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

Image for post
Image for post
Image credit: Unsplash

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

Image for post
Image for post
Image credit: Shutterstock

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

Image for post
Image for post
Image credit: Shutterstock

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. …


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

Image for post
Image for post
Image credit: Unsplash

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

Image for post
Image for post
Image credit: Unsplash

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

Image for post
Image for post
Image credit: Unsplash

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

Image for post
Image for post
Image credit: Unsplash

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. …


The network effect between supply and demand is the defining feature of a marketplace — sacrificing this in the pursuit of better customer experience can create unexpected trade-offs

Image for post
Image for post
Image credit: Shutterstock

The wave of unicorns we have seen over the past decade has been partially populated by what VCs and founders call “managed marketplaces”. This term has been used to describe a dizzying range of business models including consignments, verified matchmakers, iBuyers, asset rental services, etc. So, much like the term “platform”, the term “managed marketplace” has been used so broadly that it has lost all meaning. Let’s take a deeper look at what managed marketplaces really are and what happens when they can no longer be categorized in that bucket.

Before diving into managed marketplaces, we first need to understand what a marketplace is and why they have network effects. As I have previously explained, a marketplace connects two or more distinct types of participants to enable transactions, i.e. buy and sell products or services. The addition of each seller increases the value of the marketplace for all buyers and vice versa — this network effect is a critical facet that fuels the scalability and defensibility of marketplaces. While the strength of these network effects can vary, their existence is the defining feature of a marketplace. …


Dense network connections and a consistently high creator-to-consumer (CTC) ratio can mitigate some of the challenges faced by weaker networks

Image for post
Image for post
Image credit: Shutterstock

Most attempts at understanding network businesses revolve around studying user engagement. This is measured by a range of metrics including frequency of use, time spent, number of payments, etc. However, this tends to gloss over a very important fact — engagement is an effect, not a cause. If we want to truly understand what makes network startups work, we need to begin with the underlying cause of their engagement pattern — their network structure.

For the purpose of this discussion, let’s restrict ourselves to interaction networks, i.e. networks that connect users to enable interactions and information flow. This category can include social networks like Facebook, collaboration networks like Slack, payment networks like Paypal, etc. …


Startups can layer additional network effects on top of their business models by adding new participants or creating new connections between existing participants

Image for post
Image for post
Image credit: Unsplash

So far, I have discussed four different types of business models built on network effects — marketplaces, interaction networks, data networks, and platforms. Including the sub-categories I previously explained, this covers all discrete forms of software-based network effects. However, it is still not exhaustive because many startups fall under more than one of these categories. In fact, combining multiple forms of network effects, like Slack, Carta, and Poshmark have done, is one of the most effective ways to strengthen both defensibility and scalability.

There are a couple of basic techniques that startups can follow to layer additional network effects onto their business models. …

About

Sameer Singh

Network Effects Advisor, Author & Investor

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store