Andrew Chen > Quotes

“Flinstoning” is a metaphor for this car, except in software, where missing product functionality is replaced with manual human effort.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“acquiring the hard side of the network and keeping them happy is paramount to standing up an atomic network.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Once Hopkins showed that this worked in creating one atomic network, the effort could be repeated in building the second, third, and so on: We proved out this plan in several cities of moderate size. Then we undertook New York City. There the market was dominated by a rival brand. Van Camp had slight distribution. In three weeks we secured, largely by letter, 97 per cent distribution. Every grocer saw the necessity of being prepared for that coupon demand. Then one Sunday in a page ad, we inserted the coupon. This just in Greater New York. As a result of that ad, 1,460,000 coupons were presented. We paid $146,000 to the grocers to redeem them. But 1,460,000 homes were trying Van Camp’s Milk after reading our story, and all in a single day. The total cost of that enterprise, including the advertising, was $175,000, mostly spent in redeeming those coupons. In less than nine months that cost came back with a profit. We captured the New York market.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Launching “Buy It Now” was a large change that touched every transaction, but the eBay team also innovated across the experience for both sellers and buyers as well. With an initial success, we doubled down on innovation to drive growth. We introduced stores on eBay, which dramatically increased the amount of product offered for sale on the platform. We expanded the menu of optional features that sellers could purchase to better highlight their listings on the site. We improved the post-transaction experience on ebay.com by significantly improving the “checkout” flow, including the eventual seamless integration of PayPal on the eBay site. Each of these innovations supported the growth of the business and helped to keep that gravity at bay. Years later, Jeff became a general partner at Andreessen Horowitz, where he would kick off the firm’s success in startups with network effects, investing in Airbnb, Instacart, Pinterest, and others. I’m lucky to work with him! He recounted in an essay on the a16z blog that his strategy was to grow eBay by adding layers and layers of new revenue—like “adding layers to the cake.” You can see it visually here: Figure 12: eBay’s growth layer cake As the core US business began to look more like a line than a hockey stick, international and payments were layered on top. Together, the aggregate business started to look like a hockey stick, but underneath it was actually many new lines of business.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“This is an important tool that is unique to networked products. Traditional products that lack networks often struggle with this, because they rely on spammy emails, discounts, and push notifications to entice users back. This usually doesn’t work, and company-sent communications rank among the lowest clickthrough rate messages. Networked products, on the other hand, have the unique capability to reactivate these users by enlisting active users to bring them back. Even if you don’t open the app on a given day, other users in the network may interact with you—commenting or liking your past content, or sending you a message. Getting an email notification that says your boss just shared a folder with you is a lot more compelling than a marketing message. A notification that a close friend just joined an app you tried a month ago is a lot more engaging than an announcement about new features. And the more dense the network is around a churned user, the more likely they are to receive this type of interaction.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Success comes with an inevitable problem: market saturation. New products initially grow just by adding more customers—to grow a network, add more nodes. Eventually this stops working because nearly everyone in the target market has joined the network, and there are not enough potential customers left. From here, the focus has to shift from adding new customers to layering on more services and revenue opportunities with existing ones. eBay had this problem in its early years, and had to figure its way out. My colleague at a16z, Jeff Jordan, experienced this himself, and would often write and speak about his first month as the general manager of eBay’s US business. It was in 2000, and for the first time ever, eBay’s US business failed to grow on a month-over-month basis. This was critical for eBay because nearly all the revenue and profit for the company came from the US unit—without growth in the United States, the entire business would stagnate. Something had to be done quickly. It’s tempting to just optimize the core business. After all, increasing a big revenue base even a little bit often looks more appealing than starting at zero. Bolder bets are risky. Yet because of the dynamics of market saturation, a product’s growth tends to slow down and not speed up. There’s no way around maintaining a high growth rate besides continuing to innovate. Jeff shared what the team did to find the next phase of growth for the company: eBay.com at the time enabled the community to buy and sell solely through online auctions. But auctions intimidated many prospective users who expressed preference for the ease and simplicity of fixed price formats. Interestingly, our research suggested that our online