Tech-Thoughts

Measuring Network Effects: Metrics in Context

2021-01-25T09:28:00.001+00:00

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.

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Curation: How to Beat Negative Network Effects

2021-01-11T09:30:00.000+00:00

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.

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Platform Monetization: Dealing with High Adoption Barriers

2020-12-07T09:29:00.002+00:00

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.

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How to Monetize Data Networks: Focus on Acquisition and Usage

2020-11-23T09:29:00.001+00:00

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.

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The Marketplace Monetization Map: Complexity and Asymmetry

2020-11-09T09:24:00.001+00:00

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.

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The Network Monetization Map: Aligning Incentives with Revenue

2020-10-19T09:29:00.003+01:00

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.

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Platform Liquidity: Why Economic Incentives Matter

2020-10-05T09:35:00.001+01:00

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.

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Data Networks and Liquidity: How to Avoid Dead Ends

2020-09-21T09:29:00.002+01:00

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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The Network Liquidity Map: Why Time Matters

2020-09-07T09:29:00.001+01:00

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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How to Disrupt Network Effects

2020-08-24T09:30:00.001+01:00

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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When is a Marketplace No Longer a Marketplace?

2020-08-10T09:26:00.002+01:00

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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How Network Structure Shapes Defensibility and Scalability

2020-07-27T09:28:00.003+01:00

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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Layering Network Effects: How to Multiply Unfair Advantages

2020-07-13T09:35:00.000+01:00

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.

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Internet Maturity & the Growing Importance of Network Effects

2020-06-29T09:26:00.000+01:00

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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The Platform Matrix: All Platforms Are Not Created Equal

2020-06-15T09:28:00.001+01:00

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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Marketplace Liquidity: How Side Switching Can Help

2020-06-01T09:35:00.003+01:00

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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The Data Matrix and the Data Decay Dilemma

2020-05-18T09:32:00.001+01:00

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.

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Why Marketplaces Fail: The Role of Engagement

2020-05-04T09:31:00.003+01:00

So far, I have discussed a handful of characteristics that create structural advantages or risks for marketplaces. This includes the geographic range of network effects, supply differentiation, SaaS integration and market fragmentation. Fragmentation is most useful as a first level filter to assess the viability of any marketplace, while the rest are second and third-level screening frameworks to evaluate defensibility and scalability. Another factor that influences the potential of marketplaces is the nature of engagement, i.e. the size and frequency of transactions. While this is less influential than other structural characteristics, it can become a major risk factor for some types of marketplaces.

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The Difference Between Virality And Network Effects

2020-04-20T09:27:00.004+01:00

Over the last couple of months, Covid-19 and subsequent lockdowns have led to a dramatic shift towards remote work tools. Zoom has been one of the biggest beneficiaries of this shift, with daily active users growing from 10M in Q4 2019 to a staggering 200M in Q1 2020. This has also led to a surge of investor and startup activity in the remote work space. As a result, I have received numerous questions about Zoom, from both investors and entrepreneurs. These questions largely boil down to “How strong are Zoom’s network effects?”. While this is an interesting topic to explore, it is the wrong question. The right questions are: “Does Zoom have network effects? And if not, what makes it defensible?”.

Fragmentation & Network Viability

2020-04-08T17:19:00.001+01:00

In my last few posts, I have put across a screening framework for network effect-based startups based on defensibility and scalability, and also explained how SaaS integration can change those assessments. Before evaluating any of that, we have to first determine the viability of developing network effects in the first place. In order to do that, we not only have to evaluate the strength of the value proposition, but also the fragmentation of markets that the network or marketplace aims to connect.

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When Networks Meet SaaS

2020-03-23T09:25:00.003+00:00

Software-as-a-Service (SaaS) startups are extremely popular with investors for a host of reasons. SaaS startups are extremely scalable because software has zero (or near-zero) marginal costs once developed, i.e. it costs virtually nothing to create another copy. Combining this scalability with subscription-based pricing results in a revenue model with high gross margins and predictable revenue (ignoring AI and data processing heavy models for now). In addition, many B2B SaaS startups are also fairly defensible because they benefit from high switching costs, i.e. those that are a “system of record” become “embedded” in the day-to-day operations of their customers’ businesses which makes it difficult for competitors to displace them.

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The Network Matrix: Bridges & Identity

2020-03-09T12:28:00.003+00:00

As I explained in my last post, the “marketplace matrix” is a great framework to get a quick understanding of the strengths and weaknesses of marketplace startups. Combining both defensibility and scalability of unit economics gives us a more holistic, but not yet comprehensive, view of these businesses. Interestingly, the core tenets of this framework are not restricted to marketplaces and can be extended to other types of network-based businesses as well (1-sided, 2-sided or multi-sided networks). But moving from the subset of marketplaces to the superset of networks requires the introduction of new concepts. I’ll explain these by looking at some well-known names in the social media space, and then extend these ideas to other network-based business in far-flung spaces, from payments to online gaming.

