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.

When YouTube got to millions of videos, it got hard to find what you wanted to watch.” Yes, this is a fancy problem for a networked product but it’s exactly the problem that YouTube faced as it grew.

When you do the math, the result is startling: for a 50 percent decrease in conversion of invites, there’s an 80 percent decrease in total new users.

“The Engagement Effect” is what happens when a product gets stickier, and more engaging, as more users join. This is the closest to the classic definition of the network effect, as defined by Theodore Vail of AT&T.

Tool, network Create + share with others (Instagram, YouTube, G Suite, LinkedIn) Organize + collaborate with others (Pinterest, Asana, Dropbox) System of record + keep up to date with others (OpenTable, GitHub) Look up + contribute with others (Zillow, Glassdoor, Yelp)

Although this entire phenomenon is often called market saturation, in a networked product there’s actually something more subtle going on. I think of it as network saturation, not just market saturation. Here’s how I define this term: the 100th connection for any given participant is likely less impactful than the first few, and as the network gets

Every new product eventually has to achieve, and then sustain, Escape Velocity. In the next few chapters, I talk about this middle phase, where the hard work is focused on scaling growth.

Launching a product like Tinder is hard. It requires you to simultaneously attract multiple segments of users, at the right proportions, at the same time.

Many of Microsoft’s security issues, instability, and less elegant interfaces can be traced directly back to the decision to focus on the needs of developers, particularly enterprise customers who made big investments