Platforms face unique constraints that make them very sensitive to friction — this limits their monetization options to just 3 out of the 6 models used by other types of networks

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Platforms share many characteristics with marketplaces. They create value by connecting demand with supply — specifically, they connect users with app developers. So it is natural to assume that platforms have the same monetization options available to them as marketplaces do. But in reality, platforms are far more constrained in the ways they can monetize.

Before going further, let me recap what I mean by the term “platform” — it refers to a combination of an underlying product, a software development framework, a way to “match” users with apps, and an economic benefit for developers. The last two elements overlap with the characteristics of marketplaces. …


Data networks can use five out of six possible monetization models, depending on the way they acquire data from users and the way that data is consumed

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Data networks are unique within the world of network effects. Most network types create value by allowing participants to interact with each other in some way. Data networks, however, do not connect participants directly. Instead, they crowdsource data from participants to improve the product for all of them. This has a direct impact on the way they monetize. For one, it automatically invalidates one of the monetization models used by other networks — interaction taxes (or commissions). Since there are no direct interactions between participants, they cannot be taxed. …


Marketplace monetization depends on two factors — Transaction complexity and the asymmetry in value to the demand and supply sides

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Like other types of startups built on network effects, marketplaces create value by connecting participants. Specifically, they connect demand with supply to enable transactions. This gives them an obvious way to monetize — take a cut of every transaction. However, this cannot be blindly applied to all marketplaces as there are constraints involved. Depending on these constraints, marketplaces can choose between five out of the six possible monetization models.

In my last post, I explained that monetization on interaction networks was a function of network structure, i.e. the nature of relationships between network participants. This is true for marketplaces as well. However, these relationships are governed by different factors on marketplaces. …


Interaction networks can use one or more of six possible monetization models — the choice between them depends on the structure of their network

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Startups succeed by uncovering a unique insight to create value for their users. This value creation is only sustainable if they can find a way to capture some of it themselves, i.e. monetize. This is just as true for startups built on network effects. However, they are more complex to monetize than traditional business models. This is because they primarily create value by connecting participants, not just by developing a standalone product. So in order for value creation to continue, their monetization model needs to be aligned with the incentives of all participants. …


There is a long list of metrics that attempt to measure network effects — putting them in context is the best way to identify the most relevant KPIs for each situation

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Metrics (or KPIs) are among the most well-researched topics within the startup ecosystem. This is just as true for those built on network effects. There are great resources available from A16Z, Point Nine Capital, and Speedinvest Pirates just to name a few. The goal of this post is not to simply list all these metrics again. Rather, it is to put these metrics in context and explain when each is most relevant.

The most important metrics for any startup can vary based on their lifecycle stage. Network startups have an additional consideration because they need to gain critical mass (or liquidity) before network effects can kick in. As a result, the most important pre-liquidity metrics vary from those that should be prioritized post-liquidity. And finally, some of these metrics can also vary based on the type of network. …


There are two types of negative network effects in the software realm — networks can choose between five curation mechanisms to overcome them

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So far, I have explained various characteristics of network effects and their impact on scalability, defensibility, liquidity, and monetization. The implicit assumption here is that interactions between participants are positive for everyone involved. This is true most of the time, but not all of the time. Interactions between network participants can also be negative. As a result, successful networks need to put curation mechanisms in place to encourage positive interactions; and dissuade or prevent negative ones. Let’s take a deeper look at what these negative network effects look like and the curation mechanisms to mitigate their impact.

Negative Network Effects

The definition of positive network effects is that the addition of a user makes the network more valuable for all users. So negative network effects exist when the addition of a user makes the network less valuable for all users. In software networks, negative network effects come in two…


The economic incentives of a platform determine its liquidity barriers — but they also create long-term trade-offs with the effectiveness of developer marketing programs

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Network effects can only take hold when a product has reached a minimum threshold or critical mass of users (also called liquidity) — this is true for marketplaces, interaction networks, and data networks. Platforms, on the other hand, are unique because they are always built on top of another product with existing adoption. So, as we saw with SaaS-enabled marketplaces, it is natural to assume that platforms can leverage these existing customers to attract a critical mass of developers. Wouldn’t they have liquidity right from the get-go? Not always.

Platforms are a combination of four elements — an underlying product, a development framework, a storefront to “match” users with apps, and an economic benefit for developers. Thanks to the underlying product (and existing customers), fledgling platforms already have a critical mass of demand. As a result, liquidity is purely a function of supply, i.e. developer adoption. This is driven by their economic incentive which varies based on the type of platform in question. I previously identified two types of platforms, each of which creates different economic incentives for developers, leading to different liquidity…


The rate of data decay, geographic constraints, and the method of data acquisition combine to define liquidity barriers for data networks — traits that strain liquidity should ideally be balanced by those that ease it

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Data network effects are a tricky beast and come with a difficult set of trade-offs. But these trade-offs only become meaningful after the data network has gained critical mass. The considerations for gaining critical mass on a data network are largely unique from other models because users don’t interact with each other — they just interact with a product that is augmented by crowdsourced data. This results in even more trade-offs that add to the complications of building a data network.

For data networks, critical mass (or liquidity) is best defined as the minimum quantity and quality of crowdsourced data required to create a valuable product. This is influenced by three factors — the rate of data decay, how “local” the data is, and the method of data acquisition. The first two of these factors were also the primary determinants of defensibility and scalability. …


Synchronous networks like Meerkat and Clubhouse are susceptible to liquidity challenges because they need users to be active at the same time — pivoting to emphasize asynchronous features is one way to overcome these challenges

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In the startup world, time is primarily viewed as a hurdle to be removed — everything needs to be instant and real-time. This is certainly valuable as a general principle, and it can even be critical to the defensibility of certain types of startups, i.e. data networks. However, blindly applying this principle in all situations can create complications. Time-delayed behavior is sometimes a requirement to gain critical mass, in particular for interaction networks — ones that connect specific users to enable interactions, e.g. social networks.

I have previously explained how network structure influences the potential of network businesses — this includes the presence of network bridges, importance of user identity, nature of connections, and network density. However, this is irrelevant if a network cannot sustainably build critical mass or liquidity in the first place, i.e. it needs to have a minimum density of users that can interact with each other on an ongoing basis. While some elements of network structure can have an impact on liquidity, they are better viewed as secondary constraints. The primary determinant of liquidity is how “real-time” an interaction network is — whether it is asynchronous or synchronous. …


Making networks redundant is one way of disrupting strong network effects, but the cost of this is to sacrifice network effects yourself — Startups with foresight should build in new forms of defensibility to compensate for this

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Network effects are among the most powerful economic forces in technology and have created trillions in value. The reason for this value creation is not just compounding, but also the defensibility created by network effects. These advantages have allowed network effect-based startups to disrupt incumbent SaaS players. But there are also immensely valuable incumbents who are built on network effects themselves. Is there any way to disrupt them?

The short answer is yes. Often, incumbent networks are disrupted in the same way other types of businesses are. Startups create new value propositions that initially target novel, low-value markets, and gradually encroach on the incumbent’s market. For example, Airbnb started by allowing its original hosts to rent out spare beds to guests. Their approach unlocked new types of supply and eventually created new holiday experiences rivaling hotels. This was a true disruption to Booking.com’s hotel reservation marketplace. Startups following this approach have to contend with a near-term risk, i.e. the risk that there is no market for their product. But if they cross that hurdle, they have the opportunity to create strong network effects. …

About

Sameer Singh

Network Effects Advisor, Author & Investor

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