The Cold Start Problem by Andrew Chen: book notes

💡JC's Newsletter #102

Jan 11, 2022

Dear friends,

This week, I wanted to try a new format for the newsletter and send my curated notes on a book I have read recently: The Cold Start Problem: How to Start and Scale Network Effects by Andrew Chen.

I strongly recommend you to buy it!

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🔄 What’s a Network Effect?

A network effect describes what happens when products get more valuable as more people use them.
The questions to ask are simple:
First, does the product have a network? Does it connect people with each other, whether for commerce, collaboration, communication, or something else at the core of the experience? And second, does the ability to attract new users, or to become stickier, or to monetize, become even stronger as its network grows larger?
Networked products are fundamentally different from the typical product experience—they facilitate experiences that users have with each other, whereas traditional products emphasize how users interact with the software itself.
Building a product with network effects can both be difficult and slow.

🥶 The Cold Start Theory

There are five primary stages:
The Cold Start Problem ⚙️
Tipping Point ⚖
Escape Velocity 🚀
Hitting the Ceiling 🎩
The Moat 🏰

1. The Cold Start Problem ⚙️

Every product faces it at its inception when there are no users.
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.
If it’s not overcome quickly, a new product will die.
Solving the Cold Start Problem requires getting all the right users and content on the same network at the same time—which is difficult to execute in a launch.
Who 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?

➡️ Very important to define what is your very narrow segment of users you want to put together very early in the network.

“The Atomic Network”—the smallest network where there are enough people that everyone will stick around.
The target should be on building a tiny, atomic network—the smallest that could possibly make sense —and focus on building density, ignoring the objection of “market size.” And finally, the attitude in executing the launch should be “do whatever it takes”—even if it’s unscalable or unprofitable—to get momentum, without worrying about how to scale.
Your product’s first atomic network is probably smaller and more specific than you think. Not a massive segment of users, or a particular customer segment, or a city, but instead something tiny, maybe on the order of hundreds of people, at a specific moment in time. It was similar for Uber, in the earliest days, the focus was on narrow, ephemeral moments—more like “5pm at the Caltrain station at 5th and King St.”

➡️ I really like this concept of atomic network instead of market size.

Hard sides exist because there are tasks in any networked product that just require more work, whether that’s selling products, organizing projects, or creating content. Their expectations are higher, and competitive products to compare. As a result, their expectations are higher, and it’s difficult to engage and retain them.
What is the unique value proposition to the hard side? (And in turn, the easy side of the network.) How do they first hear about the app, and in what context? For users on the hard side, as the network grows, why will they come back more frequently and become more engaged?

➡️ Great questions!

How do you find a problem where the hard side of a network is engaged, but their needs are unaddressed? The answer is to look at hobbies and side hustles.
What people are doing on their nights and weekends represents all the underutilized time and energy in the world. If put to good use, this can become the basis of the hard side of an atomic network. Sometimes the army is built on people with excess time, but sometimes it is built on people with underutilized assets as well.

2. Tipping Point ⚖

Each launch makes the next set of adjacent networks easier, and easier, and easier, until the momentum becomes unstoppable—but it all radiates 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.

➡️ I wonder how we could land and expand more from verticals to verticals, city to city.

For many networked products that touch transactions like marketplaces, teams can just subsidize demand and spend millions to stimulate activity, whether that’s in paying content creators for your social network, or subsidizing driver earnings in rideshare. If the hard side of the network isn’t yet activated, a team can just fill in their gaps themselves, using the technique of “Flintstoning”—as Reddit did, submitting links and content until eventually adding automation and community features for scale.
In the end, all of these strategies require enormous creativity.

👉 Some ideas:

Tinder: launching a big event party on one campus where you had to download the Tinder app to get in.
Invite-only: For Gmail, LinkedIn, Facebook, and many other networked products, “invite only” has worked.
Come for the Tool, Stay for the Network: Instagram which was a free Hipstamatic with a better design. The network would come later.
Paying Up for Launch: Coupons. The goal is to get the market to hit the Tipping Point, driving toward strong positive network effects, and then pull back the subsidies.
Flintstoning: Reddit founder was answering most questions at first!
Always Be Hustlin’ (Uber): Operations, made up of the thousands of “boots on the ground” that launched new cities. They grew riders and drivers the hard way—coordinating street teams that handed out discount cards next to train stations–and reacted to the constant threats of regulation and competition.
Targeted marketing campaigns…

3. Escape Velocity 🚀

Three 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;
the Economic Effect, which improves monetization levels and conversion rates as the network grows.
The Acquisition Effect is powered by viral growth, and a positive early user experience
The Engagement Effect is done by introducing people to new use cases via incentives, marketing/communications, and new product features.
The Economic Effect can be improved over time as well, by increasing conversions in key monetization flows and ramping up revenue per user, as the network grows.

👉 Key concepts here:

Growth team at Dropbox that focused on a succession of quick wins on monetization, from optimizing the pricing page to nudges that reminded customers when they were likely to hit their storage limit. In the early days, a small design change might result in millions of dollars for the company. In parallel, the team began to explore the data, looking for critical insights that made one user more valuable than another. Not every user is the same, nor is every network the same. They made a split between High-Value Actives (HVAs) and Low-Value Actives (LVAs), which was useful as a quality indicator. I
Retention is the most critical metric in understanding a product, but most of the time, the data is not pretty.
“Nearly 1 in 4 people abandon mobile apps after only one use.”
People spend 80 percent with just three apps
As a rough benchmark for evaluating startups at Andreessen Horowitz, I often look for a minimum baseline of 60 percent retention after day 1, 30 percent after day 7, and 15 percent at day 30, where the curve eventually levels out.

➡️ Very interesting data points.

The real magic starts to happen as the viral factor starts to approach 1.
After all, at a viral factor of 0.95, 1,000 users show up and then bring 950 of their friends, who will then bring 900, and so on–ultimately the amplification will be 20x.

➡️ How can we boost our viral factor?

4. Hitting the Ceiling 🎩

“The Law of Shitty Clickthroughs.” describes how marketing channels inevitably become less effective over time—banner ads and email marketing are two good examples. If your product’s network effects depend on these channels—for example, people sending each other invites over email—growth will inevitably decline over time. The same can be found for nearly all growth channels over time.
At eBay, the strategy to overcome saturation was adding layers and layers of new revenue—like “adding layers to the cake.” Together, the aggregate business started to look like a hockey stick, but underneath it was actually many new lines of business.

➡️ On the importance of basing growth on innovation (which can be controversial because we can break the existing) and adding layers on top of layers.

5. The Moat 🏰

The upstart has 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, on the other hand, uses its larger size to drive higher monetization and value for its top users, and fast-following any niches that seem to be growing quickly.

👉 One big learning:

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.
The bottom-up motion has the advantage that once the viral growth features start to work, they are likely to continue across many different networks.
To build a massive successful network effect, you must start with a smaller, atomic network. And use the success in the first set of networks to tip over the next set of small networks.

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