Network Effects In A Nutshell

A network effect is a phenomenon in which as more people or users join a platform, the more the value of the service offered by the platform improves for those joining afterward. Imagine the case of a platform like LinkedIn. For each additional user, joining, which also enriches the online resume, makes the platform more valuable to recruiters, as they can easily find qualified candidates. This is an example of a two-sided network effect. 

Definition Network Effects: The value of a service/platform inreases for each additional user, as more users join in. Sub-type Description – Example
Direct, Same Side or One-Sided   As more users join, the platform’s value increases for each additional user. Take the case of a social media platform, like Facebook, Instagram, TikTok, LinkedIn, Twitter. The more users join, the more the platform will be valuable for each additional user, as the new user might find exponentially richer and broader content (provided the platform can prevent congestion or pollution).
Indirect or Cross-Side   In this case, a user type joining the platform makes it more valuable for other user types. Take the case of LinkedIn. While LinkedIn enjoys the same-side network effects, the platform becomes more valuable to users looking to enhance their careers as more users join in. At the same time, LinkedIn enjoys indirect or cross-side network effects. More users who join the platform to grow their career make it more valuable for recruiters (so a different user type) as they can find more qualified candidates on top of the platform.
  Two-Sided Take the case of LinkedIn. While LinkedIn enjoys the same-side network effects, the platform becomes more valuable to users looking to enhance their careers as more users join in. At the same time, LinkedIn enjoys indirect or cross-side network effects. In this case, a user type joining the platform makes it more valuable for other user types. More users who join the platform to grow their career make it more valuable for recruiters (so a different user type) as they can find more qualified candidates on top of the platform.
  Multi-Sided In this case, more than two user types are driven by the network dynamics. Take the case of Uber Eats; the more restaurants join the platform, the more the platform becomes valuable for eaters. While at the same time, by leveraging on its existing platform, Uber drivers have additional riding options. So they can earn extra income by delivering food instead of giving rides. That makes the overall platform much more valuable for the three main user types: eaters, restaurants, and riders.
Definition Negative Network Effects: The Value of the sercice/platform decreases for each additioanl user, as more users join in. This might be due to congestion (when increased usage can’t be handled by the platform) or pollution (when the increased size of the network makes it hard to incrementally add value, and instead its value shriks). Description – Example
Congestion (Increased Usage) In this case, there is a reduced quality of the service when certain parts of the networks carry way more data than they can handle. That usually happens because of scale limitations and noise due to curation limitations. Since this is a technological issue, it manifests as service slowdown or perhaps the platform crashing. Take the case of services like YouTube crashing for too much traffic. Or, if you’re a professional, a service like Slack crashes as it cannot handle the traffic spikes. That becomes a disservice with potential negative network effects because you suddenly prevent a whole team from functioning properly. Therefore, a negative network effect can have exponential negative consequences. For instance, users would switch to alternatives en masse if this was repeatedly happening, thus creating structural damage to the network.
Network Pollution (Increased Size) The case of pollution is more tied to the ability of the platform to keep its service relevant at scale. Thus, imagine the case of a platform like Twitter, in which the principal asset is the feed. As Twitter becomes more and more popular, it needs to make sure that the user-generated content is qualitatively on target. Otherwise, the risk is for the user’s Twitter feed to become less relevant and lose value. Or imagine the case that many user-generated platforms face today, where spambots take over. Here, suppose the platform cannot handle this automatically generated content. In that case, it can quickly lose value, as the service becomes worthless for users (take the case of a user who has to spend an hour a day cleaning up the feed because of spamming).

Platform business models and network effects


Network effects have become an essential element of a successful digital business, for several reasons. First, the Internet itself has become a facilitator for network effects.

As it becomes less and less expensive to connect users on platforms, those able to attract them in mass become extremely valuable over time.

Also, network effects facilitate scale. As digital businesses and platforms scale, they gain a competitive advantage, as they control more of the total shares of a market.

Last but not least, as we will see, network effects are considered among the defendable, or what confers to digital business, a competitive advantage.

Where in the past linear businesses gained a competitive advantage by buying assets and controlling supply chains. Digital companies gain competitive advantages by building network effects.

