The Three Systems Beneath Network Effects

Hello friends!

Today I have something special for you: my favorite chapter of The Cold Start Problem, a new book, coming out tomorrow, that’s one of the more interesting and detailed explorations of network effects I’ve ever read. 

As you will see, it is equal parts love letter and rebuke towards the idea of a “network effect”—a concept that is both indispensable and yet totally oversaturated and misunderstood.

The author, Andrew Chen, is someone I suspect many of you already know. I first met Andrew back in 2011 when I had recently graduated college and moved to Palo Alto to join a startup. His essays shaped my foundational understanding of how startups can get off the ground and grow. So I reached out on a whim and a few weeks later was delighted to find myself wandering around with Andrew on University Ave, asking his advice on everything startups and career-related.

Then, as now, Andrew was absolutely dripping with insights about the forces that create viral success. He’s basically the opposite of the common thing you encounter in Silicon Valley, where people throw around terminology they clearly lack any functional knowledge of. And there’s probably no term more commonly abused than network effects. Yet it remains critical for anyone wanting to understand how market power works.

I was lucky enough to bump into Andrew a few months ago and pestered him to let me share a chapter of his book with you, so here we are!

Before we begin, let’s quickly set the scene to give you some context for this chapter.

Andrew’s overall goal for The Cold Start Problem is to propose a new theory of network effects that is more detailed and useful for practitioners than the abstract, academic versions of the idea that have prevailed to date. One classic example is Metcalfe’s law, which claims the value of a network is proportional to the square of its number of participants. Ask anyone in the field and they’ll tell you it’s neither true nor useful.

Instead, Andrew breaks network effects into five stages of development:

1. Cold start
2. Tipping point
3. Escape velocity
4. Ceiling
5. Moat

Each stage of network development has several chapters in the book that consist of case studies with the founders, engineers, marketers, and product managers that have actually worked with these forces.

The chapter below is from the part of the book covering the third stage of network development: escape velocity. In it, Andrew argues that network effects aren’t one single thing. There are actually three separate forces that benefit businesses in three distinct ways.

I took a lot away from it, and I’m sure you will too.

Here’s Andrew! Enjoy.

Chapter 18: The Trio of Forces

Escape velocity is often described as a kind of end state, the moment when a product becomes dominant in the market, where everything gets easier. These companies are supposed to have uncontested growth based on their strong network effects. Yet look inside any product team that’s reached Escape Velocity and you see something different—what looks so easy on the outside is not so easy on the inside. Thousands of employees are working furiously to scale up the network. Dropbox, for example, employed 2,000+ full-time highly paid designers, engineers, and marketers, doubling or tripling the employee base each year leading up to the 2018 IPO.

While you may only need a small handful of employees to achieve product/market fit—famously, Instagram had thirteen employees and 30 million users when it was bought by Facebook—you need a significant coordinated effort to scale a product to its full potential. This is a big contrast to our everyday, overly simplistic explanations of hockey stick growth curves: “They’ve got lightning in a bottle!” Or, for many of the tech products I’m unpacking in this book—from multiplayer games to chat apps to workplace products—sometimes the offhand explanation is: “Of course, they’re growing fast—they have network effects!” But this is superficial.

It takes a tremendous amount of energy to scale a network—both in playing defense to counteract market saturation and competition, and on the offense, to amplify network effects over time. It’s not just Dropbox with this kind of story [Note from Nathan: the previous chapter in the book was about Dropbox]—Pinterest, Slack, Zoom, Uber, Airbnb, and others also have thousands (or tens of thousands) of full-time employees, many of them working within the confines of a single app or small family of apps. Ask any of these teams, and they’ll tell you they feel understaffed, and there’s so much more to do. This is what Escape Velocity actually looks like. It heralds a new stage, focused on building up network effects to amplify their strength. It is not a period where teams can coast on their momentum, because inevitably, momentum will slow as market saturation, spam, competition, and other forces appear.

Strengthening network effects is easier said than done. Everyone wants to improve their network effects, but what does that really mean? Product teams work in the concrete—in designing and picking product features, in deciding timelines for launching new products, and in trading off engineering complexity for functionality. Tell a team something abstract like “go improve your network effects!” and you’ll get blank stares. In the coming chapters, I will discuss how to move from strategy to execution. To create a plan to strengthen a product’s network effects, we need to connect the abstract with the concrete, so that the output can reflect the practical reality of picking and prioritizing projects.

Three Systems Underlying the Network Effect

Let’s start with a surprising idea that goes against the grain of industry jargon: the network effect is not one effect. Instead, the network effect is a broader umbrella term that can be broken down into a trio of underlying forces: the Acquisition network effect, the Engagement network effect, and the Economic network effect. Each one of these can contribute to a business in a different way, and is stronger the more dense a network is.

