Andrew Chen Archives

Hi readers,

My first book — The Cold Start Problem — is out next week!!! The countdown begins. You can preorder it here as hard cover, kindle, or audiobook. And yes, I recorded the audiobook myself.

I have a bit more more audio today to talk about as well… When I first moved to the San Francisco Bay Area in 2007, I was introduced to Tim Ferriss at a dinner — before he wrote 4 Hour Work Week, before his podcast, when we were both getting started. We lived not too far from each other — I was in an apartment with roommates in Bernal Hill. I remember visiting his place and checking out these weird cannonballs with handles on them — kettlebells — and talking about how he was going to launch his book.

Years later, it’s been awesome to follow his success and today, something new:

I’m on his podcast — The Tim Ferriss Show — for the first time. The episode was just released this morning. You can listen to it on Apple Podcasts, Spotify, Overcast, Podcast Addict, Pocket Casts, Stitcher, Castbox, Google Podcasts, Amazon Music, or on your favorite podcast platform.

A couple of the topics we talk about:

• “Growth hacking” and how it became popular
• The history of marketing — coupons, direct marketing versus brand, and more
• Games and the future of the Metaverse
• The magic of Bay Area tech companies
• The creator economy
• Web3 and how it’ll impact consumer startups
• The Cold Start Problem and what it was like to write it

I hope you enjoy!

Thanks,
Andrew
(Back from a long road trip from Zion, Bryce, Sedona, etc over Thanksgiving)

Hi readers,

In just a few weeks, I will be dropping new book, The Cold Start Problem!! If you haven’t gotten your copy — if you are in the US, here are the relevant links:

• Amazon · Bookshop (support your local bookstore!)
• Or if you are international, go to the coldstart.com website to find all the pages for Europe, Asia, and more.

Thanks to all of you for the tens of thousands of emails, tweets, and preorders in support of my work. If you’re reading about this book for the first time, you might be asking yourself — what is the Cold Start Problem about?

Let me set it up in a few bullets:

• Many of tech industry’s most valuable products — Slack, Zoom, Instagram, Twitch, YouTube, and others — are at their core products that connect people to each other. People are connected for commerce, communication, collaboration, and more
• I’ve come to believe this is the secret of much of Silicon Valley’s success. I saw it first-hand at Uber, which scaled to billions in revenue, and also within startups at Andreessen Horowitz, which has funded companies from Github, Coinbase, and Figma to Clubhouse and Airbnb
• These types of products benefit from “network effects” — that they become more useful as more users engage. This is why they grow to be so powerful, and valuable. But also why they are impossible to get off the ground, because people won’t use products where their friends/colleagues aren’t already engaged
• The Cold Start Problem is a book a collection of case studies, from Tinder, Twitch, credit cards, Dropbox, and others — about the lifecycle of these networked products. How to get them started, how to scale them, and what it’s like to compete
• These frameworks are targeted at teams building new products, first and foremost. But it’s also for people who work in the business of commerce, travel, publishing, and many other industries that are getting reinvented by tech.

I hope that’s a good teaser :)

In previous posts about the book, I’ve hinted at its contents. But over the next few weeks, I’ll be more substance and previews of the content.

Today, for the first time, I’m going to provide a preview of the opening chapter of The Cold Start Problem — which is partially about my experience at Uber and Andreessen Horowitz, but also about why I began writing in the first place.

I hope you enjoy it!

Thanks,
Andrew
writing from Venice, CA

 

The Cold Start Problem

Introduction
It was 2015 in December, and on a Friday evening, the office was buzzing. Amid the vast, monochromatic corridors of Uber’s San Francisco headquarters at 1455 Market Street – two football fields worth of gleaming LED lights, light woods, concrete, and steel – the office was still mostly occupied at 8PM. Some sat at their desks quietly typing email while others debated energetically with colleagues over videoconference. Others were drawing on whiteboards, hosting impromptu jam sessions to tackle the tricky operational problems facing who-knows-what. And a few pairs of employees were walking up and down the main flow in 1-on-1 meetings, some in intense discussion and others just catching up.

Everywhere you looked, there were reminders of the global scale of Uber’s business as well as the international heritage of the team driving it. Colorful flags from every country hung from the ceiling. Conference room screens hosted videoconferences with colleagues from faraway offices in Jakarta, San Paulo, and Dubai – sometimes simultaneously! Flat screen TVs were scattered throughout the floor showing metrics, broken down by mega-region, country, and city, so that teams could monitor progress. The global culture seeped all into the naming conventions for conference rooms: Near the entrance, the names started with Abu Dhabi and Amsterdam, and at the far other end of the floor, ended with Vienna, Washington, and Zurich.

At first glance Uber might just look like a simple app — after all, the premise was always to hit a button and get a ride. But underneath its deceptively basic user interface was a complex, global operation required to sustain the business. The app sat on a vast worldwide network of smaller networks, each one representing cities and countries. Each of these networks had to be started, scaled, and defended against competitors, at all hours of the day.

It was in my role at Uber that I really came to viscerally understand networks, supply and demand, network effects, and their immense power to shape the industry. As you might imagine, the Uber experience had its ups and downs – it was a rocketship and a rollercoaster, rolled into one. I’ve come to call it a “rocketcoaster” experience, which is an appropriate description for a company that had went from an idea to a tiny startup to a massive global company with over 20,000 of employees in less than a decade.

The worldwide operations of the company was complex and intense, and much of the command and control radiated from the center of the of Uber’s San Francisco headquarters. In the middle of the main floor, built from gleaming surfaces of glass and metal, stood the War Room.

To many, it was a big mystery – the War Room didn’t share the normal naming convention of city names where Uber operated. It couldn’t be booked for meetings as the others could, and was sometimes attended to by security guards. That’s because it wasn’t a normal meeting room. Many companies (inside and outside of tech) have the notion of “war rooms” but they are typically conference rooms converted temporarily to dedicated use by a product team that works intensely to tackle an emergency project, and after the situation is resolved, is quickly converted back into normal use. For Uber, perhaps appropriate to its unique needs, this War Room was not temporary at all – it was built to operate 24 hours, around the clock. It was built as a huge, permanent room with dark wood walls, multiple flatscreen TVs, a large conference table that could fit a dozen people, with additional sofa seating. Red digital clocks gave the current times in Singapore, Dubai, London, New York, and San Francisco. Given the company’s global footprint, there was almost always some kind of emergency situation somewhere in the world that needed attention, and this was often the room where it was dealt with.

That December, the emergency was in San Francisco, the company’s hometown.

Scheduled to start at 7pm and run into the night, the urgent meeting was booked on everyone’s calendar as “NACS” – which stood for the North American Championship Series, an oblique reference to its agenda focusing on operations, product roadmap, and competitive strategy in the top markets in the US and Canada. This meeting was a key mechanism for the CEO of Uber, Travis Kalanick – called “TK” within the company – to review the entire business, city by city.

A small group of about a dozen executives and leaders attended the meeting, including myself and the heads of finance, product, and critically, the RGMs — short for “Regional General Managers.” The RGMs ran the largest teams at Uber, constituting the on-the-ground Operations city teams that engaged with drivers and riders. The RGMs were thought of as the CEOs of their markets, holding responsibility for revenues and losses, the efforts of thousands of Ops folks, and were always closest to the trickiest problems in the business. I was there to represent the Driver Growth Team — a critical team responsible for recruiting the scarcest asset in the entire business, Uber drivers. It was a big effort for Uber — we spent hundreds of millions just on driver referrals programs, and nearly a billion in paid marketing. Adding more drivers to the Uber network was one of the most important levers we had to grow the business.