As we’ll see there are different kinds of network effects. And network effects can also be reverse or negative. 

Competitive advantage = network effects

If you think about something like Instagram, the software is great but could fairly easily be replicated. But the network of users and the content they create is impossible to replicate. That is where the value is created. Or think about Reddit where the community creates the content. Or think about Waze where the users generate the data about which way has the least traffic.

Fred Wilson, AVC, Business Model Innovation.

In the era of platform business models in most cases, technology can bring an initial advantage. However, over time technology might become a commodity as it can be easily copied and replicated as it becomes wider adopted.

What can’t be easily copied is the community comprised of the network of users part of the platform. That is because users interacting produce several positive network effects.

Positive network effects

As more users join the platform the more it becomes valuable. Think of the case of a dating app with a few users in your town. How many chances there are the app would be able to match you up quickly? A few, thus the app won’t be much valuable to you

Data ownership

The interactions between users also generate data that the platforms can control, analyze, and own, which can be leveraged to create a lasting advantage. Therefore, the value of the network isn’t necessarily in the technology but in the data produced by the interactions

From transactions to interactions

As network effects become a primary advantage of the platform business, which can’t be easily replicated. The company needs to shift its mindset and manage the interactions in the platform. This is a key step to take. Think of the case of a company like Amazon, which over the years passed from being e-commerce to a platform business.

Indeed, most of. the transactions that happen today on Amazon are mostly from third-party sellers. While Amazon understood the value of network effects and of flywheel models early on.

A platform business model implies that the company in charge of the ecosystem that generates those network effects learns how to keep those interactions happening. This implies a shift in mindset to think in terms of product sales to interactions happening on the platform.

To understand this concept read the full interview on digital platform businesses. 

The power of network effects


Image credit: Ray Stern, CMO of Intuit.

Network Effects enable digital businesses to gain value quickly. That is because they have built-in asymmetries between the costs and value of the network. Where costs might increase linearly, the value of the network increases exponentially as the network grows.

Network effects have become a key ingredient in the digital era enabling the dominance of a particular business model: the platform business models.

The era of platform business models


Platform business models make up most of the value captured and created by digital businesses.

Companies like Google, Facebook, LinkedIn, PayPal, and more are platform business models, which benefited and created a strong competitive advantage by leveraging network effects.


Image Credit: Applico, Inc.

That’s because, in theory, platform business models manage to scale efficiently. Thus, where a traditional business, at a particular scale, it reaches a point of inefficiency where diseconomies of scale pick up.

A digital, platform business, might scale so efficiently, to be able to grow close to the total size of the market. This enables the formation of monopolies.


Thus, network effects become the real “assets” in the digital era. However, those “assets” won’t be seen on the company’s balance sheets.

Quite the opposite, platform business models enable exchanges among a large number of people within a network, but in most cases, they don’t control any of the assets owned by the people in the network.

Instead, those platform businesses only facilitate exchanges. And as a facilitator, they collect a “tax” as a transaction fee. That’s why modern platform business models might look and act more like nations, rather than corporations.

Read: Platform Business Models In A Nutshell

Types of network effects

Examples of network effects


Source and Image Credit:

NFX points out thirteen main types of network effects:

  1. Physical (e.g., landline telephones)
  2. Protocol (e.g., Ethernet)
  3. Personal Utility (e.g., iMessage, WhatsApp)
  4. Personal (e.g., Facebook)
  5. Market Network (e.g., HoneyBook, AngelList)
  6. Marketplace (e.g., eBay, Craigslist)
  7. Platform (e.g., Windows, iOS, Android)
  8. Asymptotic Marketplace (e.g., Uber, Lyft)
  9. Data (e.g., Waze, Yelp!)
  10. Tech Performance (e.g., BitTorrent, Skype)
  11. Language (e.g., Google, Xerox)
  12. Belief (currencies, religions)
  13. Bandwagon (e.g., Slack, Apple)

As James Currier, from NFX, points out, “Network effects have emerged as the native defense in the digital world.” Within network effects as a defensible NFX points out three key elements: scale, brand, and embedding.

It is essential to highlight that the types of networks above are not exhaustive, neither set in the stone. But the framework offered by NFX is a great starting point to understand how network effects work.

