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Usually, these types of stunts and hacks are neither scalable nor repeatable. A funny viral video might work once or a few times, but it can’t be the only lever to drive growth over the long terms.
Every new city launch in the early days was a Cold Start Problem, and the city teams were structured to be autonomous and decentralized, able to react quickly to new ideas on the ground.
On the supply side, the Ops team would call local limo service companies one by one, stand outside major local events to pass out flyers, and text drivers to get them to start driving, among dozens of other highly manual tactics.
Uber started in major cities like San Francisco, New York City, and Los Angeles, and found that each one required dramatically different tactics.
Uber Ice Cream is fun, but the real magic comes from creating an innovative, bottom-up organization that can create endless variations of ideas like this.
The Ops teams would “holidize” their efforts, aligning special dates with product features that promoted growth.
But I argue that within the framework of taking a market from zero to the Tipping Point, these types of quick, clever tactics played a key role in getting markets off the ground.
In a research study called “How today’s fastest growing B2B businesses found their first ten customers,” startup veteran Lenny Rachitsky interviewed early members of teams from Slack, Stripe, Figma, and Asana.
The question isn’t which of these two routes to pursue, but instead how far your own network will take you before you move on.
It’s a huge advantage to have a strong personal network in B2B, which you can also build by bringing a connector investor or joining an incubator such as YC. Getting press is rarely the way to get started.
B2B startups have an equivalent card to play: they can manually reach out and onboard teams from their friends’ startups, building atomic networks quickly, as Slack did in their early launch.
They innovated with a referral program where users could give and get storage by inviting friends. User growth was explosive.
Dropbox’s users could be divided into High-Value Actives (HVAs) and Low-Value Actives (LVAs), which was useful as a quality indicator.
Dropbox, in the years before its IPO, came to orient itself in a new direction—to focus on highest-value users in the highest-value networks interacting with the highest-value files.
Often startup stories skip the middle part of the story, going from origin story to IPO in a few short paragraphs.
In this phase, the challenge quickly becomes maintaining a fast growth rate and amplify a successful product’s network effects.
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.
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 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.
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.
Gain or loss in active users = New + Reactivated − Churned
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.
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.
I published an essay titled “Losing 80% of mobile users is normal,” which illustrated the rapid decay that happens right after a new user signs up to a product.
the users who install an app, 70 percent of them aren’t active the next day, and by the first three months, 96 percent of users are no longer active. The shape of the retention curve matters a lot—ideally,
its curve consistently falls over time, eventually whittling itself to zero. The brutal conclusion is that the usual result for most apps is failure—but there are, of course, exceptions.
As a rough benchmark for evaluating startups at Andreessen Horowitz, I 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. That’s because networked products are unique in that they often become stickier over time, which cancels out the inevitable customer churn.
In rare but exceptional cases, the curve will “smile”—meaning that retention and engagement will actually go up over time,...
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Based on this segmentation, product teams can search for a “lever” that will move users from one level of engagement to the other.
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.
The question then becomes how to get these users to take the various actions that will make them higher value.
Content and communications might be a series of how-to videos teaching effective use of LinkedIn’s connection features. And an incentive might look like a free subscription when the user completes certain actions.
For a workplace chat product, how do you make sure the right people are in the channel where you post your content? How do you create easy, positive feedback to encourage people to continue participating—whether that’s emojis, likes, or something else? Do your users have enough connections to consistently close the loop, and if not, how do you quickly get a critical density of networks around them?
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.
In other words, up to 75 percent of users are inactive at any given point, most of whom will never come back.
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.
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.
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.
a Dropbox user might engage infrequently because they are only on one important shared folder. But when their colleagues eventually share a dozen more important project folders, Dropbox might become a critical part of their workflow. The bigger the network, the more likely an infrequent user continues to get reengaged, and over time, that might make all the difference.
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.
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.
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.
the embeddable player for YouTube, that could be added to any blog or MySpace profile. Or LinkedIn’s use of email contacts to connect with your work colleagues. Or Eventbrite’s emailed invitations to an audience of potential attendees.
The eBay community was a tight-knit network, and PayPal quickly spread. The initial product had fewer than 10,000 users. Within a few months, PayPal had 100,000 users. A few months after that, 1 million. Within a year, 5 million.
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.
This loop is fundamentally created within the product experience by software engineers writing code, which makes it different from a fun, viral video—because it’s software, it can be measured, tracked, and optimized to make it more effective. This
measuring for conversion rates and the number of invites sent. Optimizing each of these steps with A/B tests might only boost each step’s conversion by 5 percent here or 10 percent there, but it’s a compounding effect.
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.