The weekly NACS meeting opened with a familiar slide: A grid of cities and their key metrics — tracking the top two dozen markets. Each row represented a different city, with columns for revenue, total trips, and their week-over-week change. It also included operational ratios like the percentage of trips that hit “surge pricing,” where riders had to pay extra because there weren’t enough drivers. Too much surge, and riders would switch to competitors. Uber’s largest markets, New York, Los Angeles, San Francisco were always near the top as the list, representing billions of annual gross revenue each, with smaller cities like San Diego and Phoenix near the bottom.

TK sat closest to screen, dressed casually in a gray t-shirt, jeans, and red sneakers. At the sight of the numbers, he sprung up from his chair and walked up close to the screen. He squinted, staring intensely at the numbers. “Okay, okay…” he said, pausing. “So why did surge increase in San Francisco so much? And why is it up even more in LA?” He began to pace up and down the side of the War Room, the intensity of the questions increasing. “Have we seen referral signups dip in the last week? How’s the conversion rate in the funnel going? Were there a big events this week? Concerts?” Folks in the room began to chime in, answering questions and raising their own.

A network of networks
It was my first year at the company, and although many companies have weekly reviews, Uber’s were different. First, in the discussion about each city, the level of detail surprised me. For San Francisco, the group began to discuss the surge percentages in the city’s seven-by-seven versus East Bay, versus the Peninsula. This was a senior group of executives, but the granularity and level of detail was incredible. But this was a requirement to run a complex, hyperlocal network like Uber where supply and demand went down to popular neighborhoods and frequent “lanes” — like Marina and the Financial District — which tended to be poorly served by other transportation options.

In the weekly dashboard, each row represented a city — yes — but more importantly each city was an individual network in Uber’s global network of networks that needed to be nurtured, protected, and grown. 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.

The NACS meetings were used to evaluate the health of each of the networks and the global network as a whole — a central means of accounting for the 20 or so cities that represented the majority of revenue to the company. Furthermore, it was important to go even further in granularity and break the network into the two sides, both the rider side (demand) or the driver side (supply), to make sure each side was healthy but also that they were in balance with each other. Too much surge, and riders stop taking trips. Too little surge, and drivers start to go offline and head home after a long night.

The slides continued. Several of us on the NACS team, including myself, had been working on a hypothesis over the past few days. Ops teams had reported seeing large increases in driver referrals by our primary US competitor, Lyft, over the past few weeks, which was causing drivers to switch over in droves. Driver referrals typically structured as a give/get incentive — give $250 and get $250 when your friend signs up to drive. In conjunction with a dramatic rise in demand during the holiday season, it was causing a big undersupply of drivers in the key competitive markets on the West Coast, primarily SF, LA, and San Diego. For riders, this resulted in a terrible experience — if you request a ride, it would take far longer than usual, sometimes twenty minutes, which meant more riders were canceling their requests. They might even decide to check our competitor’s pricing and service level, and book there instead. These cancels were frustrating for Uber’s drivers, who might have already driven for a few minutes. Piss them off too many times, and it might cause a chain reaction as they’d have even more incentive to stop for the night, switch off to a competitor’s network.

TK grew more intense and agitated as the hypothesis was presented. “This is not good, guys. Not good.” He exhaled deeply. What was the right solution? With the years of experience from operating these networks, it was likely that one solution would be in quickly rebalance the sides of the market. The right solution would need to start on the supply side, to grow our base of drivers quickly and lower ETAs and the cancel rate, and that meant a driver incentive. “What if… we did a $750 / $750 referral bonus here in SF, LA, and San Diego?”

This would be a big move, a far bigger number than had ever been thrown out. But SF, LA, and San Diego needed the help. These were some of the most competitive markets that would need to be quickly rebalanced with more supply. TK looked around the room, pausing, and then answered his own question. “Yeah. That would get their attention. That’ll wake them up!” he said, smiling and nodding.

Others were not so quick to jump to incentives as the solution. The past year had been good for Uber in the US, turning it into a cashflow positive area as the competition in the new China business simultaneously generated both incredible trips growth as well as severe losses. Uber was in a vicious fight with Didi — its Chinese rideshare competitor — burning on the order of a billion dollars a year primarily because of incentive spend. We started to bat around other ideas, from improving how to display ETA estimates as well as ways to discourage riders from canceling. There were other ways to rebalance the various networks without using incentives, which is a powerful tool but not the only one. The conversation went in circles, and TK grew visibly frustrated.

TK paced around the room again. “No, no! Look, guys. Our network is collapsing. We need to stop the bleeding… now!“ He chopped his hand into the other. “Let’s do the other stuff and get it on the roadmap, but let’s get this email out over the weekend. Who can help me put it together?” This decisiveness was informed by years of fierce in-the-trenches competition — companies like Flywheel, Sidecar, Hailo, and many others that were vanquished — driven by lightning fast responses in situations like this. The Uber team monitored and responded to the health of their local city networks with speed and precision. And with that, the next step was clear.

The RGMs agreed to own it, and I would work with my team — which was accountable the product/engineering side of driver referrals — to make changes to the structure and amounts. We committed to ship the changes before Monday. We took note of a number of other follow-ups due from the meeting, and we all decided to reconvene the group again next week. It was Friday and almost 10pm, and many of us had been working since early morning to prep for this meeting. I walked home, just a few short blocks away in the Hayes Valley neighborhood of San Francisco, and started my “Netflix and email” routine to close out the day.

This was my first experience with the North American Championship Series, and it turned into a weekly briefing, usually Friday mid-morning. But sometimes it got scheduled at Tuesdays at 9pm, or Sundays at 2pm, when that was the only way to get everyone together. Although NACS was just one part of my role at Uber, it quickly became one of the most educational in how to think about starting and scaling network effects. For a multiyear span, I was lucky to embedded in this critical team that operated Uber’s biggest markets. Each week was different. At the NACS meetings, we shifted our attention nimbly each session from network rebalancing on the West Coast, to prioritizing product features to increase revenues, to launching new regions, and everything in between.

Uber was already hitting its stride when I joined but I had a front row seat to the team that took grew the business to 100 million active riders in 800+ markets worldwide, and $50B in gross revenue. It was an incredible experience, and am proud of the work that we did there. It didn’t happen automatically — there were tens of thousands of people working hard to deal with network dynamics in hundreds of markets around the world, and we learned all the hard lessons from competing with fearsome local competitors who have their own strong network effects too. I’m lucky to have been at Uber during a hypergrowth period, where I joined just at the base of the hockey stick curve when it was well under a billion trips, but saw it 10x over the next few years:

My time at Uber was an unforgettable experience. I got to see a startup scale to tens of thousands of employees, millions of customers, and billions in revenue. I saw new products start at zero and then rapidly scale up to dominate the market. It was a deeply educational journey, one that created many lifelong friendships — including people I still talk to every week. But by 2018, it was time for me to move on. The company had a tumultuous few years, a complete changing of the guard, and a new set of priorities that were less entrepreneurial than in the past. I wanted the opposite of that, and for my next chapter, I decided to go back to my roots: Working with entrepreneurs to build the next new thing, but this time, as a venture capitalist.

Foundational questions
In 2018, I began a new career after Uber, as a startup investor at Andreessen Horowitz. Started a decade earlier by entrepreneurs Ben Horowitz and Marc Andreessen, the firm made a splash when it launched, quickly making a series of notable investments in startups including Airbnb, Coinbase, Facebook, Github, Okta, Reddit, Stripe, Pinterest, Instagram, and others. The firm was built around a philosophy of hands-on operating expertise — this fit me perfectly as I would parlay my lessons from Uber into picking and building the next great technology startups.