auction users were biased towards men, who relished the competitive aspect of the auction. So the first major innovation we pursued was to implement the (revolutionary!) concept of offering items for a fixed price on ebay.com, which we termed “buy-it-now.” Buy-it-now was surprisingly controversial to many in both the eBay community and in eBay headquarters. But we swallowed hard, took the risk and launched the feature . . . and it paid off big. These days, the buy-it-now format represents over $40 billion of annual Gross Merchandise Volume for eBay, 62% of their total.65”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“The other question to ask is, if a user wants to reactivate, how hard is it? At Uber, we had a staggering statistic where several million users were failing their password recovery per week—how do you make this much easier, and treat reactivation with the same seriousness as the sign-up process? While reactivation is typically not a concern for new products—they should focus on new users, since their count of lapsed users won’t be large—for products that have hit Escape Velocity, there will be a pool of many millions of users to draw upon. Reengaging them can become as big a growth lever as acquiring new users.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“To learn why bundling sometimes works, and other times doesn’t, I went to the source. I asked Brad Silverberg, who in his decade at Microsoft headed up some of the company’s most important product efforts—including the much-celebrated release of Windows 95, accelerating the franchise from $50 million to $3.5 billion, as well as all the early releases of Internet Explorer. He’s been a mentor of mine for years, having served on the board of a startup I founded years back. I interviewed Brad for The Cold Start Problem over videoconference; he was mostly retired and spending time with family in Jackson Hole, Wyoming. But his experience from the 1980s and ’90s has made him the definitive authority on this topic, and perhaps surprisingly, he’s skeptical of the power of bundling: Bundling a product is not the silver bullet everyone thinks. If it were that easy, the version 1.0 for Internet Explorer would have won, by simply bundling it with Windows. It didn’t—IE 1.0 only got to 3% or 4% market share, because it just wasn’t good enough yet. Bing is another example, when Microsoft wanted to get into search. It was the default search engine across the operating system, not just in Internet Explorer but also MSN and everywhere Microsoft could jam it. But it went nowhere. The distribution advantages don’t win when the product is inferior.91 Even if bundling gets you a lot of new users trying out a product, they won’t stick around if there’s a huge gap in features.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“The Instagram versus Hipstamatic story is perhaps the canonical example of a strategy made famous by Chris Dixon’s 2015 essay “Come for the tool, stay for the network.” Chris writes: A popular strategy for bootstrapping networks is what I like to call “come for the tool, stay for the network.” The idea is to initially attract users with a single-player tool and then, over time, get them to participate in a network. The tool helps get to initial critical mass. The network creates the long term value for users, and defensibility for the company.40 There are many other examples across many sectors beyond photo apps: The Google Suite provides stand-alone tools for people to create documents, spreadsheets, and presentations, but also network features around collaborative editing, and comments. Games like Minecraft or even classics like Street Fighter can be played in single-player mode where you play against the computer, or multiplayer mode where you play with friends. Yelp started out effectively as a directory tool for people to look up local businesses, showing addresses and phone numbers, but the network eventually built out the database of photos and reviews. LinkedIn started as a tool to put your resume online, but encouraged you to build up your professional network over time. “Come for the tool, stay for the network” circumvents the Cold Start Problem and makes it easier to launch into an entire network—with PR, paid marketing, influencers, sales, or any number of tried-and-true channels. It minimizes the size requirement of an atomic network and in turn makes it easy to take on an entire network. Whether it’s photo-sharing apps or restaurant directories, in the framework of the Cold Start Theory, this strategy can be visualized. In effect, a tool can be used to “prop up” the value of the network effects curve when the network is small.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“One notable example of this is the ever present “People You May Know” or “Friend suggestions” feature. Every social platform at scale has some kind of implementation of it for a reason: it works incredibly well. My friend Aatif Awan, formerly vice president of growth at LinkedIn—who helped them scale to hundreds of millions of users and spearheaded their acquisition by Microsoft—explains how their algorithm works: People You May Know was a key part of LinkedIn’s success, generating billions of connections within the network. It started with “completing the triangle”—if a bunch of your friends have all connected with Alice but you haven’t yet, then there’s a good chance you might know Alice, too. Later, we incorporated implicit signals—maybe Alice just updated her profile to say she works at your same company. Maybe she’s viewed your profile multiple times over several days. Putting all of these inputs into a machine learning model continued to give us mileage on this feature over many years.77”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Another product, Couchsurfing, already existed as well, and was an indirect competitor, albeit a peculiar one. Founded in 2003 as a nonprofit, Couchsurfing allowed for people to crash on each other’s sofa while traveling but did not require payment. Instead the focus was on community and letting members guide each other around a new town. (The result was occasional romantic advances, both wanted and unwanted, in the absence of economic clarity and motivations.)”