Growth hacking: leading indicators of engaged users

As a rough benchmark for evaluating startups at Andreessen Horowitz, I often look for a minimum baseline of 60 percent retention after day 1, 30 percent after day 7, and 15 percent at day 30, where the curve eventually levels out. It’s usually only the networked products that can exceed these numbers. That’s because networked products are unique in

We tracked the “funnel metrics” behaviors that were critical to our engine of growth: customer registration, the download of our application, trial, repeat usage, and purchase. To have enough data to learn, we needed just enough customers using our product to get real numbers for each behavior.

One final misconception must be addressed. Growth hacking is often characterized as being specifically about bringing in new users or customers. But in fact, growth teams are, and should be, tasked with much broader responsibilities. They should also work on customer activation, meaning making those customers more active users and buyers, and figur

The Engagement Effect manifests itself by increased engagement as the network grows—this can be developed further by conceptually moving users up

In contrast, the ones who used Dropbox for collaboration and sharing—the network features—became significantly more valuable over time. Dropbox’s users could be divided into High-Value Actives (HVAs) and Low-Value Actives (LVAs),