Rejoining the startup world, this time as an investor, let me tap into a network of relationships and knowledge built over a dozen years in the San Francisco Bay Area. Pre-dating Uber, I had been writing and publishing nearly a thousands essays on topics like user growth, metrics, viral marketing — along the way, popularizing tech industry jargon like “growth hacking” and “viral loops.” My blog was would be read by hundreds of thousands, and due to this as well as the natural serendipity of the startup ecosystem, I came to become acquainted with a broad community of entrepreneurs and builders. I would come to serve as an advisor and angel investor to dozens of startups, including Dropbox, Tinder, Front, AngelList, and many others. All of this, combined with my expertise from Uber, would be the foundation to launch my career in venture capital.

Everything was different in the new role. Rather than commuting to Uber’s offices in the chaotic center of San Francisco, instead I headed to the firm’s idyllic offices near Stanford University. The a16z offices combine culture and invention — its hallways lined with artwork from Rauschenberg, Lichtenstein, and contemporary artists, while its conference rooms are named after great inventors and entrepreneurs like Steve Jobs, Grace Hopper, Ada Lovelace, and William Hewlett. The work was very different from Uber’s day to day as well — rather than going very deep into one sector, like rideshare, instead my purview was extremely broad.

Every day I was meeting with entrepreneurs to talk about their new ideas. In a given year, the firm might see thousands of startup ideas, many of which are new kinds of social networks, collaboration tools, marketplaces, and other new products — relevant to the examples to this book. Conversations with startups begin with a “first pitch” meeting, where the entrepreneurs introduce themselves, show the product, and talk through their strategy. These are pivotal meetings, because when they go well, the startup could eventually receive an investment in the millions or even hundreds of millions of dollars. It’s high stakes.

Jargon thrives in these presentations: “Network effects.” “Flywheel.” “Viral loops.” “Economies of scale.” “Chicken and egg.” “First mover advantage.” These are some of the buzzwords and jargon that get thrown around in pitch meetings. And they are often accompanied with diagrams full of arrows and charts going up and to the right. The term “network effect” has almost become a cliché. It’s a punchline to difficult questions, like “What if your competition comes after you?” Network effects. “Why will this keep growing as quickly as it has?” Network effects. “Why fund this instead of company X?” Network effects. Every startup claims to have it, and it’s become a standard explanation for why successful companies break out.

But with all of these discussions and pitches, I realized I was getting confused, and I wasn’t the only one. While “network effects” and its related concepts were often invoked, there was no depth to the idea. No metrics that could prove if it was really happening or not.

In my work with startups, and after a decade and a half of living in the San Francisco Bay Area, I’ve heard “the network effect” used a zillion of times in conversation. Sometimes over coffee, in meetings, or in investor discussions, but the concept was always discussed at a superficial level.

So how do you hear something thousands of times and still not quite understand it?

I argue that we don’t understand network effects well because if it were a straightforward concept to understand, we would be in strong agreement on which companies have network effects, and which ones don’t. We would know what numbers to look at to validate it was really happening. And we’d have a step-by-step understanding of how to create and build up network effects. And yet we don’t. And it bothers me to a great degree, because it is has become a critical topic in today’s technology landscape. This is the journey that brought me to writing this book.

I began to research and to write THE COLD START PROBLEM because I found my own understanding of the dynamics of networks to be unforgivably shallow for something so core to the technology industry. The network effect is something I’ve seen firsthand at Uber, and yet I lack the vocabulary and the frameworks to articulate the deep nuances.

There’s a gap between the practitioners and the rest of the business world. For practitioners who work on specific networked products, the focus is on improving the mechanics within their very particular domains. Within rideshare, the discussion revolved around riders and drivers, reducing pickup times, surge pricing, and an accumulated set of specialized vocabulary and concepts that only apply to on-demand transportation. For a workplace chat tool, it’s about channels and discovery and notifications and plug-ins. They feel unrelated, even though both product categories have deep network effects and are both ways to connect people. There should be a set of universal concepts and theories to talk about network effects, regardless of their product category.

We need to be able to answer the basics:

What are network effects, really? How do they apply to your business? How do you know if your product has them — and which other products don’t? Why are they so hard to create, and how do you create them? Can you add a network to your product after the fact? How do they impact your business metrics, at the tactical level? Is Metcalfe’s Law actually right, or should you apply something else to your strategy? Will your network fail and will it succeed? Does your competitor have network effects, and if so, what is the best way to compete with them?

Startup advice says, all that matters is to build a great product — after all, that’s what Apple does. But why has it also been so critical to launch products in the right way? To get your product in the hands of influencers, or high school students, or aspirational technology companies — if B2B — if all that matters is the product? What’s the right way to launch, and what’s the sequence of ways to expand?

How do you build network effects in your product? How do you know when network effects are kicking in, and if they are strong enough to create defensibility? How do you pick the right metrics to optimize to achieve viral growth, re-engagement, defensibility, and other desired effects? What product features do you build to amplify network effects?

When fraudsters, spammers, and trolls inevitably show up, what’s the proper recourse? What have we seen other networks do in the past to combat the negative effects of a large, thriving network? And more generally, how do you keep scaling a network that’s already working, especially in the face of saturation, competition, and other negative dynamics?

What happens when two networked products compete — what makes one player win over another? Why did we see big networks often succumb to smaller ones? How do you launch new networks across new geographies and product lines, particularly in competitive markets?

These are the most fundamental questions we can ask about network effects, and when you search for the answers — whether in books or online — there are only smatterings of actionable, pragmatic insights though there was plenty of high-level strategy. The best thoughts came from operators, at startups and bigger companies, who have done been in the trenches and so that’s where I started the process of writing my book.

I began by conducting more than a hundred interviews with the founders and teams that built Dropbox, Slack, Zoom, LinkedIn, Airbnb, Tinder, Twitch, Instagram, Uber, and many others. I asked them questions to learn about the earliest days, when it was just the co-founders and a handful of other people trying to take on the world. I also researched historical examples spanning hundreds of years — going back to chain letters, credit cards, and telegraph networks, and tying their success to modern innovations in Bitcoin, livestreaming, and workplace collaboration tools. All of this exposed a rich set of qualitative and quantitative data which forms the foundation of this book.

I found that people were repeating the same ideas and concepts, and observed that they were recurring throughout multiple sectors. You could talk to someone who spent their career working on social networks, and find that they had ideas that were equally applicable to marketplaces. Similarly, my time at Uber made me understand the dynamics in a network of riders and drivers, which informs my view of products like YouTube and its two-sided network of creators and viewers. Or Zoom, with its meeting organizers and attendees. Dozens of these recurring themes echo throughout the industry, whether we’re thinking about B2B or consumer products.

The definitive guide to network effects
THE COLD START PROBLEM is the culmination of hundreds of interviews, two years of research and synthesis, and nearly two decades of experience as an investor and operator. It takes much of the knowledge and core concepts swirling inside the technology industry and frames them in the context of the beginning, middle, and end of a network’s lifecycle. This is the core framework I’ll describe via the major sections of this book, along with examples and hopefully inspiring you to take actionable can apply to your own product.

This is a critical topic. I’ve come to see network effects — how to start them, and how to scale them — as one of the key secrets of Silicon Valley. There are just a few dozen software products with a billion active users on the planet, and many of them share lineages of founders, executives, and investors who have unique expertise. This knowledge, in turn has been developed in the tech community over decades of building social networks, developer platforms, payment networks, marketplaces, workplace apps, and so on. This community of elite talent collaborates and cross-pollinates, switching from one product category into another, bringing all of this knowledge together. I have seen this first hand, and my interviews with founders and experts in writing THE COLD START PROBLEM further illustrated the interconnectedness of these concepts.

Based on the foundational theories of network effects, I’ve taken these lessons and put skin in the game, focusing my venture capital investing at a16z towards products that have networks at their core. I find myself most captivated by new startups where connecting people lay at the heart of the product, whether for communication, socializing, work, or commerce. I’m now 3 years into the industry, and have invested over $400M into over two dozen startups in marketplaces, social apps, video and audio, and more. I’ve found my learnings about network effects to apply widely across the industry — everything from Clubhouse, which seeks to build a new audio social app, or Substack, which lets writers publish and monetize premium newsletters for their readers. And even video games, food pickup, or edtech.