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Humans are the networked species. Networks allow us to cooperate when we would otherwise go it alone. And networks allocate the fruits of our cooperation. Money is a network. Religion is a network. A corporation is a network. Roads are a network. Electricity is a network.6”
― Andrew Chen, The Cold Start Problem: Using Network Effects to Scale Your Product

“On September 18, the bank mailed 60,000 Fresno residents a BankAmericard. There was no application process. The card simply arrived in the mailbox, ready to use. Credit card fees for merchants were set at 6 percent and consumers received between $300 and $500 in instant credit. There was a certain brilliance behind the 60,000-person drop: On day 1, cardholders simply existed. This permitted Bank of America to sign up all merchants who didn’t already have proprietary credit card programs. BofA focused on fast-moving, small merchants like Florsheim Shoes, not giants like Sears. More than 300 Fresno merchants signed up.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“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. When companies don’t understand these nuances, it leads to disaster.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Switching over Entire Networks Part of why cherry picking can be dangerous for the incumbent is that the upstart networks can reach over and directly acquire an entire set of users who have been conveniently aggregated on your network. It’s just software, after all, and users can spread competitors within an incumbent’s network by using all the convenient communication and social tools. Airbnb is again an example of this. The company not only unbundled Craigslist and turned the shared rooms idea into an entire product, but they actually used Craiglist users to advertise Airbnb to other users. How? Early on, Airbnb added functionality so that when a host was done setting up their listing, they could publish it to Craigslist, with photos, details, and an “Interested? Got a question? Contact me here” link that drove Craigslist users back to Airbnb. These features were accomplished not by using APIs provided by Craigslist, but by reverse-engineering the platform and creating a bot to do it automatically—clever! I first wrote about this in 2012 on my blog, in a post titled “Growth Hacker is the new VP Marketing” with this example in mind. By the time Craigslist decided it didn’t like this functionality and disabled it, months had passed and Airbnb had formed its atomic network. The same thing happened in the early days of social networks, when Facebook, LinkedIn, Skype, and others grew on the back of email contacts importing from Hotmail, Yahoo Mail, and other mail clients. They used libraries like Octazen—later acquired by Facebook—to scrape contacts, helping the social networks grow and connect their users. At the time, these new social networks didn’t look like direct threats to email. They were operating within niche parts of messaging overall, focused on college and professional networks. It took several years for the email providers to shut down access after recognizing their importance. When an incumbent has its network cherry-picked, it’s extra painful along two dimensions: First, any network that is lost is unlikely to be regained, as anti-network effects kick back in. And second, the decline in market share hits doubly hard, which has implications for being able to raise money.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Fred Wilson at Union Square Ventures,”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“New users were asked to import their email contacts to invite more people. After each connection request, users were shown screens of even more suggestions. New users who appeared in other people’s contacts—even if they skipped importing it themselves—had suggested connections right after sign-up.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Within a year, it was clear that YouTube’s growth in videos, comments, channels, and profiles rapidly exceeded the team’s expectations. Its rise was rapid, and blew through every milestone that the team set in its first year. Initially, they tried to get to 1,000 views per day. Then 10,000 views/day, and when they hit that, 100,000 views/day. In less than a year, YouTube hit 1 million views per day—the start of a massive growth trajectory. The YouTube team rolled out solution after solution to solve overcrowding, but focused on the simple ones first—displaying a list of recently uploaded videos, followed by a popularity-based sort, and eventually country segmentation. The evolution of YouTube’s solution to overcrowding evolved from manual curation to popularity rankings to algorithmic methods. This is a necessary transition that every networked product has to make to solve the overcrowding problem.