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Defensibility x Scalability = The Marketplace Matrix

2020-02-24T09:29:00.001+00:00

In the first part of our marketplace thesis, I explained how we think about the geographic scalability of marketplaces, i.e. we prefer marketplaces where a given unit of supply is accessible to customers across borders as opposed to those where it is restricted to a small local area. We saw that marketplaces with cross-border network effects (e.g. Airbnb), created a lot more value for investors than those with hyperlocal network effects (e.g. Uber). In sports terminology, this is a view of the “offensive” side of the game. But we also need to study the “defensive” side to gauge how well marketplaces can defend themselves from competition and copycats at scale.

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Marketplaces & Scalability: Lessons from Uber & Airbnb

2020-02-11T09:05:00.001+00:00

Uber and Airbnb are two of the most iconic companies from the last decade. Both companies created entirely new markets via a marketplace model and were originally considered to be part of the same “sharing economy”. Since then, their paths have diverged. While they were both wildly successful, Uber (and Lyft) required far more funding to create value than Airbnb did.

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Netflix's Napster Moment

2019-12-16T11:30:00.000+00:00

Netflix has been the darling of technology investors for the past few years. Since early 2017, its overall subscriber base has increased by roughly 60%, as it more than doubled its subscriber base outside the US. And, in response, Netflix's stock price nearly tripled over this period. By now, the engine for its growth is well known, i.e. original scripted content exclusive to Netflix. Of course, as we can see in the chart above, this has also dramatically increased its cash burn. Netflix's stated strategy was to invest into original content to acquire users and then subsequently ease off these investments to reach cash flow break-even. But a lot has changed in the global video streaming landscape since this strategy was conceived.

US & Western Markets

Netflix's first pivot into online streaming was built on a model of aggregating licensed content from other production companies. Essentially, Netflix was a one-stop shop for consumers, promising entertainment free of cable and enabling "cord cutting". As Netflix pivoted to its original content strategy, it became more valuable to consumers in the short-term. After all, they had more content to watch. But competing with your partners always has longer-term consequences. And in Netflix's case, production companies decided they were better off pulling flagship content from Netflix and building their own streaming services. Now, content from Disney-owned 21st Century Fox will be moving to Hulu, other Disney content will be exclusive to Disney+, The Office will be exclusive to NBC Universal's Peacock, Friends will move to WarnerMedia's streaming service HBO content will be available on HBO Max, and so on. Of course, not all these streaming services will survive, but Disney+'s early success shows that content is king and not the streaming provider.

All of a sudden, Netflix finds itself in a world where must-watch content is fragmented across streaming services with individual subscriptions. Not that different from the pre-cord cutting world. The content economics are largely similar and all that has changed is that it is now delivered over the internet. In this world, does Netflix have the freedom to cut down on original content investments? The subscribers they have acquired have their own niche tastes. If they no longer get new content they are interested in, they have enough options available from competitors.

Even if Netflix continues to invest in content, is that enough to keep subscribers around in this world? Users are unlikely to sign up AND stick to every streaming service that has a "must watch" show. A fan of Stranger Things, His Dark Materials and The Mandalorian is unlikely to pay for Netflix, HBO Max and Disney+ every month. A more likely outcome is that users "hop" between streaming subscriptions based on what they want to watch. There is already evidence of this, as HBO NOW subscriptions in the past few years experienced a dramatic peak during every new season of Game of Thrones. Subscriptions then dropped back down as soon as the season was complete. This pattern will dramatically increase user churn and, consequently, customer acquisition costs. Of course, the other eventuality is an increase in piracy, which will also hurt economics.

Emerging Markets

If the situation in Netflix's core markets is dire, couldn't Netflix replicate its playbook over the last few years in supposedly "untapped" emerging markets like India and Southeast Asia? Netflix has had a hard time growing in these markets precisely because they are not untapped. Local streaming providers like Hotstar in India and iflix in Southeast Asia already have access to a significant base of localized content to attract local subscribers. In addition, they are far cheaper than Netflix (a significant advantage in price conscious markets). For example, Netflix's current, device-agnostic base subscription in India is 2.5X pricier than market leader Hotstar. Netflix has been testing mobile-only plans and longer-term plans at lower prices to entice price conscious consumers. In addition, Netflix is increasing original content investments targeted at these markets. Of course, these price cuts merely attempt to bring Netflix to price parity with competitors. In addition, creating original content for markets like India is always challenging because of language fragmentation. And finally, increasing original content investments while also cutting prices in competitive markets isn't exactly a sustainable strategy.

The Napster Moment

All in all, Netflix is in a bind. In its core markets, content is increasingly being fragmented across streaming services which will limit options to reduce content investments. In addition, it is likely to result in higher subscriber churn and an increase an piracy. In emerging markets, it is facing an onslaught from cheaper competitors with large local content libraries.

The upside is that Netflix isn't the only company facing these challenges. The entire video streaming industry is on an unsustainable path. High content costs, subscriber churn and piracy will affect everyone in the industry. This, in turn, is likely to create the conditions necessary for a new industry structure. Take yourself back to the music industry in the early 2000s. Revenue losses caused by piracy, and Napster in particular, forced industry players to co-operate and created the conditions necessary for Apple to unbundle music albums via iTunes. Video streaming is on a very similar path (a combination of unbundling and re-aggregation onto a single platform). Of course, a change in structure this dramatic will necessitate a significant reduction in content investments across the industry. So we should enjoy the golden age of TV we are currently in... while it lasts.