My goal is to write the definitive book on network effects — one that was practical enough, and specific enough, to apply to your own product. You should be able to use its core framework to figure out where your product is on the journey, and what product efforts are needed to drive it forward. I’ve tried to lay out the entire lifecycle — from the underlying mechanics of how to create network effects, how to scale them, and the best way to harness them — all from a practitioner’s point of view, diving deep far beyond the buzzwords and high-level case studies that have been written.

The first phase of the core framework, naturally, is called The Cold Start Problem, which every product faces at its inception, when there’s no users. I’m borrowing a term here for something many of us have experienced during freezing temperatures — it’s extra hard to get your car started! In the same way, there’s a Cold Start Problem when a network is first launched. If there aren’t enough users on a social network and no one to interact with, everyone will leave. If a workplace chat product doesn’t have all your colleagues on it, it won’t be adopted at the office. A marketplace without enough buyers and sellers will have products listed for months without being sold. This is the Cold Start Problem, and if it’s not overcome quickly, a new product will die.

This is all in the service of helping you, the reader, whether you are a software engineer, designer, entrepreneur, or an investor. Perhaps you partner with one of these companies I reference throughout the book, or are seeing technology reinvent your industry in the form of networks. Network effects are a powerful and critical force in the technology sector — as the entire economy is increasingly reinvented, it will become even more important to understand.

But let’s not get ahead of ourselves — first, what’s a network effect, anyway?

Above: Prosperity Club, one of the first chain letters. This is the type of spikey, bad viral growth you don’t want!

Hello readers,
Viral growth — specifically the type baked into product features, in the form of invites, sharing, and collaboration — is a topic I’ve been endlessly fascinated by. It’s the central force that has propelled many of the world’s most popular products to billions of users. I’ve been talking about viral loops, k-factors, and invite mechanisms for almost ten years now — it’s a favorite topic.

However, having implemented many of the ideas in my own startup products (back in the day) and then in viral referral programs at Uber, I have several new interpretations of these classic ideas that I want to share. Importantly, I have a chapter of my upcoming book, The Cold Start Problem, where I argue that viral growth is a flavor of network effect (more on that later) — which has important implications on how to amplify its effect.

(And by the way, you can order my upcoming book here! I appreciate your support)

I plan to write a few essays on this topic — it’s relevant, given the new trend of “Product-Led Growth” in B2B, and the resurgence of new social apps in the wave of Creator Economy startups. It’s time to revisit this topic in more detail.

thanks,
Andrew

 

Viral growth and retention are inextricably linked
Most people think of viral features in their app — like sharing, collaboration, or invites — in the context of a single session. How do you get your users, upon installing an app, to invite at least a few of their colleagues and friends? You might focus on small growth hacks, like leveraging their addressbooks for inviting, pumping up the dollar amounts in referral codes, or radically optimizing a sharing flow. But this is a simple minded way to look at viral growth.

The better way to understand virality is that it is inextricably linked to retention. After all, if a user retains well and is likely to use the product almost every day through the course of a month, you’ll have 30x the number of visits as a product that doesn’t retain well and generates a lot of one-and-done users. Each visit is a small opportunity to prompt them to share, invite, or otherwise bring a friend in. You’ll have 30 shots a month, and each shot can add up through the course of time to represent a lot of invites sent, or content shared. Whereas a poorly retaining product has be aggressive, making most of the one session where the user will engage, a sticky product can take its time, building viral growth over weeks and months. When you think about this mathematically, it makes sense — a (nearly) infinite sum of invites generated by a sticky product will eventually surpass a single, static number generated in one or two sessions.

But I get ahead of myself. First, what do I mean by viral growth anyway?

Viral growth is often misunderstood
Let’s start with all the confusion with the term “viral growth” in the context of new products. Let me excerpt a passage from The Cold Start Problem where I describe how it’s often misunderstood:

Viral growth is deeply misunderstood — you might read the phrase and think, is this the same thing that happens when a funny video “goes viral?” Or maybe it makes you think of an ad agency organizing a clever stunt, like a flash mob, where dozens of people simultaneously begin to dance, and then sharing it on social media?

No, what I’m referring to is completely different. What ad agencies have called “viral marketing” is usually to take consumer goods or services — usually with no network effects — and build an advertising campaign around it, usually with a bit of shareable content. What I’m referring to — network-driven viral growth — is much more powerful.

[Products with network effects] 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 also accomplishes the same. This is the Product/Network Duo at work again, where the product can have 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.

(From Chapter 20 of The Cold Start Problem: The Acquisition Effect — PayPal)

This is an important distinction because it’s the product-led versions of viral growth that has propelled products like Facebook, Slack, LinkedIn, Dropbox, Zoom, and others to hundreds of millions of users, or billions, of users. In fact, these the userbases for these products are so huge that it would have simply been impossible to build them through traditional marketing techniques that emphasize customer acquisition spend. It’d take tens of billions of marketing dollars to build a product with that type of scale — it’s simply not possible. Viral growth is the only way to get there, because the primary business benefit of virality is that the customer acquisition cost is zero.

Instead, the right way to think about viral growth — one of the most powerful forces in user growth — is the following provocation:

How does your product grow itself, based on its network of users to attracting their friends/colleagues?

In other words, viral growth is the ability for a product to tap into its network of users to bring in even more users. Sometimes this is via sharing, collaboration, or invitations. There are dozens of mechanisms to help drive this forward, and you can write software to optimize the virality to be more effective over time. In this way, I’ve come to believe that viral growth is actually a flavor of a network effect — it’s the ability for a product to leverage its network of users to acquire other users. (Alongside the Engagement Effect, and Economic Effect, I refer to this as the Acquisition network effect — it forms the trio of forces that underpin the broad term “network effects” that we use in the industry)

But the most mind-blowing part of this idea is that you can actually quantify viral growth — using what’s commonly referred to as viral factor.

The viral factor
We’re going to take a nerdy detour and do a little simple math, and cover the single most important metric at the heart of viral growth. In its simplest and most naive retelling, the viral factor is a single metric of how well the existing network of users in your product ends up inviting the next batch of users. For example, let’s say your new workplace chat app has 1000 active users, and over the next few months, this group invites 500 of their coworkers. This means there’s a viral factor of 0.5, which 500/1000=0.5. But after you get the 500 new users, they eventually also invite 250, then 125, and so on.

This is a powerful metric, because it adds up. Here’s how viral factor helps you multiple your userbase — a simple equation:

1/(1-v) where v=viral factor

Thus, a viral factor of 0.5 = 1/(1-v0.5) = 2x. So if you start with 1000 active users, with a viral factor of 0.5, you’ll end up with 2000 actives at the end.

With a tremendous amount of difficulty — honestly, far more than is worth it — it’s possible to directly measure the viral factor of a product and then instrument each step of the signup funnel and invite flows. This then gives you the ability to make changes to specific parts of your flow and see its effect on viral factor. Optimize a single page, and see your viral factor grow +10%. Add a prompt to share, and grow your viral factor even more.

The magical thing that can sometimes happen — for short periods — is when viral factor exceeds >1.0. This means 1000 users brings in 1100 users, which brings in 1210, and so on. (For the nerds, the equation is: users*v_factor^(1+time_period), which gives you the new users each time period, which you can then sum up in a spreadsheet). Of course, >1.0 means exponential growth. Back in the day, when asking people to invite their friends was fresh and new, you might have 20% of users invite 10 people during their onboarding flow — it meant crazy growth right off the bat.