The “Acquisition Effect” is the ability for a product to tap into its network to acquire new customers. Any product can buy Facebook or Google advertising, for instance, to attract new users, but only networked products can tap into viral growth—the ability for users in its network to tell others in their own personal networks. This keeps customer acquisition costs low over time, fighting against the natural rise that comes with market saturation and competition. The types of projects that amplify the Acquisition Effect are oriented around viral growth: referral features that reward users when they invite others, tapping into contacts to create suggestions for who to add to an app, and improving conversion along the key moments in the invitation experience. All of these help increase metrics like new user sign-ups, the so-called viral factor of a product, and bring down the cost of acquiring a customer (CAC).

The “Engagement Effect” describes how a denser network creates higher stickiness and usage from its users—it is a more specific form of the classic description of network effects that I covered at the beginning of the book, “the more users that join the network, the more useful it gets.” However, the classic definition can be refined to include the underlying system that drives the value—use cases and “loops” that define how users derive value when engaging with a product—as well as the specific metrics that increase with a denser network. For example, Twitter is a lot more interesting to use, now with media outlets, celebrities, and politicians on it, than in the early days when you might just have a nerdy friend or two on the platform. Because there’s more types of content creators in the network, what might have felt like an app to stay in touch with friends in the early days might eventually evolve into a diverse set of use cases: tracking political news, keeping abreast of what’s happening in your industry, keeping up with your favorite celebrities, and so on. In turn, these elevated use cases drive key metrics, as more engagement directly maps to the number of sessions per user, or the number of days per month that you’re active in the product. Retention curves, often one of the most important visualizations of how long people are sticking around, can be improved as stickier use cases emerge.

The “Economic Effect” is the ability for a networked product to accelerate its monetization, reduce its costs, and otherwise improve its business model, as its network grows. Workplace products, for example, often convert to higher tiers of pricing as the number of knowledge workers using them grows within a company. The more workers that adopt a product, the more advanced features they might want to upgrade into, particularly when the features are collaborative in nature—like Slack charging for the ability to search messages from all users across the organizations. Similarly, app stores and other marketplaces will grow their average revenue per user as the number of listings increases. If customers have more choices, they often have a better chance of finding exactly what they’re looking to buy. Then, conversion rates increase.

The Growth Accounting Equation

I use Engagement, Acquisition, and Economic network effects as the core taxonomy for the reason that they map to the key outputs that product teams care most about: active users and revenue, and the leading indicators to these metrics. Active users are made up of a combination of new users signing up, and how engaged and retained the existing users are. Revenue is a by-product of active users and the average revenue each user is generating, whether that’s from purchases or advertising revenue. Growth rate, another important metric, is the ability to repeatably scale these network effects consistently over time.

The relationship between these inputs and outputs is just arithmetic. Here is what’s often called the “Growth Accounting Equation,” which shows how these key metrics relate for active users:

Gain or loss in active users = New + Reactivated − Churned

Then based on the delta of each time period, you can figure out if you’ll gain or lose active users:

This month’s actives = Last month’s actives + gain or loss

This example uses “Active users”—relevant more for social networks and messaging apps—but it could be “Active subscribers,” too, for a SaaS product like Dropbox or a consumer subscription service like YouTube Red. It’s become a best practice to take this equation and build dashboards out of its inputs, so that in any given month you know how the underlying components are trending. If your goal is to grow 3x year over year, and sign-ups are way down, then it becomes clear how much churn has to be improved in order to still make the target—it’s just some simple math. Overlaying revenue is easy, too. You just add two more variables, multiplying the active users number with the average revenue per active user (ARPU).

Every product can be thought of in this way, and it’s the product team’s goal to increase each of these metrics. However, networked products are special in how they can leverage their networks to drive up each of these variables—something that traditional products can’t. As they grow and hit Escape Velocity, the density of the network makes the Engagement, Acquisition, and Economics effects more powerful, causing the input metrics to increase. More new users will appear, based on viral growth, and the product will get stickier, decreasing churn. More money will be made, as conversion rates increase. The central inputs into a networked product’s growth equation will improve on their own, as a function of the network as opposed to the features of the product—creating an accumulating advantage over time. This is the magic of network effects.

And while I describe each of these network effects independently, in practice they all work together in concert. A more engaged and retained audience will have more opportunities to share the product with their friends, driving up viral growth. A stronger Acquisition Effect means there will be a steady stream of new people to engage the existing community, keeping them more engaged. Stronger monetization might mean that users make more money, which then stimulates more engagement. Amplifying one will often drive the others up as well.

This newsletter is already going a bit long so I’ll be brief, but before we part ways for today I wanted to share my take on what you just read.

Andrew frames the “trio of forces” as what’s beneath network effects, and says that the term “network effect” is really an umbrella term to describe three independent forces, but I model it in my own head slightly differently. I think of the effects Andrew explains of decreased acquisition costs, increased user engagement, and increased monetization as actually three benefits that are possible when you have a strong network effect. The core network effect itself—that people want to be on the same systems as other people—is its own thing.

This is maybe just a tiny nitpick, but I’m curious what y’all think? Click one of the feedback buttons below and leave a comment! 

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Ok that’s it! See you again soon!