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Kindle Notes & Highlights
by
Andrew Chen
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March 15 - July 27, 2023
“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.
Here is what’s often called the “Growth Accounting Equation,” which shows how these key metrics relate for active users: Gain or loss in active users = New + Reactivated − Churned
Overlaying revenue is easy, too. You just add two more variables, multiplying the active users number with the average revenue per active user (ARPU).
However, networked products are special in how they can leverage their networks to drive up each of these variables—something that traditional products can’t. As they grow and hit Escape Velocity, the density of the network makes the Engagement, Acquisition, and Economics effects more powerful, causing the input metrics to increase. More new users will appear, based on viral growth, and the product will get stickier, decreasing churn. More money will be made, as conversion rates increase.
The central inputs into a networked product’s growth equation will improve on their own, as a function of the network as opposed to the features of the product—creating an accumulating advantage over time. This is the magic of network effects.
One study50 published on tech blog TechCrunch told the story in its headline: “Nearly 1 in 4 people abandon mobile apps after only one use.” The authors looked at data from 37,000 users to show that a large percentage of users would quit an app after just a single try.
Of 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install an app, 70 percent of them aren’t active the next day, and by the first three months, 96 percent of users are no longer active.
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.
The first lever comes from the Engagement Effect’s ability to raise retention curves by layering on use cases.
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.
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.
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.
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.
The viral factor can also be above 1, in rare cases, but this typically can’t last for long—eventually market saturation and changing user demographics start to drag down the metrics. Once a metric like this has been defined, it becomes much easier to understand what changes in the product drive it higher. Retention is usually the strongest lever.
However, that’s not to say that innovations that generate large blasts of users—like using email contacts to easily invite friends, or referral programs with big bonuses—don’t help. They definitely do, but it’s a combination of big virality projects, lots of little optimizations, and strong retention that ultimately drives big viral factor numbers.
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.
Economic network effect, which is how a business model—including profitability and unit economics—improves over time as a network grows. This is sometimes driven by what are called data network effects—the ability to better understand the value and costs of a customer as a network gets larger. This helps in driving higher efficiency when promotions, incentives, and subsidies into a network.
Almost every large marketplace company is built on an underutilized asset, whether that’s unused real estate in the case of Airbnb, a car that’s sitting idle for Uber, or unused time for many labor marketplaces.
When B2B bottom-up startups can’t execute on layering on a sales team, then growth will inevitably slow. No wonder Slack and Dropbox, even with their early success with small businesses, eventually added enterprise sales teams as well.
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. You’d want to hit that in 7–10 years, to sustain the engagement of the key employees and also reward investors who often work in decade-long time cycles. These two goals—revenue and time—work together to create an overall constraint.
Neeraj Agarwal, a venture capitalist and investor in B2B companies, first calculated this growth rate by arguing that SaaS companies in particular need to follow a precise path to reach these numbers:64 Establish great product-market fit Get to $2 million in ARR (annual recurring revenue) Triple to $6 million in ARR Triple to $18 million Double to $36 million Double to $72 million Double to $144 million
The framework can be reverse engineered to get guidance for any type of company, with a few parameters needed to get there: Valuation goal Input metric Years to get to the valuation Empirical data on front-loaded growth
Networked products, on the other hand, have a massive advantage—they can tap into their network effects to fight the slowdown. For example, while the steady decline of marketing channels is inevitable, teams can amplify viral growth by optimizing sign-up funnels, recommendations for friends to invite, and so on.
Rather than focusing on the core network of Power Users—the loud and vocal minority that often drive product decisions—instead the approach was to constantly figure out the adjacent set of users whose experience was subpar.
When I started at Instagram, the Adjacent User was women 35–45 years old in the US who had a Facebook account but didn’t see the value of Instagram. By the time I left Instagram, the Adjacent User was women in Jakarta, on an older 3G Android phone with a prepaid mobile plan. There were probably 8 different types of Adjacent Users that we solved for in-between those two points.
the framework of the Adjacent User, teams need to continually evolve their offering to attract the next set of sellers or creators to their platform. For example, when Uber ran out of full-time limo drivers for its service, the next set of Adjacent Users was people who had never driven as a form of income. But eventually this pool was exhausted as well, and the company started to think about signing up people who didn’t own cars—the company would provide vehicles. And so on.
