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This is where the classical definition of a “network effect” is wrong. I redefine it so that it’s not one singular effect, but rather, three distinct, underlying forces: the Acquisition Effect, which lets products tap into the network to drive low-cost, highly efficient user acquisition via viral growth; the Engagement Effect, which increases interaction between users as networks fill in; and finally, the Economic Effect, which improves monetization levels and conversion rates as the network grows.
a rapidly growing network wants to both grow as well as tear itself apart, and there are enormous forces in both directions.
In the real world, products tend to grow rapidly, then hit a ceiling, then as the team addresses the problems, another growth spurt emerges. Then follows another ceiling.
The final stage of the framework focuses on using network effects to fend off competitors, which is often the focus as the network and product matures. While it is not the only moat—brand, technology, partnerships, and others can help—it is one of the most important ones in the technology sector.
This is the Cold Start Theory. It is made up of five stages for creating, scaling, and defending the network effect, and aims to provide a road map for any new product team—at a startup or larger company—to leverage in their work.
Cold Start Theory is meant to apply to a large set of companies in the technology industry: video platforms, marketplaces, workplace collaboration tools, bottom-up SaaS products, social networks and communications apps, and more. Throughout the book, I also draw on historical examples—coupons, credit cards, and early internet protocols. There are surprising shared dynamics between archaic forms of communication that we used hundreds of years ago and the modern apps that we use today.
When you start a new product with network effects, the first step is to build a single, tiny network that’s self-sustaining on its own. You just need one to get started. However, building even a single network is very hard.
Years later, Tiny Speck would relaunch with a second product—that product was called Slack.
Each of these beta customers formed an atomic network—a stable, self-sustaining group of users who can drive a network effect. Once an atomic network was formed in one of their beta testers, Slack would continually add users, become more useful, ramp up engagement, and ultimately become the de facto method of communication within their workplaces. The minimum number of people to be defined as a team, even today at Slack, is three. As long as you have three people, it can be stable. But it’s even better if there’s a larger team of fifty people in an organic unit—like a department—or an entire
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Like many of the examples I’ll visit later on, Slack is a network of networks.
Each workspace might be a business unit or subsidiary, and each smaller team might independently have a ringleader or early adopter who set up the product, invited coworkers in, and started initiating conversations with others.
The book is named after the first stage because, quite frankly, it’s the most important one.
Small, sub-scale networks naturally want to self-destruct, because when people show up to a product and none of their friends or coworkers are using it, they will naturally leave. What solves this? “The Atomic Network”—the smallest network where there are enough people that everyone will stick around.
However, the most important part of any early network is attracting and retaining “The Hard Side” of a network—the small percentage of people that typically end up doing most of the work within the community.
A small group of drivers, about 5 percent of Uber’s users, carry most of the load within the rideshare marketplace—riders are numerous but engage less frequently and deeply.
for every successful launch like Slack, there are many more that are failures—and they usually stumble right at the start. Anti-network effects are the negative force that drives new networks to zero. While the industry tends to focus on the positive results of network effects, at their inception, network effects are a destructive force, driven by a vicious—not virtuous—cycle where new users churn because not enough other users are there yet.
People won’t use their product unless their friends are on it. Features are feverishly added, marketing efforts are redoubled, but the network never gets off the ground, and the team ultimately is out of runway. They didn’t solve the Cold Start Problem, and the result is failure.
But it hit us that, regardless of any other factor, after 2,000 messages, 93% of those customers are still using Slack today.”
Facebook’s famous growth maxim, “10 friends in 7 days,” is an expression of the same idea.
for Airbnb, early employee Jonathan Golden said: Cofounder Nate Blecharczyk is highly quantitative and had determined that 300 listings, with 100 reviewed listings, was the magic number to see growth take off in a market.
as much as critics have occasionally argued that the company lacks defensibility and network effects, today it maintains a big advantage on upstarts who can no longer solve the Cold Start Problem in the same way.
Ten people using Slack all from the same team is better than ten random people in a larger company. Density and interconnectedness is key.
It’s not magic—it’s not a black/white switch that takes you from a sub-scale network to a working one. Instead, it feels like a gradual series of improvements in the core metrics as the network fills in.
The solution to the Cold Start Problem starts by understanding how to add a small group of the right people, at the same time, using the product in the right way. Getting this initial network off the ground is the key, and the key is the “atomic network”—the smallest, stable network from which all other networks can be built.
If you study the launch of products with network effects, you’ll see that one of the most common threads is that they often start small, in a single city, college campus, or in small beta tests at individual companies—like Slack’s story. Only once they nail it in a smaller network do they build up over time to eventually conquer the world.
Credit cards have network effects for the same reasons that marketplaces do: they aggregate consumers, merchants, and other financial institutions as a multi-sided network. Everyone in the network benefits, particularly the consumer, who can go shopping without carrying physical cash. Merchants and banks are happy, too. And the bigger the network gets—meaning more consumers, more places where credit cards are accepted, etc.—the more useful the network. This in turn drives new merchants and consumers to adopt it.