When I first grokked the viral factor concept, it blew my mind. And if you’re like me, it makes you jump immediately to a key question:

How do you build a product with viral factor over 1.0!!???

… but funny enough, this is the wrong question to ask. It’s the tail wagging the dog.

And let me explain why.

How viral factor can nudge you towards spammy thinking
In the early social network days — think of this as around 2005-2010 — the model for increasing viral factor was simple. For any social network, you’d view it something like this:

X = % of users who invite friends
Y = # of friends they invite

X*Y = viral factor (albeit the most naive understanding of it)

At the time, Web 2.0 was just starting, and the concept of an invite was considered novel. The goal at this point was to get as many people to invite friends as possible, and to use techniques like email addressbooks to maximize the # of people you invite. And if you get these two to increase, you could dramatically increase the amount of users brought in through viral growth. A lot of innovation happened here. It became obvious that asking people to invite as early as possible in their signup flow was better, since there was less of a natural dropoff of users then. If you asked people to put in emails of their friends, they might only put in 2 or 3. But if you asked them to “Find their Friends” by connecting their addressbooks, you could sneakily ask them to email 200+ of their contacts. With enough work optimizing email invite subject lines, deliverability, and the various signup flows, you could get a viral factor over 1.0.

There was a problem with this strategy though — it pushes you towards spammy thinking. It incentivized product designers to aggressively get people to invite their friends — sometimes aggressively, leading to accidental sends of 100s of email invites. Eventually the term “dark patterns” was coined to describe these kinds of user interfaces. As users got used to them, they learned to skip prompts, ignore email invites, and response rates went down. For a short time window, this all worked, leading to spammy apps gathering millions of users overnight. But years later, very few of this generation of startups endured — we rarely talk about social networks and Facebook apps like Tagged, Hi5, BranchOut, and RockYou, who grew quickly and mastered this style of viral growth. Many of these products would also quickly collapse. In an essay I wrote over a decade ago, called Facebook viral marketing: When and why do apps “jump the shark?” I built a series of simple Excel models showing how poor retention eventually cancels out viral acquisition. And as the app saturates its market, it quickly falls back down to from the peak, thus a “shark fin.”

The predecessor to viral invites
The funny thing is that history repeats itself. If you go back 100 years ago, you actually see this entire phenomenon of highly viral mechanics followed by a quick collapse in the late 1800s, in the form of chain letters. And this story precisely tells you exactly why you don’t want to build viral loops like this. Here again is an excerpt from my upcoming book, The Cold Start Problem.

Chain letters — yes, the type you still occasionally get via email, or see on social media — have their roots in snail mail, first becoming popularized in the late 1800s. One of the most successful ones, “The Prosperity Club,” originated in Denver in the post-Depression 1930s and asked people to send a dime to a list of others who were part of the club. Of course, you would also add yourself to the list as well, as to participate. The next set of people would return the favor, sending dimes back, and so on and so forth — the promise was that it would eventually generate $1562.50. This is about $29k in 2019 dollars — not bad! The last line says it all: “Is this worth a dime to you?”

It might surprise you that in a world before email, social media, and everything digital, The Prosperity Club chain letter spread incredibly well — so well, in fact, that it reached hundreds of thousands of people within months, within Denver and beyond. There are historical anecdotes of local mail offices being overwhelmed by the sheer volume of letters, and not surprisingly, eventually the US Post Office would make chain letters like Prosperity Club illegal, to stop their spread. It clearly tapped into a post-Depression zeitgeist of the time, promising “Faith! Hope! Charity!”

However, even if chain letters have network effects, they suffer from the dynamic that they are heavily oriented around viral acquisition and lack strong retention mechanics. Ultimately the value of these networks are primarily driven by novelty and require a constant inflow of new people into the chain. Yes, in this way, it’s just like multi-level 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 mathematical limits of the expansion mean it runs out of new, novelty-seeking recipients, and as a result, existing participants stop getting paid. This in turn, causes churn, which then unravels the network entirely. A network needs retention to thrive, it can’t just continually add new users.

If you want to see an example of this chain letter, I included a screenshot of it at the top of this essay. It’s a fun historical example of something that has the same dynamics as viral invites on email and in other forms. And yes, if you did the math, you could model out the viral factor of the Prosperity Club chain letter — you just have to ask yourself, what % of people who receive this follow the instructions to mail it out to 5 new people? If it’s over 20%, then it will be viral factor >1.0 and grow exponentially.

The myth of viral factor and why retention matters
So back to the original point of this essay. If these aggressive viral loops aren’t in fact the right way to think about viral growth, what is the better approach instead?

In the aftermath of the social network wars and the eventual shutdown of the Facebook apps platform, I looked at the products that grew with viral loops and succeeded, and they shared a couple interesting traits. First, unlike my original assumption, they weren’t very aggressive about viral growth. I remember thinking through Dropbox and Slack’s viral loops, and realizing their viral hooks just felt natural, and user aligned. How was it at these products which were growing the fastest were also not aggressive? I realized they had the benefit of a secret weapon: Retention. This changed their math entirely.

To update my mental model, I realized that a modern theory of viral growth has to incorporate a number of nuances:

• Viral factor isn’t actually a single, static number. In reality, people can share and invite in sessions over time. So 100 users can invite 10 friends in the first week, then another 10 in the next, and so on. The more sessions you have with a user — meaning, higher engagement and retention — the more you can build up the viral factor over time. It can be 0.2 on the first session, then 0.3 on the 5th session. And 1.2 on the 50th session. The better the retention, the more “shots on goal” you get to ask the user to take the viral action
• Asking people to invite 100s of others isn’t realistic. There was a brief period of time where consumers were naive and might accidentally invite everyone in their email addressbook in one click. Not so, anymore. Spam scores will punish you for sending tons of random email. Users will complain when this happens. Gmail’s spam filters filter out all this “bacon.” And also, people are building mobile apps, and generally are more suspicious about sharing contacts (<20% of people shared contacts with the Uber app, at least when I was there), and they might only text an invite link to a handful of users. At one point, people tried to use mobile contacts and Twilio to deliver invite links, but that didn’t work very well — this technique fizzled. Products now grow by having users invite small batches of other users, in natural use cases
• Sharing > invites.  The advantage of a simple viral mechanism like invites is that you can generally apply it to every product. On the other hand, the problem is that consumers are tired of this mechanic. Same with “Give $10, get $10” which was novel 5 years ago, but no longer fresh. And less fresh means lower response rates. Instead, the best viral features are “native” to the product. Dropbox’s folder sharing functionality is both a really useful feature, completely consistent with the goal of the product, and also generates millions of organic, virally acquired customers for free. Same with Instagram, whose early content sharing features helped it grow without paid marketing.
• Virality amplifies other channels, rather than being a core driver. Based on the above, it’s clear that getting >1.0 viral factor is in fact unrealistic. Instead, it’s more likely to get something like 0.5, which is a 2x multiplier on everything else that’s happening in the product. Or to slowly build up to 1.0 over a long period as a result of very strong retention. But it’s most often the case that other activities — organic word of mouth, SEO, content marketing, and so on, generates a stream of users, which are then amplified by viral growth

In this new model of viral growth, instead of the simple X*Y dynamic, instead you get an equation like this:

X = % of users who engage in some viral feature (ideally content sharing, collaboration, or other user-aligned feature)
Y = # of people who are invited each time (probably a small group, a handful at a time)
Z = % of users that retain between each visit

Viral factor = X*Y*Z + X*Y*Z^2 + X*Y*Z^3 …

In other words, it’s likely that X and Y are actually very small numbers — few users inviting a small set of other users. But if retention is strong (high Z) then this term gets added up over and over again. Eventually over time, sometimes weeks or even months, the viral factor can summed into something real.