In this guide, I want to focus on two main kinds of network effects:

  • Direct or same-side.
  • And indirect or cross-side.

Direct or same-side network effects

Direct or same-side network effects happen when an increasing number of users or customers also increases the value of the product or service for the same kind of user.

Direct network effects usually follow Metcalfe’s law (one of the laws on the basis of network effects).

In short, Metcalfe’s law, developed in communications theory, states that, as users of a network grow, this enables the exponential growth in the number of potential connections, thus also an exponential increase in utility of the platform.

Indirect or cross-side network effects

Indirect or cross-side network effects happen when an increased number of users on the side of the platform drives up the value of the product or service offered for the other side of the platform.

Indirect network effects aren’t necessarily symmetric. In other words, in some cases, increasing one side of a platform might have more profound effects, than increasing the other side.

For instance, in Uber’s case, as a two-sided platform, driven by the exchanges between drivers and riders, the former plays a more critical role.

Indeed, Uber uses dynamic pricing strategies that make the service less convenient for riders, but it keeps drivers going back to the platform.

Also, indirect network effects might not necessarily be reciprocal. Thus, increasing the one side of the network might improve the service for the other side. But the same might not apply if the other side of the network is increased.

Virality vs. network effects



I want to clarify the critical difference between virality and network effects. Often (too often) those terms are used, or thought of as the same thing.

However, they are not, and understanding the critical difference between the two is vital to also formulate a better business strategy for a platform business model.

Network effects happen when a platform becomes more valuable as more users join it. Virality instead is a growth tool that companies can use to create more exposure for their product or platform.

Thus, a network effect is a way to create a lasting competitive advantage. And to offer more value to users. A viral effect is primarily a marketing tactic to gain traction and visibility for your product.

Network effects and virality can work together. For instance, as more users join through viral effects, if the platform is taking advantage of network effects, the more also it will become prone to improve its virality.

As highlighted in the interview with Sangeet Paul Choudary, best selling author of Platform Scale and Platform Revolution:

Network effects create value on the platform. Viral effects spread the word about the platform or the product externally.

And he continued:

So network effects, an example is the more users who are on Airbnb. The more hosts are setting up listings on Airbnb, the more choice there is for travelers. Now that’s a network effect.

Or take the example of YouTube, the more videos that are being set up on YouTube, the more choice I have as a viewer to view things on YouTube.

Instead, virality happens when:

Now, if I take a video from YouTube and embed it on Facebook, that’s not a network effect, that’s a viral effect.

In short:

So a viral effect is a growth tool that brings external users back to the platform. Whereas a network effect increases the value on the platform, just like adding more than more and more videos onto YouTube.

Beware of negative network effects

In a negative network effect as the network grows in usage or scale, the value of the platform might shrink. In platform business models network effects help the platform become more valuable for the next user joining. In negative network effects (congestion or pollution) reduce the value of the platform for the next user joining.

Network congestion 

As highlighted in the interview with Sangeet Paul Choudary, author of Platform Scale and Platform Revolution:

The more people using the highway system, the more traffic jams you end up in. Or the more people in a room, the less likely it is to have good decent conversation just because it gets crowded, but also because everybody is talking too loudly and so you can’t hear and you can’t meet the right person within that room.

He continues:

So we understand congestion in traditional terms because in the traditional world we have networks that were limited by scale.

When it comes to the digital world, instead, where there are fewer scale limitations. Or at least those can be initially overcome.

Thus, at least on the congestion side, it’s very hard to reach a point of negative network effects (for instance, if the platform crashes for usage). 

Congestion, therefore, is primarily about the usage of the network. 

In the bits world, it is possible not only to overcome congestion but also to build a more solid engineering infrastructure as usage increases. Of course, network congestions can also be bad for platforms (poor network design, over-subscription, security attach due to overused network parts). 

Another form, of negative network effect, can take place for platform business models. 

Network pollution 

Where network congestion is primarily about the size of the platform. 

As the platform scales, it gains network effects, as it becomes more valuable for an increasing number of users.

Yet, also for digital platforms scale might create situations of diseconomies

As highlighted in the interview with Sangeet Paul Choudary:

What happens is the more users come on board, the more difficult it becomes to manage quality of the interactions.

