The Cold Start Problem: How to Start and Scale Network Effects

by Andrew Chen

It was deeply and uniquely ingrained in Uber’s DNA to talk about metrics at the hyperlocal network level. In my several years there, it was unusual to ever hear about an aggregate number—like total trips or total active riders—except as a big vanity milestone at a company all-hands. Those aggregate metrics were regarded as mostly meaningless. Instead, the discussion was always centered on the dynamics of each individual network, which could be nudged up or down independently of each other, with increased marketing budget, incentive spend for either drivers or riders, product improvements, or on-the-ground operational efforts.

In its classic usage, a network effect describes what happens when products get more valuable as more people use them.

Although the networks don’t own their underlying resources, it’s the connection that matters. The entire ecosystem stays on because the value is in bringing everyone together. That’s the magic. The “effect” part of the network effect describes how value increases as more people start using the product.

Given these definitions, how do you tell if a product has a network effect, and, if yes, how strong is it? The questions to ask are simple: First, does the product have a network? Does it connect people with each other, whether for commerce, collaboration, communication, or something else at the core of the experience? And second, does the ability to attract new users, or to become stickier, or to monetize, become even stronger as its network grows larger? Does the user face a Cold Start Problem where retention is low when there’s no other users?

Larger competitors are often able to copy the product, but find it difficult to capture the network.

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—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.

But to do this, teams have to do what Dropbox did and find a way to segment higher versus lower value users. Monetary value might not be the right segmentation—it might be something else, like frequency, lifetime value, use cases, or some other defining characteristic.

These insights are often found by examining a power user segment—or HVAs, in Dropbox’s parlance—and trying to understand what makes them unique. Perhaps they’re using a particular feature, or engaging with the network in a certain way. It might be tempting to force every user to then use the product this way, but unfortunately correlation does not mean causation. You don’t want to study fire departments and fires and conclude that the former cause the latter!

But in the positive case, if a network scales and the connections get denser, then the loop gets tighter—content creators get more social feedback, marketplace sellers get more purchases at higher prices, and users of workplace tools can effectively collaborate with their coworkers.

It’s incredibly useful to lay out an engagement loop, one screen at a time, and brainstorm ways to increase each step—this method is at the heart of what I typically do when advising startups on creating higher stickiness.

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. A notification that a close friend just joined an app you tried a month ago is a lot more engaging than an announcement about new features. And the more dense the network is around a churned user, the more likely they are to receive this type of interaction.

To amplify the Engagement Effect as it relates to reactivation, the key question to ask is, what is the experience of a churned user? If you’re inactive, what kinds of notifications are you getting from other users, and are they compelling enough to bring you back? Almost always, churned users don’t receive any communication at all. You can boost reactivation success rate significantly just by sending a weekly digest of the activity in a user’s network, or “Your friend X just joined” notifications. The other question to ask is, if a user wants to reactivate, how hard is it? At Uber, we had a staggering statistic where several million users were failing their password recovery per week—how do you make this much easier, and treat reactivation with the same seriousness as the sign-up process? 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.

The early work on scurvy gave us the foundational tools. But instead of providing citrus and measuring for malnutrition, tech companies can reason by analogy: create user cohorts by levels of engagement, and analyze what differentiates high value users from lower value ones. These start out as correlations, so use A/B testing to prove causality—once the best levers are found, test many variations of these ideas. Rinse and repeat, to systematically strengthen the Engagement network effect.

Viral growth builds on the power of networks to acquire users, often free of charge.

Networked products are unique because they can embed their viral growth into the product experience itself. When a product like Dropbox has a built-in feature like folder sharing, it can spread on its own. PayPal’s badges and core user-to-user payments accomplishes the same. 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. Workplace collaboration products like Slack ask you to invite your colleagues into your chat, and photo-sharing apps like Instagram make it easy to invite and connect to your Facebook friends. They can tap into your phone’s contacts, integrate with your company’s internal employee directory, or tap into the sharing widgets built into your phone. This is software, not just building a buzzy, shareable video.

To 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. In this example, things are looking good—starting with 1,000 users with a viral factor of 0.5 leads to a total of 2,000 users by the end of the amplification—meaning an amplification rate of 2x. A higher ratio is better, since it means each cohort is more efficiently bringing on the next batch of users. Once you have calculated this metric, you can use A/B testing and implement new product features to try to improve it. This might involve easier sharing to social media, or multiple SMS reminders after an invite is sent. Perhaps the landing page is optimized to ask for less information upfront to get prospective users to sign up in just a few clicks. If you increase the viral factor of your product to 0.6,...

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. The teams working on growth must be aware of what’s worked in the past—there have been viral loops built on birthday alarm clocks, sending sheep emoji, comparing personality test results, building photo collages where you tag your friends, and much more. Some of these ideas are based on user psychology, which doesn’t change, and can be tweaked and iterated upon for any new product. In fact, it’s the psychological elements, combined with the value proposition of a product, that make the best viral growth strategies difficult to copy.

There’s a reason why the term used for viral growth is to “land and expand”—to build new networks as well as increasing the density of existing networks. By “landing,” viral growth can start new atomic networks, as a Dropbox invite from an ad agency to their client brings a new company into the collaboration network. Or, when a WhatsApp group chat invite brings onboard a new set of friends who hadn’t previously used the service. But then the product “expands”—increasing the density of a network as all the coworkers in an office ultimately join Dropbox.