Bad data, bias, misinterpretation—there are so many ways that mobile app metrics can lead you astray if you aren’t careful. Here are some posts to help you identify all the mistakes that you can (and should) avoid to make the most of your app’s analytics.

As data-driven as we try to be, all organizations are essentially and necessarily human-driven. And humans, naturally, are riddled with irrationality and biases.

You may think you have a handle on your user behavior, but unless your analytics are fully cross-platform you’re not going to be getting the full picture.

How do you explain apps that soar in popularity for a day, then tumble back down into obscurity? Data, of course.

Fear, uncertainty, and doubt (FUD) is all too common in the mobile analytics world. Here are a few things to think about when you hire your next analytics vendor.

Few businesses actually understand what it means to have a fully integrated culture of data.

The biggest challenge in understanding your analytics today isn’t the data itself or how it’s presented. It’s adjusting for bias.

Andrew Chen explains why mobile apps understand acquisition to succeed but not retention.

Avinash Kaushik on why numbers rarely tell the truth. Skepticism is every analysts’ best friend.

Great products aren’t enough writes Andrew Chen. Here’s a new way to think about launching and growing an app.