This might sound hard. And I think it is, because it requires the product to be extremely high retention. But I think it’s a more accurate reflection of reality — that in fact, the highest retention products have empirically shown to be the most viral. And when you optimize a product that’s already working well with high engagement and retention, then it is able to grow even faster. And at the same time, it matches the empirical observation that low retention products eventually die, even when they grow really fast at first.

Lessons for the next generation of social apps and product-led GTMs
The core lesson of all of this is that — for better or worse — you can’t force viral growth to happen in an era where small batches of invites are sent to small numbers of people. Virality is still a powerful force, but it has to happen through retention and natural sharing flows, not forced invites. The underlying math of viral factors is still relevant, but needs to be adjusted to think of the metric as an ongoing number that builds up over time.

And finally, the medium of viral growth has shifted over time — early on, of course, it was chain letters! Kidding, I won’t go that far. Originally, it was more centered around email invites, then there were other forms of virality like sharing content or embedding playable widgets and so on. But today, that’s all moved towards sharing on social media — how do you make apps that naturally generate shareable content as part of using it? This is particularly interesting in the world of video and other visual platforms. Similarly, B2B products are interesting because a lot of these growth ideas are still counterintuitive in an industry that is still mostly dominated by sales-driven go-to-markets. Adding viral features throughout your product will naturally allow it to expand after landing in a new account — these underpin some of the huge successes, in recent years, of products like Notion, Airtable, Figma, and so on. I think we’ll continue to see more of this here.

There was a period of time that I was convinced that you could “engineer” a viral product into existence. And from a lot of work that I did pre-Uber, at my own startups, it turns out you can. But it doesn’t mean that you can keep them. And thus, the most powerful force in the tech industry isn’t viral growth, as I once believed. It’s actually retention, and strong product/market fit. That’s the magic.

The most frequent question
One of the most frequent questions that founders ask is – I’ve read all about the importance of user growth, so now, how do I hire a Head of Growth? It’s asked often for good reason. Growing your startup’s users and revenue is so critical that it makes sense to hire someone to run it, and to potentially add a team underneath them to support this goal. It’s been a decade since “Growth Teams” became popular in the industry, sprouting up at places from Facebook and LinkedIn to Uber, Slack, Dropbox and others — and it’s natural that startups want to replicate the strategy.

However, it’s rarely easy to hire a Head of Growth. Aside from the historically tight labor market for these skills, it’s also tricky to have a simple answer to the question. “It depends” is the right place to start.

First, you have to define the correct job requirements, and what you’re looking for ultimately depends on the context of your startup — Are you trying to get your startup off the ground? Or are you trying to scale its success? Or are you focused on fending off competitors? Or growing yourself out of a saturation point. Depending on your goals, you should be looking for different things. The initial step is to define your needs.

In this essay, I’ll aim to answer this critical question — how do you hire a Head of Growth — using parts of framework from my upcoming first book, The Cold Start Problem. In the book, I describe stage-by-stage how to successfully start and scale the central forces that power tech’s most successful companies — network effects. Network effects emerge when products, like marketplaces, social apps, B2B collaboration tools, and so on, get more useful the more users that are on them.In the book, I present 5 distinct stages of a product’s lifecycle that require different goals and skillset, and understanding these stages will help you answer the more important question:

How do I hire a Head of Growth for my stage in the framework?

Let me start by spending time on each stage.

Which stage of the startup journey are you on?
I want to excerpt a passage from The Cold Start Problem that describes each of the stages, but please have some important things in mind: Not every product has network effects. After all, some are simply utilities for individual users. However, for the ones that do — and many consumer apps and consumerized workplace apps do — it’s an incredibly powerful idea that fundamentally changes the dynamic of the journey. Many startups fall in this category, and there are unique growth challenges when getting these types of products off the ground.

At this point, I’ll take a few sentences to describe “The Cold Start Problem” — one of the central ideas in the book. The idea is that for every network effects-driven product, there’s an existential problem at the beginning where users won’t find a product valuable if there’s no other users on it. Imagine a dating app with no profiles on it, or a new video app with no users and no content. Or a workplace chat app where none of your coworkers are active. These products are useless, and it’s kind of a circular problem, since to get users you need users. That’s the Cold Start Problem.

And as you might imagine, if you want to hire a Head of Growth while you’re in this first phase, that is very different than someone who is taking you from tens of millions of users to hundreds of millions of users, which is primarily the phase that I experienced at Uber.

Here’s an excerpt from The Cold Start Problem on each of the stages in the book’s central framework:

The central framework described in this book is a new way to think about network effects — split into stages, each with its own distinct challenges, goals, and best practices. My goal is to not simply to describe what happens as a network grows and evolves, but actually how to take action and propel a product from one stage to the other.

There are 5 primary stages:

1. The Cold Start Problem
2. Tipping Point
3. Escape Velocity
4. Hitting the Ceiling
5. The Moat

1. The Cold Start Problem
The framework for the book starts with an empirical insight: Most networks fail at the beginning. If a new video-sharing app launches and doesn’t have a wide selection of content early on, users won’t stick around if they can’t find what they want. The same is true for marketplaces, social networks, and all the other variations – if users don’t find who or what they want, they’ll churn, but this leads to a self-reinforcing destructive loop. In other words, it is the average case that the network effects that startups love so much actually hurt them. I call these “anti-network effects” because these dynamics are downright destructive – especially in the early stage as it’s getting off the round. Solving the Cold Start Problem requires getting all the right users and content to be on the same network at the same time — making for a difficult launch plan.

This is the Cold Start Problem, and to solve it, I look at a series of examples — examining Wikipedia’s most prolific content creators, the invention of the credit card, and how Zoom launched a killer product. From these case studies, I describe an approach that focuses on building an “Atomic Network” — that is, the smallest possible stable network that is stable and can grow on its own. For example, Zoom’s videoconferencing network can work with just two people, whereas Airbnb’s requires hundreds of active rental listings in a market to become stable. I also look at the product idea at the heart of every network effect, and the similarities many startups have used to pick the product and its features. I also ask, what are the first, most important users to get onto a nascent network, and why? And how do you seed the initial network so that it grows in the way you want?

2. Tipping Point
It takes an enormous amount of effort to build the first atomic network, but it’s obviously not enough to just have one. To win a market, it’s important to build many, many more networks to expand into the market — but how does this happen, at scale? Luckily, an important dynamic kicks in: As a network grows, each new network starts to tip faster and faster, so that the entire market is most easily captured. This is the second phase of the framework, the Tipping Point. I use Tinder as an example here, showing their initial launch at USC, but then how their success then unlocked other colleges in, then larger cities like Los Angeles, and then the broader market — including India, Europe, and other markets.

Imagine network launch as tipping over a row of dominos. Each launch makes the next set of adjacent networks easier, and easier, and easier, until the momentum becomes unstoppable — but all radiating from a small win at the very start. This is why we so often see the most successful network effects grow city-by-city, company-by-company, or campus-by-campus as rideshare, workplace apps, and social networks have done. SaaS products often grow inside of companies — landing and expanding — which also jumping between companies as employees share products with partner firms and consultants. This is when a market hits its Tipping Point.

3. Escape Velocity
When a company like Dropbox, Slack, or Uber hit scale, it might seem like network effects kick in, and the next phase is easy. But it’s not — to the contrary, this is when technology companies start to hire thousands of people, launch a series of ambitious new projects, and try to continue the product’s rapid trajectory. The Escape Velocity stage is all about working furiously to strengthen network effects and to sustain growth.

This is where the classical definition of a “network effect” is wrong. Instead, I redefine so that it’s not one singular effect, but rather, 3 distinct, underlying forces: The Acquisition Effect, which lets products tap into the network to drive low-cost, highly efficient user acquisition via viral growth. The Engagement Effect, which increases interaction between users as networks fill in. And finally, the Economic Effect, which improves monetization levels and conversion rates as the network grows.