Thus this makes the digital platform make sure to have a mechanism to manage the quality of those interactions to avoid that those negative network effects pick up.

If we think of social media, or publishing platforms at scale, among the most difficult task, that requires dozens if not hundreds of engineers and humans (Google might have thousands of human quality raters)  to fix spam and low-quality user-generated content. 

Examples of negative network effects

As highlighted negative network effects, dilute the value of the platform. And the way they can take over will depend on the kind of platform you’ve built. For instance:

Google case study

In a platform that leverages direct side network effects after a certain number of users, it might also result in increased spam on the platform which can’t be easily managed through automated processes, or human curation, thus diluting the value of the platform.

Take the case of how Google, back in the days, it was offering an index of the web with its core algorithm called PageRank. At a certain point had to figure out also how to manage the spam on its index. 

Practitioners understood how to trick Google’s core algorithm into showing up spammy results on top of that. This would have jeopardized the value of the overall platform, thus resulting in a diluted value of that.

Thus Google had to start to build up a solid team dedicated to spam and update its algorithms to avoid spam in search results in order to keep its platform valuable.

Airbnb case study

In two-sided platforms, a kind of user joining the platform makes it more valuable for another type of user.

Take Airbnb where, for instance, more hosts improve value for users on the platform. On the two-sided more value is created by more hosts (travelers have more selection)? On the other hand, the value of the platform is diluted on the hosts’ side of the platform.

They will find themselves competing for the same users.

Thus it becomes crucial to understand what’s the proper ratio between travelers and hosts on the platform to make sure it keeps being valuable on both sides. 

Tinder case study

Take the case of a dating app that draws value from having people encounter locally. If more users join but from locations spread across the world, no many local network effects are picking up.

Quite the opposite. If a critical mass is not reached at each local hub, the platform might lose value quickly. Imagine the case of a woman looking for a date. The faster she will be able to meet the best match.

The more the platform will be valuable. However, to be very valuable, the service has to make sure those people can meet in a place nearby. And it there are no matches available locally, the dating platform would lose value quickly.

Amazon from e-commerce to platform

When Amazon first started, its business model was an e-commerce. 

Over the years, especially in the early 2000s, Amazon started to explore the option to feature more and more third-party stores, on top of Amazon. 


By 2014, for the first time in its history, third-party sales passed the sales of Amazon own products. While the process of transitioning from e-commerce (simply selling its own products) to platform (enabling others to sell their own products), started in the early 2000s, it took over ten years for Amazon to complete this transition. 

And this was all about network effects!

Amazon has a diversified business model. In 2021 Amazon posted over $469 billion in revenues and over $33 billion in net profits. Online stores contributed to over 47% of Amazon revenues, Third-party Seller Services,  Amazon AWS, Subscription Services, Advertising revenues and Physical Stores.

Today, Amazon is a platform business model, with built-in network effects. 

Why is it about network effects?

Because, by the early 2000s, Amazon business model engine had to be structured in terms of network effects within its platform strategy

Indeed, the more third-party sellers joined the platform, the more the platform became valuable to users, who could find more product variety. 

And, Amazon could further pass lower prices to its customers. Once this process was repeated over and over, this is how Amazon built positive feedback loops into its business model. 

And this was a paradigm shift!

Netflix, from platform to media company

While some companies transition to a platform strategy, and therefore, need to start thinking in terms of network effects.

Other companies go into the opposite direction.

Take the case of Netflix business model:

Netflix is a subscription-based business model making money with three simple plans: basic, standard, and premium, giving access to stream series, movies, and shows. Leveraging on a streaming platform, Netflix generated over $29.6 billion in 2021, with an operating income of over $6 billion and a net income of over $5 billion.

Netflix started primarily with a platform strategy, offering licensed content on top of its platform.

It first transitioned from CD rental, to DVD subscription services. Then it moved to on-demand streaming, with an effective subscription model, with no surplus charges, neither late fees. 

Yet, starting in 2013, Netflix started to invest more and more on producing its own content!


By 2021, content produced by Netflix represented 34% of the total investments made in content. This is a huge jump, considering that in 2019 alone, that number was 21%.


