The Cold Start Problem

A network effect describes what happens when products get more valuable as more people use them.

The “network” is defined by people who use the product to interact with each other.

For YouTube, the network is defined by software. It is the content uploaded by creators and the viewers that watch them—and the software platform sits in the middle, making recommendations, organizing the video with tags, recommendations, and feeds—so that the right videos are shown to the right consumers.

These networks are counterintuitive in that they connect people, but don’t own the underlying assets.

The “effect” part of the network effect describes how value increases as more people start using the product. Sometimes the increasing value manifests as higher engagement, or faster growth.

The network effect version of this in the technology industry happens when there is “overcrowding” from too many users. For communication apps, you might start to get too many messages. For social products, there might be too much content in feeds, or for marketplaces, too many listings so that finding the right thing becomes a chore. If you don’t apply spam detection, algorithmic feeds, and other ideas, quickly the network becomes unusable. But add the right features to aid discovery, combat spam, and increase relevance within the UI, and you can increase the carrying capacity for users.

While the terms are different, the core concepts and the math are the same: The Allee effect → The Network Effect Allee Threshold → Tipping Point Carrying capacity → Saturation

There are five primary stages: The Cold Start Problem Tipping Point Escape Velocity Hitting the Ceiling The Moat Figure 8: The stages of the Cold Start framework Let

The Acquisition Effect is powered by viral growth, and a positive early user experience that compels one set of users to invite others into the network.

The Engagement Effect manifests itself by increased engagement as the network grows—this can be developed further by conceptually moving users up the “engagement ladder.” This is done by introducing people to new use cases via incentives, marketing/communications, and new product features.

“Slack” was picked to mean “Searchable Log of All Conversation and Knowledge.”

“The Atomic Network”—the smallest network where there are enough people that everyone will stick around.

These networks often have “sides,” whether they are buyers and sellers, or content creators and consumers. Generally one side of the network will be easier to attract—this is the easy side of the network. 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.

How many users does your network need before the product experience becomes good? The way to answer this is for companies to do an analysis on the size of their networks (on the X-axis) plotted against a set of important engagement metrics (on the Y-axis). For Uber, this chart showed that more drivers generally meant a lower waiting time, and thus more users—at least up to some point. Eventually, the difference between two minutes to get a car or one minute to get a car becomes diminishing returns. Facebook’s famous growth maxim, “10 friends in 7 days,” is an expression of the same idea.

Solving the Cold Start Problem requires a team to launch a network and quickly create enough density and breadth such that the user experience can improve in leaps and bounds.

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 can create one stable, engaged network that can self-sustain—an atomic network—then likely you can build a second network adjacent to the first one. And if you can build 1, and then 2, you can probably build 10 or 100 networks. Copy and paste many times, and you can build a huge interconnected network that spans the entire market.

The networked product should be launched in its simplest possible form—not fully featured—so that it has a dead simple value proposition. The target should be on building a tiny, atomic network—the smallest that could possibly make sense—and focus on building density, ignoring the objection of “market size.” And finally, the attitude in executing the launch should be “do whatever it takes”—even if it’s unscalable or unprofitable—to get momentum, without worrying about how to scale.

The next big thing will start out looking like it’s for a niche network.

Engagement goes up, because users are more likely to find other relevant users. Viral growth goes up when prospective users of a product see that their friends and colleagues are all using the service.

There is a minority of users that create disproportionate value and as a result, have disproportionate power. This is the “hard side” of your network. They do more work and contribute more to your network, but are that much harder to acquire and retain.

While it might seem strange to some of us to spend hours per day writing on Wikipedia, when you look across user-generated products, this is actually the norm, not the exception. There are nearly 100 million riders on Uber, but just a few million drivers. There are two billion active users on YouTube, but just a few million upload videos.

Consumers are generally the easy side of a network, and are typically cheaper and easier to attract and retain.

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 when a new network emerges, they will retain on your product? These are difficult answers, and require a deep understanding of the motivations of your users.