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Chain letters—yes, the type you still occasionally get via email, or see on social media—have their roots in snail mail, first 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 add yourself to the list as well. The next set of people would return the favor, sending dimes back, and so on and so forth—with the promise that it would eventually generate $1,562.50. This is about $29,000 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 Depression zeitgeist of the time, promising “Faith! Hope! Charity!” This is a clever, viral idea (for its time), and I will also argue that this is an analog version of a network effect from the 1800s, just as telephones and railways were, too. How so? First, chain letters are organized as a network, and can be represented by the list of names that are copied and recopied by each participant. These names are likely to be friends, family, and people in the community, furthering the Prosperity Club’s credibility, thereby increasing the engagement level. It follows the classic definition of network effects: the more people who are participating in this chain letter, the better, since you are then more likely to receive dimes. And it even faces the Cold Start Problem: if enough people aren’t already on the list and playing along, then it will fail to grow.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Cofounder Nate Blecharczyk is highly quantitative and had determined that 300 listings, with 100 reviewed listings, was the magic number to see growth take off in a market.11”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage. The products or services that have wide, sustainable moats around them are the ones that deliver rewards to investors.84 Because Buffett generally invests in low-tech companies like See’s Candies or Coca-Cola, the moat he refers to is often a strong brand or a unique business model. For software products with network effects, a strong moat means something different: how much effort, time, and capital does it take to replicate a product’s features and its network? In the modern era, cloning software features is usually not the hard part—replicating the complete functionality of a Slack or Airbnb might take time, but it is tractable. It’s the difficulty of cloning their network that makes these types of products highly defensible. I’ll use an example to think through the competitive moat. Let’s start from first principles, with an example of Airbnb trying to launch in a new city with no competitors in sight. As the early Airbnb team described, the Cold Start Problem lies in the difficulty of launching a new city to a Tipping Point of over 300 listings with 100 reviews. This requires real effort, because the minimum network size is quite large—contrasted to many other network types like communication apps, which might only require two or three people to get started. But once Airbnb has reached Escape Velocity in a market, the Cold Start Problem creates the defense against new entrants.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Recently, the opposite trend has emerged—products like Uber and email company Superhuman, have started at the top of the market as a luxury product, and worked their way down.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“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.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Stewart and Ali Rayl, who ran customer experience, would personally handle all the feedback on social media and customer support tickets. Even once Slack publicly launched, Stewart personally handled the lion’s share of 10,000 tweets per month and 8,000 customer support tickets.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Up-front investment to try to professionalize the supply side early on in a network’s development inevitably comes with risk. In a well-publicized misstep for Uber, the company sought to expand its supply side by financing vehicles to provide cars to potential drivers who didn’t own vehicles, a program called XChange Leasing. The hypothesis was that this should push these drivers into power-driver territory quickly. Payments could be automatically deducted from their Uber earnings, and their driver ratings and trip data could be used to underwrite the loans. XChange Leasing unfortunately lost $525 million and failed to professionalize the driver side of the market. The problem was, it attracted drivers highly motivated by money—usually a positive—but who didn’t have high credit scores for good reason. They often failed to make payments, using their Uber-provided car to drive for competitors and avoid the automatic deductions. They would steal the cars and sell them for, say, half price. They would drive for Lyft instead of Uber, as a way to avoid the automatic payment deductions—they would try to have their cake and eat it, too. Uber needed to organize a massive repossession effort to get the cars back, but it was too late—many had been sold illegally, some finding themselves as far away as Iraq and Afghanistan, GPS devices still attached and running. This is a colorful example of how scaling the supply side, when a lot of capital is involved, can be tricky.