This, I like to call, the “mediafication of Netflix” where Netflix is completing its transition from platform (where it solely focuses on building the underlying infrustructure to enable others to offer their content) to media powerhouse (think of it as the new Hollywood!). 

The on-demand streaming platform is still driven by network effects, but those are trickier. Indeed, those are more cultural network effects. 

Imagine the case of more and more people watching Netflix original series, those who don’t will feel left out, and they will feel compelled to join in! 

Key take on negative network effects

Platform business models can leverage network effects to enable the core platform to become more valuable over time. However, they need to factor in negative network effects, which if picking up might not only prevent the success of the business but also destroy it.

Key takeaways on network effects 

  • The internet has become a facilitator for network effects.
  • Digital businesses work on a set of premises and principles that are different from traditional or linear businesses.
  • Network effects have become the “assets” for digital organizations.
  • Those network effects don’t sit on companies’ balance sheets. Rather digital businesses can trigger and build them up to create a strong competitive advantage.
  • Network effects enable digital businesses to scale efficiently and to get close to the total size of the market.
  • Network effects can be direct (when they when an increased size of the network improves the value of the platform for the same kind of users) and indirect (where the increased size of one side of the network improves the service for the other side).
  • Network effects are not the same thing as virality. Virality is a marketing tactic to acquire users or customers at a lower cost. Network effects represent a business strategy aimed at creating a long-term competitive advantage for digital businesses.

Understanding what a platform strategy entails

When it comes to platform business models it’s important to think in terms of markets and ecosystems. In short, as the platform usually makes money as fee-based on the interactions among key players on the platform, its main role will be the development of the market it sits on. 

Market expansion consists of providing a product or service to a broader portion of an existing market or perhaps expanding that market. Or yet, market expansions can be about creating a whole new market. At each step, as a result, a company scales together with the market covered.

For instance, if we look at how various tech players evolved over the years based on a platform strategy, we can see how those first developed an ecosystem, and then “extracted” fees in the form of a tax from the key players once the new ecosystem had been built. Perhaps in the PC era, Microsoft’s Windows followed a platform strategy, where the PC worked as the hardware for Microsoft’s software products. On top of these, Microsoft built products, it helped other developers build their own products and it fostered an ecosystem to control the whole distribution. 

As we moved from PC to smartphone other players like Apple use the same platform strategy. Perhaps, what made the iPhone interesting in the first place were the apps available in it. The App Store, therefore, evolved as a platform, that Apple fostered over the years, and that in 2020 made Apple $64 billion in revenues, as reported by CNBC.

This is what a platform strategy entails. That is why platform business models are evaluated in terms of the market they can capture. 

A total addressable market or TAM is the available market for a product or service. That is a metric usually leveraged by startups to understand the business potential of an industry. Typically, a large addressable market is appealing to venture capitalists willing to back startups with extensive growth potential.

The wider the total addressable market, the more it will be interesting also for potential investors to finance the development of that ecosystem, as the platform will be able over time to extract value in form of revenues from that (here we’re not discussing whether that’s fair or not, but just how digital platforms evolved in the web era).

It’s worth noticing that platforms usually grow a business ecosystem by killing the fragmented intermediaries existing in a market (think of what Uber initially did with Taxis, and what Airbnb initially did with Hotels). While these players do eat up part of an existing marketplace, the main argument is also that those do expand the existing ecosystem, as they scale it up, thus bringing more potential customers into a system that before was clogged by too many intermediaries. 

As a quick example, many Uber users might use it in ways that never had been done with Taxis (perhaps with users going so far as to sell their cars to only moving locally through Uber’s app). 

That is also why marketing for a platform business model looks more like a flywheel, where there is a continuous loop between key players that feed each other’s thus making the flywheel spin faster. The classic example is the Amazon flywheel

The Amazon Flywheel or Amazon Virtuous Cycle is a strategy that leverages customer experience to drive traffic to the platform and third-party sellers. That improves the selections of goods, and Amazon further improves its cost structure so it can decrease prices which spins the flywheel.