In a widely read essay called “Creators, Synthesizers, and Consumers,” Bradley Horowitz, now a vice president of product at Google, described the 1 percent of users who create versus everyone else: 1% of the user population might start a group (or a thread within a group) 10% of the user population might participate actively, and actually author content whether starting a thread or responding to a thread-in-progress 100% of the user population benefits from the activities of the above groups (lurkers)19 This is often called the “1/10/100” rule, and it’s no surprise that the 1 percent of highly engaged users is extremely valuable.

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.

Within these platforms, just ask yourself, “If a piece of content was created, and no one saw it, would the creator be disappointed?” If the answer is yes, then social feedback is a key value.

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 answer is to look at hobbies and side hustles. There are millions of content creators, app developers, marketplace sellers, and part-time drivers that power the hard side of networks. They are smart, motivated, early adopters who are finding opportunities to make themselves useful.

Thinking about zeroes and unfulfilled requests was such a useful concept at Uber that we baked it into many of our more common dashboards, split by city and region so we could understand how often it was happening. I encourage product teams to develop their own form of this metric, laid out as a dashboard of networks—whether that’s divided by geography, product category, or whatever else makes sense. Within each, it can be useful to track the percentage of consumers that are seeing zeroes. If it’s too high a number, that category of users is experiencing anti-network effects, and it will never break through.

The Cold Start Problem doesn’t stop once a networked product has established its first atomic network—it needs to be continually solved by the network as it grows. Even once an atomic network is thriving, the networks intertwined with it—whether you think of that as industry sector, geography, demographic, or otherwise—still need to solve the Cold Start Problem as well.

To begin the discussion of the Tipping Point, I’ll start with a prominent strategy, “Invite-Only,” that is often used to suck in a large network through viral growth. Another method to tip over a market is with a “Come for the Tool, Stay for the Network” strategy.

If the hard side of the network isn’t yet activated, a team can just fill in their gaps themselves, using the technique of “Flintstoning”—as Reddit did, submitting links and content until eventually adding automation and community features for scale.

Some have espoused the invite-only tactic as a method to generate hype, since potentially a buzzy new product might cause people to head to social media to ask their friends for invites. Others say the value of invites comes from a method of limiting audience growth so that teams can fix bugs and scale a product’s infrastructure, before going to market more fully.

Invite mechanics work like a copy-and-paste feature—if you start with a curated network, and give them invites, that network will copy itself over and over automatically.

Famously, Facebook initially required a harvard.edu email address to sign up, both defining an atomic network where everyone trusted each other, and also providing an explicit way to think about school-by-school launches. Years later, Slack would employ a similar tactic, using corporate email domains as the way to define who should join what network.

Invite-only mechanics provide a better “welcome experience” for new users as well. To explain why, imagine arriving at a large dinner party. A good friend welcomes you at the door, and as you step in, you see acquaintances, close friends, and a number of new people who’ve been curated to be absolutely fascinating. If that’s the ideal experience for a dinner guest, it’s also an apt metaphor for the best possible entry into a new product experience. Invite-only products can facilitate this, because every new user that signs up is already connected to at least one person—their inviter. For products like Slack or Zoom where you only need a few people to make it useful, having the guarantee of at least one connection is a giant step toward solving the Cold Start Problem.

People with an invite to an exclusive product will post praise, critiques, and other commentary. People without an invite will ask for it, prompting discussion and sometimes controversy, driven by scarcity and exclusivity dynamics. This in turn attracts more attention and engagement. It works!

The original idea wasn’t to create hype, but rather something more practical—the infrastructure running Gmail couldn’t support a fast ramp on the number of users, so an invite-only strategy was utilized:

You might ask, if invite-only is so great, why isn’t it used more often? There are good reasons. It’s often seen as risky, because it can kill the top-line growth rate of your product. It requires you to build a lot of extra functionality, so that people who newly sign up are connected properly with people around them. A lot of people might show up without an invite and get turned away. From the lens of a company doing a Big Bang Launch, why limit your numbers? If there aren’t enough new users showing up and interacting with others, the network might be too small—you might hit the Cold Start Problem.

