21 Sep 2020

Data Networks and Liquidity: How to Avoid Dead Ends

Data Network Liquidity

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.


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7 Sep 2020

The Network Liquidity Map: Why Time Matters


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.


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24 Aug 2020

How to Disrupt Network Effects

Disrupt Network Effects

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? 


10 Aug 2020

When is a Marketplace No Longer a Marketplace?

 

Marketplaces in name only


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.


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27 Jul 2020

How Network Structure Shapes Defensibility and Scalability

Network Structure

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.


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13 Jul 2020

Layering Network Effects: How to Multiply Unfair Advantages

Layering Network Effects

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.


29 Jun 2020

Internet Maturity & the Growing Importance of Network Effects


Technology Surge Cycle

John Luttig, from Founders Fund, recently wrote a great post outlining the macro-level challenges facing the startup ecosystem. It is worth reading in its entirety, but here’s a summary for the purpose of this post: Over the past decade, the surge of consumer and enterprise software startups has been fuelled by rapid growth in internet adoption. Now as adoption nears saturation (or maturity, as described by Carlota Perez on her Technology Surge Cycle), the tailwinds pushing the market forward are getting weaker. At the top of this S-curve, there is less opportunity to create new markets by targeting “non-consumption”. Instead, startups will need to challenge incumbents who were themselves VC funded startups until recently. In other words, the pie has stopped growing (or its growth has slowed), so now everyone needs to fight for a slice. While there is certainly a lot of truth to this, it is in the nature of macro-level analyses to obscure micro-level details. And these details often present the most valuable opportunities.


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15 Jun 2020

The Platform Matrix: All Platforms Are Not Created Equal


Developer Platforms

I have previously explained how network effects shape three broad types of tech businesses — marketplaces, interaction networks and data networks. In addition to these, there is one other type of business where network effects play a central role — platforms. Unfortunately, the tech and startup world has spent much of the past decade using the term “platform” to describe everything from operating systems to analytics tools, algorithms, APIs, etc. Quite plainly, if everything is a platform, then nothing is and the term loses all meaning. So let’s take a more granular look at what platforms really are, and then unpack how their network effects work.


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1 Jun 2020

Marketplace Liquidity: How Side Switching Can Help


Marketplace Liquidity

So far, I have explained some of the key characteristics of marketplace network effects. This includes the impact of fragmentation, geographic range, supply differentiation, SaaS integration and engagement. But none of this is remotely useful unless startups can actually get transactions flowing on their marketplace. In order to bootstrap these interactions, marketplaces need to hit a critical mass of demand and supply. In other words, they need to have “liquidity”.


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18 May 2020

The Data Matrix and the Data Decay Dilemma

Data Network Effects


The growth in internet adoption, among both businesses and consumers, has led to an explosion in the volume of data in the world. This has naturally led to more entrepreneurial interest, resulting in numerous data-based ("big data" or "artificial intelligence") startups and new business models claimed to be built on "data network effects". It is usually believed that more data leads to stronger data network effects, but this is often not the case. Data network effects are more misunderstood, much rarer, more difficult to establish and even harder to recognize than "traditional" network effects.