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“When examined through the lens of Meerkat’s Law and the central framework of this book, it is obvious why the resulting networks generated by big launches are weak. You’d rather have a smaller set of atomic networks that are denser and more engaged than a large number of networks that aren’t there. When a networked product depends on having other people in order to be useful, it’s better to ignore the top-line aggregate numbers. Instead, the quality of the traction can only be seen when you zoom all the way into the perspective of an individual user within the network. Does a new person who joins the product see value based on how many other users are already on it? You might as well ignore the aggregate numbers, and in particular the spike of users that a new product might see in its first days. As Eric Ries describes in his book The Lean Startup, these are “vanity metrics.” The numbers might make you feel good, especially when they are going up, but it doesn’t matter if you have a hundred million users if they are churning out at a high rate, due to a lack of other users engaging. When networks are built bottom-up, they are more likely to be densely interconnected, and thus healthier and more engaged. There are multiple reasons for this: A new product is often incubated within a subcommunity, whether that’s a college campus, San Francisco techies, gamers, or freelancers—as recent tech successes have shown. It will grow within this group before spreading into other verticals, allowing time for its developers to tune features like inviting or sharing, while honing the core value proposition. Once a new networked product is spreading via word of mouth, then each user is likely to know at least one other user already on the network. By the time it reaches the broader consciousness, it will be seen as a phenomenon, and top-down efforts can always be added on to scale a network that’s already big and engaged. If Big Bang Launches work so poorly in general, why do they work for Apple? This type of launch works for Apple because their core offerings can stand alone as premium, high-utility products that generally don’t need to construct new networks to function. At most, they tap into existing networks like email and SMS. Famously, Apple has not succeeded with social offerings like the now-defunct Game Center and Ping. The closest new networked product they’ve launched is arguably the App Store, but even that was initially not in Steve Jobs’s vision for the phone.87 Most important, though, you aren’t Apple. So don’t try to copy them without having their kinds of products.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“The term “network effect” has almost become a cliché. It’s a punch line 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.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“It may surprise you to know that all of Wikipedia—with more than 55 million articles—was written by a small group of users. Not just small, actually, but tiny. Even though there are hundreds of millions of users, there are only about 100,000 active contributors per month, and when you look at the small group of writers who make more than 100+ edits in a month, it’s about 4,000 people. As a ratio, it means that active contributors represent only 0.02% of the total viewer pool.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“Overcrowding works in a different way for creators than for viewers. For creators, the problem becomes—how do you stand out? How do you get your videos watched? This is particularly acute for new creators, who face a “rich get richer” phenomenon. Across many categories of networked products, when early users join a network and start producing value, algorithms naturally reward them—and this is a good thing. When they do a good job, perhaps they earn five-star ratings, or they quickly gain lots of followers. Perhaps they get featured, or are ranked highly in popularity lists. This helps consumers find what they want, quickly, but the downside is that the already popular just get more popular. Eventually, the problem becomes, how does a new member of the network break in? If everyone else has millions of followers, or thousands of five-star reviews, it can be hard. Eugene Wei, former CTO of Hulu and noted product thinker, writes about the “Old Money” in the context of social networks, arguing that established networks are harder for new users to break into: Some networks reward those who gain a lot of followers early on with so much added exposure that they continue to gain more followers than other users, regardless of whether they’ve earned it through the quality of their posts. One hypothesis on why social networks tend to lose heat at scale is that this type of old money can’t be cleared out, and new money loses the incentive to play the game. It’s not that the existence of old money or old social capital dooms a social network to inevitable stagnation, but a social network should continue to prioritize distribution for the best content, whatever the definition of quality, regardless of the vintage of user producing it. 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.75 This is true for social networks and also true for marketplaces, app stores, and other networked products as well. Ratings systems, reviews, followers, advertising systems all reinforce this, giving the most established members of a network dominance over everyone else. High-quality users hogging all of the attention is the good version of the problem, but the bad version is much more problematic: What happens, particularly for social products, when the most controversial and opinionated users are rewarded with positive feedback loops? Or when purveyors of low-quality apps in a developer platform—like the Apple AppStore’s initial proliferation of fart apps—are downloaded by users and ranked highly in charts? Ultimately, these loops need to be broken; otherwise your network may go in a direction you don’t want.”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects

“What if . . . we did a $750/$750 referral bonus here in SF, LA, and San Diego?”
― Andrew Chen, The Cold Start Problem: How to Start and Scale Network Effects