For networked products, the curation of the network—who’s on it, why they’re there, and how they interact with each other—is as important as its product design. Starting with a deliberate point of view on who’s best for your network will define its magnetism, culture, and ultimate trajectory.

Come for the tool, stay for the network” is one of the most famous strategies for launching and scaling networks. Start with a great “tool”—a product experience that is useful even for one user as a utility. Then, over time, pivot the users into a series of use cases that tap into a “network”—the part where you collaborate, share, communicate, or otherwise interact with other users.

Many months into Burbn, Kevin and Mike realized the product was getting too complex, and going to run straight into Foursquare—a location-sharing app that was rocketing into success at the time. It was time to refocus. The team looked at the best features of the product, oriented around photos, and stripped everything else out. Kevin Systrom would recount the reinvention of the app:

Chris writes: A popular strategy for bootstrapping networks is what I like to call “come for the tool, stay for the network.” The idea is to initially attract users with a single-player tool and then, over time, get them to participate in a network. The tool helps get to initial critical mass. The network creates the long term value for users, and defensibility for the company.40

For a large class of products centered on content creation, organization, and reference, it can be a winning strategy. When it works, the tool can help take on an entire network, and once atomic networks start to form, the entire market will come over.

The tool is combined with a network that allows people to interact with the content and by extension, other people. Group a few other products that have a tool/network pairing, and some clusters start to emerge: Tool, network Create + share with others (Instagram, YouTube, G Suite, LinkedIn) Organize + collaborate with others (Pinterest, Asana, Dropbox) System of record + keep up to date with others (OpenTable, GitHub) Look up + contribute with others (Zillow, Glassdoor, Yelp)

Pivoting users from tool to network can be hard. Sometimes only a small percentage will make the transition, since it requires them to change their behavior

At one end of the spectrum, the tool and network are divergent—you are just bundling one popular tool with a completely separate and unrelated networked product. This is hard because the conversion rate from tool to network might be low—

On the other end of the spectrum are tools and networks that are highly integrated, like Dropbox’s folder-sharing functionality, which defines its network. This type of integration is so elegant that it would feel like an obvious missing functionality if it didn’t exist—users would likely drive the product toward a network, not away from it. This type of conversion from tool to network tends to be high.

Using money as a growth lever can feel like a dangerous move, and it should only be executed at the right time. While establishing an initial network, it usually doesn’t make sense for an underresourced startup to throw a lot of money around to get started. Instead, teams are often better to focus on basics, like figuring out the right target market, and creating the initial product features. You need to nail the killer product, and prove that you can gain an atomic network, before reaching for the financial lever.

These levers are implemented in different ways—sometimes these look like referral programs, or up-front advances or guarantees, or differentiated tiers of pricing. What they all share is allowing the team behind the network to juice its growth by using dollars rather than building features.

These levers are particularly powerful for networked products that are close to the money—payment networks like Venmo, cryptocurrencies, marketplaces and social platforms like Twitch that allow creators to make money.

I’ve also seen a new pattern of startups offering network participants everything from stock options, to consulting fees, to investing rights, just to bootstrap the initial network. It’s particularly effective for influencers, creators, developers, and the hard side of the network. That way, the company is aligned with its network—if the network grows and succeeds, its individual participants also win.

Early product releases often go into beta while lacking simple features like account deletion, content moderation tools, referral features, and many others. In lieu of these features, the product might simply offer a way to contact the developers who will handle it manually for you, using tools they have in the back end. Once they get enough inquiries, eventually the feature gets built out and users can do it themselves.

For example, on user-generated video platforms, the initial library of videos and content might be uploaded by its founders, as YouTube did early on. For workplace collaboration tools, the team might offer onboarding and practically embed themselves within a client, offering custom software development and more, to make a particular project successful.

Study the pattern of Flintstoning across industries and you’ll see the focus tends to be on replicating the hard side of the network with employees, contractors, and other direct efforts.

Flinstoning can be thought of as a spectrum: Fully manual, human-powered efforts Hybrid, where software suggests actions to take, but people are in the loop Automated, powered by algorithms

Hustle and creativity help tip over markets, because each atomic network is not the same. The first, second, and third will likely require slightly different tactics.