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.
At the micro level, an individual marketing campaign will typically see declining clickthrough rates over time—so teams have to refresh the messaging, images, and channels. At a more macro level, channels like email or paid marketing degrade over years,
Remove the stream of new users, and engagement within the platform from more tenured users can decline, too.
if the Law of Shitty Clickthroughs says that marketing channels decline over time, the other strategy is to embrace new marketing ideas early.
The Law of Shitty Clickthroughs is best countered through improving network effects, not by spending more on marketing.
The numbers are similarly concentrated: 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.
granddaddy of all internet communities: Usenet. Think of it as the very first social network. Created in the early days of the internet in 1980,
Today, this moment in internet history is known as the Eternal September, when a rush of inexperienced Usenet users flooded the network.
The entire life cycle of Usenet’s rise and fall serves as a cautionary tale for when networked products hit scale—they suffer from the combined anti-network effects of spam, trolling and other bad behaviors, and most important, context collapse.
Context collapse is what happens when too many networks are simultaneously brought together, and they collapse into one. It’s most problematic for social networks because it inhibits the behavior of content creators—the hard side—as they no longer are able to post photos that satisfy everyone in every context.
digital content directly affects your reputation. The bigger the network, the more people might see your content, rendering it unsafe to contribute. D’Angelo calls this the “unraveling” of networks—when a network loses its top creators, many of its consumers will leave as well. Lose enough consumers and it becomes less attractive to create.
Usenet was also blessed—and cursed—to be built as a decentralized, open-source protocol, as was common at that time.
constantly tweak and iterate on their product to respond to the behavior and needs of their audience. In many ways, this is where centralized control—usually in the hands of a well-funded company—is in a better position to address the myriad of challenges that might crop up as the network expands. A startup can quickly make changes to their discovery algorithms, user interfaces, and hire moderators—as we’ve seen many social apps do. In contrast, Usenet was never a company, never raised money, and didn’t have hundreds of full-time staff. Any new product confronted with millions of users
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Otherwise a form of social capital inequality sets in, and in the virtual world, where exit costs are much lower than in the real world, new users can easily leave for a new network where their work is more properly rewarded and where status mobility is higher.
Better matching between creators and their viewers alleviates the overcrowding issues that naturally emerge in a product that has more than a billion users.
Facebook built highly dense and engaged networks starting with college campuses versus Google+’s scattered launch that built weak, disconnected networks.
Rarely in network-effects-driven categories does a product win based on features—instead, it’s a combination of harnessing network effects and building a product experience that reinforces those advantages.
the good news is, your product has network effects. But the bad news is, so does your competition. It’s how you grow and scale your network that matters.
As people leave, the value of a network exponentially disintegrates, and it will impact Acquisition, Engagement, and Economics—meaning viral growth stalls, engagement is reduced, and monetization falls.
the Innovator’s Dilemma. Clayton Christensen’s influential book on business strategy describes how new players in a market start with seemingly undesirable niche segments, which are ignored by incumbents while they are focusing on the most profitable segments and use cases.
Network density beats total size, a theme we’ve seen throughout the examples of this book.
Had Airbnb been conceived as a tool to manage Craigslist listings and nothing else, it would have served at the leisure of its parent platform—grow too large, or make a wrong move, and it might be existential. Frequently the larger network will simply reach up and duplicate functionality if it gets too popular—a
it’s been so hard for “winner take all” to really happen in a literal way. The largest networks can take a lot, in many networks, but they remain vulnerable to any new upstart that uses cherry picking as a core strategy.
The Big Bang Launch is convenient for larger, more established companies as a method to launch new products because they often have distribution channels, huge engineering teams, and sales and marketing support. But counterintuitively, for networked products, this is often a trap. It’s exactly the wrong way to build a network, because a wide launch creates many, many weak networks that aren’t stable on their own.