By understanding how these forces work, we can accelerate the systems that power them. For example, the Acquisition Effect is powered by viral growth and the user experience that compels one set of users to invite their others into the network. I describe examples like PayPal and their viral referral programs, or LinkedIn’s recommendations for connecting as tactics that increase the power behind the acquisition force. 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, as well as new product features — as Uber did as it leveled users up from airport trips to dining out to daily commutes. And finally, the Economic Effect — which directly affects a product’s business model — can be improved over time as well, by increasing conversions in key monetization flows and ramping up revenue per user, as the network grows.

Stitched together, all of these combine into a flywheel that can power networks into the billions of users.

4. Hitting the Ceiling
In many narratives about network effects, by the time a product has hit the Tipping Point, that’s the fairy tale ending of the company — it’s won. Ask the operators inside a company though, and you’ll hear a different story: A rapidly growing network wants to both grow as well as tear itself apart, and there are enormous forces in both directions.

This is when a network “hits the ceiling,” and growth stalls. This is driven by a variety of forces, starting with customer acquisition costs that often spike due to market saturation, and as viral growth to slows down. Similarly, there’s the Law of Shitty Clickthroughs, which drives down the performance of acquisition and engagement loops over time, as users tune out of stale marketing channels. There’s fraudsters, overcrowding, and context collapse — all natural outcomes of a network that grows and matures. And many other negative forces that grow as the network grows.

In the real world, products tend to grow rapidly, then hit a ceiling, then as the team addresses the problems, another growth spurt emerges. Then followed by another ceiling. Then another cycle after that, each one often successively getting more complex to address over time as the problems become more fundamental.

I look at a series of case studies as major products hit periods of slowing growth: The implosion of Usenet discussion groups from the early days of the internet, eBay’s slowing US business, to the origins of Nigerian prince scams. In each of these examples, sometimes they are easily dealt with and sometimes they destroy the network over time. The solutions are difficult — a successful product inherently comes with various degrees of spam and trolls. These are problems to be managed, not fully solved.

5. The Moat
The final stage of the framework focuses on using network effects to fend off competitors, which is often the focus as the network and product matures. While it is not the only moat — brand, technology, partnerships, and others can help — it is one of the most important ones in the technology sector.

However, there’s a problem — using network effects to compete with competitors is tricky when everyone in the same product category are able to take advantage of the same dynamics. Every workplace collaboration is able to leverage network-driven viral growth, higher stickiness, and strong monetization as more users arrive. Same for marketplaces, messaging apps, and so on.

This dynamic drives a unique forms of rivalry — “network-based competition” — that isn’t just about better features or execution, but about how one product’s ecosystem might challenge another’s. Airbnb faced this problem in Europe when a strong, local competitor called Wimdu emerged with a boatload in funding, hundreds of employees, and on paper, had more traction in its home market. Airbnb had to fight off its European competitor by competing on the quality of the network, and scaling its network effects — not via traditional competitive vectors like pricing or features.

Because all products in a category likely have the same type of network effects, competition ends up being asymmetrical while leveraging the same forces. A larger network and the smaller network in any given market have distinctly different strategies — think of it as a David strategy versus a Goliath strategy. The upstart has to use its smaller size to pick off niche segments within a larger network, and build atomic networks that are highly defensible with key product features and when applicable, better economics and engagement. The incumbent strategy, on the other hand, using its larger size to drive higher monetization and value for its top users, and fast-following any niches that seem to be growing quickly. I will also examine Uber and Lyft, eBay China and Alibaba, and Microsoft’s strategy of bundling new products, to go deeper on how networks compete.

By now, hopefully each of these stages are getting your wheels turning! Obviously this excerpt hints at a lot of different ideas that I cover in the book, and you can read more expanded versions of the ideas when it’s released!

Different skills for different stages
Back to the original question on hiring a Head of Growth. Since this essay is targeting at startups, I’m going to mostly focus on the first few stages of the framework and the skills that it implies for your new hire. If you look at each stage, they roughly imply a different set of problems you are trying to face – first, finding product/market fit and building the initial “atomic network” — a stable set of users who retain. Then finding the playbook for repeatable growth, and third, scaling growth into millions of users. The first few stages are very different:

1. The Cold Start Problem = Getting a critical mass of users to prove product/market fit
2. Tipping Point = Finding a playbook to find repeatable growth
3. Escape Velocity = Scaling up growth across many efforts and channels

And if you start to think about how this maps to the types of growth projects, some stark differences emerge. I’ll look at each stage and discuss, starting from the first stage.

Context, goals, and assessment for The Cold Start Problem
In many ways, the skills needed to succeed for the first stage are the hardest to define, because it’s the most generalist and entrepreneurial:

• The Cold Start Problem
  • Goal: Getting a critical mass of users to prove product/market fit
  • Roadmap: A series of “Hustle”-driven efforts — often idiosyncratic, entrepreneurial, and surprising
  • Outcomes: A stable “atomic network” of users who retain, are engaged, and provide a launching pad for the next phase

The honest truth it, this stage is generally usually pretty random. If you read the stories of companies like Slack, Reddit, Dropbox, YouTube, Uber, and so on, you’ll realize that what worked for one company probably won’t work for you. Passing out discount codes at the train station works for a rideshare app, and a funny viral video works for YouTube, but that may not be relevant at all. So instead, the early stage usually requires a lot of elbow grease. It requires the founders to be very involved onboarding users, sometimes one-by-one, into the product. It’s usually a 100% focus on acquisition — and not much on retention at all, so it’s more hustle and less notifications/email-led.

As a result, the “Head of Growth” that makes sense here may not be one at all. Either there’s nothing to “head” — it’s such a small startup team that it’s probably an individual contributor role — or secondarily, the people heading the growth efforts here should actually be one of the founders.

That might be disheartening for a group of early founders to hear. They may want to focus on a superpower on product rather than learning a new skill on growth. Yet it’s critical to develop an intuition around go-to-market and validate hypotheses on a new product’s growth strategy. Often, the founder has to come up with a novel launch strategy, since hiring someone leads to more of the same. In today’s zeitgeist, creator partnerships for “creator economy” companies might be the cutting edge, or using NFTs as a way to attract new users. I’m just not sure that you can hire employees to do this kind of work. Sometimes the really entrepreneurial projects — in product, but also in growth too — just have to be done by the founders.

Sometimes this opinion makes founders grumble. So what if you really, really want someone to lead growth at this stage? If you really want one, I might try to find an entrepreneurial generalist and have them mostly execute a playbook that’s already worked in the past. If a team is building a new social app, I might suggest growing from high school to high school, or college to college. Or if it’s a workplace app, to use social media to drive beta users into a wait list. Things we’ve all seen work, rather than asking them to innovate totally new growth tactics. With luck, you might be able to hire someone who’s entrepreneurial and can execute those strategies. If they’re paired with advisors and operators who are experts on the playbook, that can work well. But I still like to push one of the founders to ideally drive or be the primary sponsor of the work.

What’s needed to succeed at the Tipping Point
The next stage starts once there’s a small group of users, often only a few hundred or thousand who seem to be coming back and where the network is stable. I often use benchmarks like D30 >20% or projecting out M12 to be >30% to try to assess this. But more importantly, it should be qualitative. It should feel like it’s working.

After that, it’s time to stamp out these networks in a more repeatable fashion — this is when the market begins to “tip” in your direction, and in the book, I refer to this stage as The Tipping Point.