They innovated with a referral program where users could give and get storage by inviting friends. User growth was explosive.

Products are inherently viral when people bring their friends and colleagues into a network simply by using it—as Dropbox, messaging apps, and social networks do.

Often look for a minimum baseline of 60 percent retention after day 1, 30 percent after day 7, and 15 percent at day 30, where the curve eventually levels out. It’s usually only the networked products that can exceed these numbers.

At LinkedIn, we segmented our users as: Active the last 7 days out of the last week Active the last 6 days Active the last 5 days … and so on. This let us dig into each segment separately and understand their needs, motivations, and what it would take to move them up in engagement.52

The levers you use to increase the engagement of an infrequent user are different than deepening engagement for a power user. Early users might just need a few more connections to colleagues at their company. Power users might need to discover advanced features on search, recruiting, and creating groups, so that they have new and more powerful ways to connect with people. Segmenting our users gives us the granularity to connect the right features and user education to impact their usage.

This process can be modeled as an “engagement loop” that describes how users derive value from others in a network in a step-by-step process.

Users need to trust the loop to rely on it. If the network is too small or too inactive and the loop breaks, then users will be less likely to use it in the future.

Notifications to entice users back. This usually doesn’t work, and company-sent communications rank among the lowest clickthrough rate messages.

Networked products, on the other hand, have the unique capability to reactivate these users by enlisting active users to bring them back. Even if you don’t open the app on a given day, other users in the network may interact with you—commenting or liking your past content, or sending you a message. Getting an email notification that says your boss just shared a folder with you is a lot more compelling than a marketing message.

These churned users are sometimes called “dark nodes.” When they are surrounded by deeply engaged colleagues and friends, even if they’ve been inactive for months, they are often flipped back into an active user. These frequent network-driven interactions can drive further investment by the user over time, eventually tipping an inactive user into a very active one.

While reactivation is typically not a concern for new products—they should focus on new users, since their count of lapsed users won’t be large—for products that have hit Escape Velocity, there will be a pool of many millions of users to draw upon. Reengaging them can become as big a growth lever as acquiring new users.

This is the Product/Network Duo at work again, where the product has features to attract people to the network, while the network brings more value to the product.

Increase the Acquisition Effect, you have to be able to directly measure it. The good news is that viral growth can be rolled up into one number. Here’s how you calculate it: Let’s say you’ve built a new productivity tool for sharing notes, and after it launches, 1,000 users download the new app. A percentage of these users invite their colleagues and friends, and over the next month, 500 users download and sign up—what happens next? Well, those 500 users then invite their friends, and get 250 to sign up, who create another 125 sign-ups, and so on. Pay attention to the ratios between each set of users—1000 to 500 to 250. This ratio is often called the viral factor, and in this case can be calculated at 0.5, because each cohort of users generates 0.5 of the next cohort.

Measuring and optimizing viral growth in this way may make it feel like a spreadsheet project, but I assure you it is much more copywriting, user psychology, and product design.

It’s the psychological elements, combined with the value proposition of a product, that make the best viral growth strategies difficult to copy. They are often unique to the product itself—making them proprietary and more defensible.

Understand how one group of users taps into their respective networks to bring in the next group of users. Because these groups of users generally live inside of atomic networks, the other thing that happens is that networks tend to attract other atomic networks. And so on.

Premium features can be designed in a way where they are more useful as the network gets larger, as opposed to being based on individual usage. Thus, the larger the network, the greater the incentive to convert to premium.

Products with a strong Economic Effect are able to maintain premium pricing as their networks grow, because switching costs become higher for participants who might be looking to join other networks.

It may be easy to copy features, but it’s nearly impossible to copy a network.

Problem is, smaller customers are always churning out because they’re price sensitive, running out of money, and changing their business model—sometimes all three! Larger enterprise customers, on the other hand, are sometimes harder to break into but can grow revenue over time as more and more users adopt it within a company. Thus it’s natural for B2B startups to begin with a bottom-up sales motion but eventually add expertise to sell into enterprises.