilled.
Let’s discuss.
Lack of friction
Social network platforms gain their value by their increasing number of connections. More people join a network, the higher the value.
Up to a certain point.
If too many non genuinely interested users start joining a network and the quality of the users decrease, this is not good news for the platform. In the last decades, this phenomenon has lead to the deaths (complete or partial) of Friendster or MySpace for example. If there’s not enough friction to introduce new users/connections then the value of the network gets impacted. That’s the reason why on LinkedIn, for example, you can’t just connect or message anybody: to reduce spammy behaviors and create friction in the way non genuine users would utilize the platform.
If the friction in enabling connection or content generation is low, the network needs to defend itself with a strong curation mechanism. A platform that, as a consequence of scaling up, feeds its users with irrelevant (and bad) content is doomed and will see its users leaving it at the same speed they had joined it.
Platforms like Reddit and Quora have a well thought curation system, where users can vote for the content they prefer (hence allowing increased eye balls only for good content). Other platforms, like YouTube, combine a human voting mechanism with an algorithm, in an effort to have a curation approach that scales well.
Which leads me to the next point: failure in hyper-personalization.
Failure in hyper-personalization
For users to stay engaged overtime on a platform, the algorithm must keep serving them content they are interested in. If users are not able to effortlessly receive content that catalyze their attention, they spend less time on the platform and, eventually, may leave it completely. Social ecosystems that fail in delivering a hyper-personalized experience to their users are bound to face reverse network-effect. This could happen for many reasons:
- algorithm inaccuracy (not enough interesting posts are put in front of users);
- too much noise, not enough signal (irrelevant content, too many ads);
- users’ misbehavior (platform value proposition may be unclear or users add too many connections lightly, that affect the quality of the feed)
To recap, value is destroyed on a network because of:
- Lack of friction for users to add new connection or content;
- Failure in hyper-personalization in serving content to users.
Is it just Social Media?
Does network effect only apply to social media?
Definitely not.
In Financial Services, companies like PayPal or Square, and most of the fintech/neo-bank players, all have a similar strategy: build on network effect with peer-to-peer payments to spread app usage, introduce useful auxiliary services to increase “stickiness” and, importantly, revenue per customer, then morph into a one-stop-shop banking solution from lending to crypto.
The recent acquisition of Afterpay by Square or the words of PayPal’s CEO Dan Schulman are evidence of this.
In the last years, network effect has also heavily influenced the cryptoventures space.
As a prominent example we can cite Bitcoin, whose protocol, according to many, benefited of a network effect that is too strong to stop, and that could continue to expand exponentially until it eats the global financial system (process often referred to as hyper-bitcoinization).
A 2020 study by NYDIG, a technology and financial services firm dedicated to Bitcoin for institutional investors, shows that Bitcoin’s valuation is well described by the most fundamental factor intrinsic to its network: the number of addresses that hold Bitcoin.
Interestingly, NYDIG research shows that the expanding monthly regression analysis beginning in 2011 highlights the fact that the number of Bitcoin addresses squared explains 93.8% of the variation in the level of Bitcoin’s market cap (picture below).
I didn’t find any study, but my gut feeling is that similar observations may apply to most of the large cap cryptocurrencies – not just Bitcoin.
Conclusive thoughts
The consequences of the network effect phenomenon are powerful for platforms, services and products that are able to leverage it. Network effect allows information, and now also value, to spread to billions of people globally at unprecedented speed.
Network effect doesn’t only apply to businesses, but also to open protocols like the ones we use for the internet, email or Bitcoin.
Understanding network effects may not be immediate for most people but it can give tremendous insights around some of the forces that dominate the technology worlds around us.