• Tipping Point
  1. Goal: Finding a playbook to find repeatable growth
  2. Roadmap: Testing a series of more scalable growth channels, showing that if one network can be built, then multiple networks might be launched as well
  3. Outcomes: At least one scalable growth strategy. Typically organic “pull” from the market combined with a scalable channel with reasonable metrics (CAC/LTV, or viral factor, or otherwise) going into in-app acquisition funnels

In the second phase, the Tipping Point, usually the growth efforts to focus on a set expert skills to scale rather than explore. If you are working on a product in real estate that competes with Redfin, the expert skill is probably figuring out how to capture high-intent traffic — like SEO/SEM. If you are working on social apps, it’s probably viral growth. If it’s a marketplace product, then maybe referral codes and paid marketing. Being able to successfully guess a startup’s most probably growth channels allows you to then recruit for a “Head of Growth” who can supercharge the strategy. At this point, it’s important to note that the focus is still on customer acquisition, although efforts to improve engagement might also start.

The “Head of Growth” at this stage is probably someone who is a doer. They should be able to create a detailed plan for how their first 6 months might look like, and the handful of people they might hire. This is someone who knows what they want, which is made easier since the company they’re growing has product/market fit. They are hands-on, and has maybe led a small team before, but I’d prioritize tactical know how over management skills. A hard requirement is that they’ve stood up a new growth channel from scratch before. It’s important to find a hire who hasn’t just maintained the strategy of bigger companies for years and years — they must know how to instantiate it out of nothing. I like to see people who are still very entrepreneurial, is potentially less specialized on a primary channel but maybe has done a few related growth areas — like a smattering of projects that cross through product-led growth, email marketing, and paid. (And yes, I get scared if they talk too much about hiring lots of agencies, big budgets for TV and radio, or if they’re pushing back hard against metrics — there’s a time for all this, but not right now)

During the interview, I tend to look for clear spikes in the specific channel that the team thinks is the most likely to succeed. While I’ll ensure they have a strong foundation for understanding growth, I also want them to be an expert in at least one growth strategy. In fact, I usually call this out — “usually people in growth are great at one channel, and know the other channels fine but less. What’s yours?” And I’ll follow that up with detailed questions about on their superpower channel, making sure — if they claim to understand email marketing — that they really understand how to improve deliverability, all the various tools in the market, technical details like setting up DKIM/SPF, etc. Or if they are focused on paid marketing, that they understand retargeting down to the level of understanding why tracking “pixels” aren’t really pixels, how cookies actually work, how to calculate CAC/LTV when conversions happen months later, and so on.

Does this sound hard to hire for? It is! Because the labor market is so tight for people with the right skills, sometimes the only choice is to cross-train someone from an adjacent category. I often like people who are former founders, or from product management or marketing who started their careers in banking/consulting. It helps if they show a deep quantitative interest in business. Similarly, product folks from highly quantitative categories like travel, games, and marketplaces tend to do well. I want them to be spikey in their skillset, and be strong at execution, even if they need to go to advisors and peers to figure out the actual growth strategy. If they can pick it up quickly, can attract and retain talented employees for the various specializations, it can sometimes be the Plan B. I tend to lean away from people from the brand world, or from agency work, or who have only worked at larger companies. Speed of execution is just so important here.

The biggest mistake I see here happens here is when the founders try to hedge their bets. They have a slide on “growth strategy” in their investor deck with 20 different ideas, when the key is to just iterate their way into 2-3 core things that work. In the end, the largest properties on the Internet are driven off of just a few growth loops — SEO and inviting for Linkedin. Direct sales and product-led viral growth for most B2B collaboration apps. A lack of focus means that the startup is perpetually finding the next quick growth hack, rather than starting to build a system for repeatable growth.

Next is Escape Velocity
At this point, the product is ready for Escape Velocity if it’s consistently growing >3-5x year-over-year growth rate with at least one (if not two) channels that are obvious levers. One oft-used metaphor is that the product becomes a “coin operated machine” where $1 of spend into the growth loops will spit out $5. Or if you just put X engineers on Y product optimizations, then the growth rate increases by Z. This is when growth becomes predictable, repeatable, and forecastable.

And with Escape Velocity, we see the emergence of the classic “Growth Team” that the big companies have.

• Escape Velocity
  1. Goal: Scaling up growth across many efforts and channels
  2. Roadmap: Multiple growth strategies — primarily 2 or 3 — that work in concert to grow the product to millions or tens of millions of users
  3. Outcomes: Multiple scalable channels, combined with optimizing the in-app acquisition and engagement loops

The “Head of Growth” in this configuration is really more of an executive than a tactician, though domain-specific knowledge is needed. This role starts to emerge with small growth team of >10 people, in a wider company of >50-100 employees. The Head of Growth becomes more of a people manager, can set strategy and recruit people onto the team with the needed skills, but also work horizontally across teams at the company.

The complexity happens across multiple dimensions. Both acquisition and retention both become priorities. On the acquisition side, it’s not just one channel but pushing into multiple new ones while maintaining the core. By this point, there’s a few growth channels that are working, but there’s a big push towards testing and adding new channels. Or at least variations of the channels. If TikTok ads are working, then maybe try Instagram and YouTube and other visual formats. If viral invites are working, then maybe add in referral programs and more sharing prompts. So often there is a big effort to just grow the core. At the same time, there should be an effort to try a few big new channels. And then finally, there is the matter of integration — making the acquisition channels work alongside the activation/re-engagement products that happen over email and push notifications. And optimizing the product funnels and sharing flows to amplify all the marketing efforts.

To give an example, at Uber, all of these growth efforts eventually got complex — very complex. There were teams working referral programs, landing pages, adtech integrations, funnel optimizations, activation sequences over email, retention and loyalty programs, as well as all the underlying infrastructure. There were projects to help with A/B testing (Morpheus!), communication platforms to streamline notifications to users, and much more. Then on top of all of that, add in several hundred people working on international growth, customizing the product to work better in China, developing countries, and otherwise.

For most startups, getting to this scale of a growth effort is a fancy problem to have — the team might be 100+ within a larger startup numbering thousands of people. The “Head of Growth” in this case should probably just be recruited from a larger incumbent who already knows how to do this. But if this is a smaller team than that, then you can similarly downgrade and find a growth leader from inside an incumbent that’s a first line manager.

The most common pitfall
What’s the biggest mistake that founders make in hiring their Head of Growth? In my opinion, it’s when the founders are trying to abdicate responsibility for their growth efforts, and hoping that a magical hire solves everything. The problem is, a startup’s growth is so fundamental that the founders have to take it on themselves. The buck stops at the top. If the founders pick the wrong growth channel to double down, even if they hire an expert on the channel, most likely the journey will end in failure. If the founders hire some amazing growth folks but don’t give them the engineering and product support to get their loops to work, then it won’t work either. The founders need to be there to supervise the growth, because ultimately they are directly responsible.

The second biggest mistake is probably to hire the “executive” Head of Growth too soon. If you are under 100 employees, you’ll only have a half dozen or dozen people working on growth, and in reality, you need a hands-on leader. Someone who’s primarily a people manager — particularly one that starts their tenure by hiring a bunch of agencies to build and execute their plans — will lead you down a path that simply won’t be concrete enough, at a moment when that’s what the startup needs. Early startups need to move the needle each week, growing 5 or 10% week-over-week, and no amount of PowerPoint can do that. The executive is helpful when you start needing to manage 3 or more teams focused on building a repeatable system — this is probably a 20+ team in a broader team of hundreds.

In the end, the  “Head of Growth” title is probably too vague. Each of these stages requires such different skills and are judged so differently that a single title feels like it doesn’t capture the nuance. Instead, founders to incorporate the context of their startup — what stage are they in? How much conviction do they have on their growth channels? How many people are going to constitute the growth team, and thus, how much expertise versus management ability is needed? What are the most similar companies — from a growth perspective — that they can recruit from? These are the questions to ask, and by answering them, the Head of Growth role will be more clearly defined.