Other examples are below: 

The virtuous cycle is a positive loop or a set of positive loops that trigger a non-linear growth. Indeed, in the context of digital platforms, virtuous cycles – also defined as flywheel models – help companies capture more market shares by accelerating growth. The classic example is Amazon’s lower prices driving more consumers, driving more sellers, thus improving variety and convenience, thus accelerating growth.
Read next: 

Other business resources: 

Business models case studies:

Connected Business Frameworks

AI Supply Chains

A classic supply chain moves from upstream to downstream, where the raw material is transformed into products, moved through logistics and distributed to final customers. A data supply chain moves in the opposite direction. The raw data is “sourced” from the customer/user. As it moves downstream, it gets processed and refined by proprietary algorithms and stored in data centers.

Bullwhip Effect

The bullwhip effect describes the increasing fluctuations in inventory in response to changing consumer demand as one moves up the supply chain. Observing, analyzing, and understanding how the bullwhip effect influences the whole supply chain can unlock important insights into various parts of it.

Supply Chain

The supply chain is the set of steps between the sourcing, manufacturing, distribution of a product up to the steps it takes to reach the final customer. It’s the set of step it takes to bring a product from raw material (for physical products) to final customers and how companies manage those processes.

Data Supply Chains

In a data supply chain the closer the data to the customer the more we’re moving downstream. For instance, when Google produced its own physical devices. While it moved upstream the physical supply chain (it became a manufacturer) it moved downstream the data supply chain as it got closer to consumers using those devices, so it could gather data directly from the market, without intermediaries.

Last Mile Delivery

Last-mile delivery consists of the set of activities in a supply chain that will bring the service and product to the final customer. The name “last mile” derives from the fact that indeed this usually refers to the final part of the supply chain journey, and yet this is extremely important, as it’s the most exposed, consumer-facing part.

Backward Chaining

Backward chaining, also called backward integration, describes a process where a company expands to fulfill roles previously held by other businesses further up the supply chain. It is a form of vertical integration where a company owns or controls its suppliers, distributors, or retail locations.

Revenue Modeling

Revenue model patterns are a way for companies to monetize their business models. A revenue model pattern is a crucial building block of a business model because it informs how the company will generate short-term financial resources to invest back into the business. Thus, the way a company makes money will also influence its overall business model.

Pricing Strategies

A pricing strategy or model helps companies find the pricing formula in fit with their business models. Thus aligning the customer needs with the product type while trying to enable profitability for the company. A good pricing strategy aligns the customer with the company’s long term financial sustainability to build a solid business model.

Dynamic Pricing


Price Sensitivity

Price sensitivity can be explained using the price elasticity of demand, a concept in economics that measures the variation in product demand as the price of the product itself varies. In consumer behavior, price sensitivity describes and measures fluctuations in product demand as the price of that product changes.

Price Ceiling

A price ceiling is a price control or limit on how high a price can be charged for a product, service, or commodity. Price ceilings are limits imposed on the price of a product, service, or commodity to protect consumers from prohibitively expensive items. These limits are usually imposed by the government but can also be set in the resale price maintenance (RPM) agreement between a product manufacturer and its distributors. 

Price Elasticity

Price elasticity measures the responsiveness of the quantity demanded or supplied of a good to a change in its price. It can be described as elastic, where consumers are responsive to price changes, or inelastic, where consumers are less responsive to price changes. Price elasticity, therefore, is a measure of how consumers react to the price of products and services.

Economies of Scale

In Economics, Economies of Scale is a theory for which, as companies grow, they gain cost advantages. More precisely, companies manage to benefit from these cost advantages as they grow, due to increased efficiency in production. Thus, as companies scale and increase production, a subsequent decrease in the costs associated with it will help the organization scale further.

Diseconomies of Scale

In Economics, a Diseconomy of Scale happens when a company has grown so large that its costs per unit will start to increase. Thus, losing the benefits of scale. That can happen due to several factors arising as a company scales. From coordination issues to management inefficiencies and lack of proper communication flows.

Network Effects

network effect is a phenomenon in which as more people or users join a platform, the more the value of the service offered by the platform improves for those joining afterward.

Negative Network Effects

In a negative network effect as the network grows in usage or scale, the value of the platform might shrink. In platform business models network effects help the platform become more valuable for the next user joining. In negative network effects (congestion or pollution) reduce the value of the platform for the next user joining. 

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