The concept of an atomic network is immediately obvious here. Although Bank of America served all of California, they didn’t focus on trying to launch across the entire state at once, but rather, they focused on Fresno, a town where they had a high degree of penetration.
The threshold was just the team of people around you—under ten in a single company might be enough—and there would be enough chat activity to sustain user engagement. Contrast that to the credit card, which needed to launch in an entire city to make it work.
Embedded within Slack’s strategy, and the strategy of many early-stage networked products, is a series of short-term boosts—often called “growth hacks”—which are important in forming the initial atomic networks.
The next big thing will start out looking like it’s for a niche network.
Underestimating new products in this way is the number one way to make dumb predictions in the tech industry.
The first step to launching an atomic network is to have a hypothesis about what it might look like. My advice: Your product’s first atomic network is probably smaller and more specific than you think. Not a massive segment of users, or a particular customer segment, or a city, but instead something tiny, maybe on the order of hundreds of people, at a specific moment in time.
Years later, a company might talk about entire countries or mega-regions, like EMEA or APAC, but in the early days, it’s about something much more focused. It should be about building the smallest possible group.
Whereas our typical business verbiage revolves around aggregations of millions of people—that’s usually what we mean when we talk about “markets,” “segments,” and “demographics”—the language of launching new networks should be focused on groupings of a handful of people, with the right intent, in the right situation, at the right time. This is true in dating apps, marketplaces, but even in workplace products.
If you need hundreds of users on the same platform at once, company-wide coordination is needed. In this situation, a top-down enterprise sale that gets a company to mandate usage for everyone might work better.
Viral growth goes up when prospective users of a product see that their friends and colleagues are all using the service.
Even at the start of an atomic network, there is an important and surprising dynamic at play that only increases over time: there is a minority of users that create disproportionate value and as a result, have disproportionate power.
Sometimes the hard side is obvious, but I encourage you to think deeply about which side is which, because it can be nuanced.
You might look at a product and think its network doesn’t have sides. Sometimes this is referred to in the industry as one-sided networks, like messaging apps and social networks. But even in these cases, there are active, extroverted users who initiate conversations and organize get-togethers, and there are those who don’t. Nearly every network has them, and the hard side must all be happy for the network to function. When they work, they generate what academics often call “cross-side network effects”—when more users in one side of the network benefits the other side of the network.
Even think about all the people who write documents and make presentations versus those who just view or make small edits. This relationship exists everywhere.
Because the hard side is so critical, it is imperative to have hypotheses about how a product will cater to these users from day one. A successful new product should be able to answer detailed questions: Who is the hard side of your network, and how will they use the product? What is the unique value proposition to the hard side? (And in turn, the easy side of the network.) How do they first hear about the app, and in what context? For users on the hard side, as the network grows, why will they come back more frequently and become more engaged? What makes them sticky to your network such that
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The more difficult the work needed to be part of the hard side of a network, the smaller the percentage of users who will participate.
Yet across many other platforms, particularly “broadcast” apps where you share videos or photos widely, the value proposition is displaying your status. No wonder our Instagram feeds are photos of travel adventures, cars, concerts, working out, and so on. Users become addicted to the “social feedback loop”—you publish content, and others see it and engage in the form of likes, shares, and comments. When this feedback is positive, it drives the creator to generate even more content.
It’s important to focus on this tiny slice of users so that messaging, product functionality, and business model are all aligned to serve them. Without this group, the atomic network will collapse—a social network can’t exist without its content creators, and a marketplace can’t exist without its sellers.
The classifieds-based design created a poor product experience since the popular members—particularly women—would become overwhelmed with a large number of messages, and they would struggle to reply. At a bar or club, potential suitors might be dissuaded if they saw a line of people waiting to talk to an attractive man or woman, but online, there was no such signal. So in turn, the experience for everyone else also ended up poor, because it seemed like no one would write them back.
And of course, the mechanic of swiping itself is a way to make sure people don’t feel overwhelmed. Whereas men tend to swipe right (that is, to indicate interest) on about half of women’s profiles—about 45 percent to be exact—the ladies in the product swipe on only 5 percent of profiles they see. As a result, women mostly match with the guys they select.
Thus the order of operations, at least for most consumer-facing marketplaces, is “supply, demand, supply, supply, supply.” While supply might be easy to get onto the network early on through subsidies, eventually it will become the bottleneck. The hard side of a network is, by definition, hard to scale.
Rideshare networks, for example, fundamentally depend on the underutilization of cars, which generally sit idle most of the time besides the daily commute and the occasional errand. Airbnb is built on the underutilization of guest bedrooms and second homes, combined with the time and effort of the hosts. Craigslist and eBay are built on letting people sell their “junk”—the stuff that they don’t value anymore—to new owners who might value it more.
For networks that are derived from underutilized assets, it might be the niche of those who like to have new side hustles every weekend to make money online.
When we combine disruption theory with network effects, it makes even more sense—atomic networks often start at the low end in terms of functionality, in a niche market. But once an atomic network is established, the hard side of the network is willing to extend their offerings and services to go into the next vertical. This attracts an incrementally higher-end opposite side, which in turn spurs the hard side to extend even further—and the cycle continues!
