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The real magic starts to happen as the viral factor starts to approach 1. After all, at a viral factor of 0.95, 1,000 users show up and then bring 950 of their friends, who will then bring 900, and so on—ultimately the amplification will be 20x.
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. They are often unique to the product itself—making them proprietary and more defensible.
Yes, in this way, it’s just like multilevel marketing campaigns, Ponzi schemes, and the like. And of course, what happens with both chain letters and Ponzi schemes is that they collapse when the supply of new, novelty-seeking recipients dries up. As a result, existing participants stop getting paid. This in turn causes churn, which then unravels the network entirely.
network needs retention to thrive; it can’t just continually add new users.
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
This is a clear example of the Economic Effect, where a larger network has much higher efficiency than a small one—the company burns significantly less per trip, because the network can deliver more demand on an hourly basis.
This form of Economic network effect can be strengthened as more participants join a network, because the additional data allows for personalization and targeting. In Uber’s case, instead of a fixed $25/hour guarantee across the whole network, drivers can receive personalized offers based on sophisticated machine learning models.
Dropbox’s High-Value Active Users is an example of this—users were found to upgrade into paid subscriptions when they had collaborative use cases with their coworkers, like shared folders and collaboration around documents.
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.
As a result, a more developed network creates an incentive for people to invest in their standing within the game—this is the Economic Effect at work.
Because there are negative forces that appear during the late stage of a network’s life cycle. Market Saturation. Churn from early users. Bad behavior from trolls, spammers, and fraudsters.
The company also eventually started their own annual conference, TwitchCon, which became a real-life venue for viewers to meet their favorite streamers.
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.61
In the end, only new products and innovation will kick off the next big growth curve, which is what encourages startups to grow from single products into multi-product companies.
There are hundreds of exits per year, but only a few dozen are large enough to define the industry.
Hitting a $1 billion valuation generally requires at least $100 million in top-line recurring revenue annually, based on the rough market multiple of 10x revenue.
The first phase, in which the team initially gets to product/market fit, takes 1–3 years. Add on the time to reach the rest of the growth milestones, and the entire process might take 6–9 years.
This means to achieve $1 billion in valuation, a company must hit $200 million in net revenue. And you want it to happen in year 10.
In year 1 and 2, you might argue there’s probably zero revenue since the team will be focused on product development. Then year 3 becomes about solving the Cold Start Problem, and only in year 4 does it get to meaningful revenue.
This is where the Rocketship Growth Rate starts to look formidable. It turns out that doubling a product’s revenue each year isn’t enough. Doubling over six years is 64x, and it wouldn’t hit the goal in time.
This is an average growth rate, and typically products grow fastest in the early years when revenues are small. A trajectory like 5x 4x 3x 2x 1.5x 1.5x would work, as would 4x 3.5x 3x 2x 2x 1.2x.
When you grow fast one year, you start to expect that you’ll need to grow as fast or faster in the next. Ambition grows, as the vision gets bigger.
Networked products, on the other hand, have a massive advantage—they can tap into their network effects to fight the slowdown.
Success comes with an inevitable problem: market saturation.
This phase of eBay’s story is not unique for fast-growing startups. What looks like an exponential growth curve is often, in reality, a series of lines layered quickly on top of each other.
diminishing returns arise at larger numbers. And travel booking sites, app stores, and many other marketplaces face the same thing.
Our success was anchored on what I now call The Adjacent User Theory. The Adjacent Users are aware of a product and possibly tried using it, but are not able to successfully become an engaged user. This
In the framework of adding layers to a cake, serving each adjacent network is like adding a new layer. Doing this requires a team to think about new markets, rather than listening to their vocal core markets—a hard feat when the core market generates most of the revenue.
The cheat code for large companies is simply to buy startups that have hit Escape Velocity, and integrate them into a preexisting network.
