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.
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.
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.
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. So data networks are left with five of the six monetization models I have previously listed.
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.
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. As a result, the relationship between these participants exerts an outsized influence on the choice of monetization model.
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.
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.
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 be dangerous. Time-delayed behavior can sometimes be a requirement to gain critical mass, in particular for interaction networks — ones that connect specific users to enable interactions, e.g. social networks.
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?
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