Today, banner advertising clickthrough rates usually hover around 0.3–1 percent, but the first ads ever had incredible engagement: 78 percent at the start! This was a novel way to reach consumers, and people were curious, so they clicked. But more than two decades later, it’s dropped to 1/100th of its initial levels.
Consumers acclimate to specific brands, marketing techniques, and messaging, and tune them out.
The solution to the Law of Shitty Clickthroughs is to embrace its inevitability. When new products launch, there are usually one or two acquisition channels that work—but they might not scale.
For example, in Twitch’s journey, the team deeply focused on creators, giving them better tools and monetization, which in turn caused them to become more active. More satisfied creators meant they would broadcast live video streams more often, bringing in more viewers, which drove further engagement and monetization.
Slack’s S-1 filing showed less than 1 percent of Slack’s total customers accounted for 40 percent of the revenue, and Zoom’s indicated that 30 percent of revenue came from just 344 accounts, again less than 1 percent of their customer base.
as a network scales, its hard side will professionalize. Quality and consistency will probably increase, and the most sophisticated players will be able to do it at scale. On the other side of the paradox, this dynamic eventually misaligns incentives—drivers, sellers, and creators might protest. App developers might complain, quit, or compete with you.
Eventually, Uber needed to convince people who’d never earned income by driving to try it—the market of professional drivers was saturated. It needed to grow the market, and onboard a larger and more mainstream segment of users. This segment of drivers required more education, more vetting, and more encouragement on how they should interact with riders.
The breakdown of netiquette meant that it became harder to discover the pockets of high-quality conversation that defined Usenet’s early years. People began to migrate to other technologies—online groups, mailing lists, and eventually social networks.
The problem is not lack of context. It is context collapse: an infinite number of contexts collapsing upon one another into that single moment of recording. The images, actions, and words captured by the lens at any moment can be transported to anywhere on the planet and preserved (the performer must assume) for all time. The little glass lens becomes the gateway to a black hole sucking all of time and space—virtually all possible contexts—in on itself. The would-be vlogger, now frozen in front of this black hole of contexts, faces a crisis of self-presentation.72
When far-off remote offices, more managers, and many hundreds of other employees are added, your usage might become more muted, as a poorly phrased joke or overly casual remark that might be fun for your team might land poorly for others.
For a marketplace product, an early community of high-end sneaker enthusiasts might grow and eventually find itself inundated with casual buyers who care more about affordability.
Products like iMessage or WhatsApp give us a clue. Messaging apps are resistant to context collapse. You talk to your dozen or so friends and family, and even if the network adds millions more people, it doesn’t change your experience.
Context collapse is meant to be managed carefully, so that there’s enough discovery to hold the network together, but not in such a way that it alienates or overwhelms users.
The way Reddit handles content moderation today is unique in the industry. We use a governance model akin to our own democracy—where everyone follows a set of rules, has the ability to vote and self-organize, and ultimately shares some responsibility for how the platform works.
Human upvotes, downvotes, and flagging are all inputs into automated systems that can be built. Software allows users to create and enforce standards on a network—this is “netiquette” embedded into the product, in software form.
We had the idea that everyone who uploaded a video would share it with, say, 10 people, and then 5 of them would actually view it, and then at least one would upload another video. After we built some key features—video embedding and real-time transcoding—it started to work.
One hypothesis on why social networks tend to lose heat at scale is that this type of old money can’t be cleared out, and new money loses the incentive to play the game.
This helped scale the density of the LinkedIn network so that even after you added hundreds of connections, the site could still help recommend relevant people to you.
Workplace collaboration tools are user friendly when there are only a few folders and people to keep track of, but once it spreads to the entire company, the UI needs to evolve to handle searching among the projects of hundreds of people.
All supply isn’t created equal. Wimdu’s top 10% of inventory was at the bottom 10% of Airbnb’s. They went for numbers, but recruited large property owners that managed hundreds of units in the form of low-end hostels.
You need this high NPS to get people to tell their friends, and it makes hosts more likely to join too. Our competitors who took shortcuts couldn’t deliver here.81
