Intro to Metrics
Critical metrics every product manager must track by Evgeny Lazarenko
16 Startup Metrics by Jeff Jordan, Anu Hariharan, Frank Chen, and Preethi Kasireddy
16 More Startup Metrics by Anu Hariharan, Frank Chen, and Jeff Jordan
The only metric that matters by Josh Elman
Metrics Versus Experience by Julie Zhuo
Data Analysis Tips for Product Managers and Product Owners by Roman Pichler
Beat the Feature Factory: Run Pre-cap Design Studios by John Cutler
Metrics Debt by Jamie Quint
What factors influence DAU/MAU? Nature versus nurture by Andrew Chen
Retention is King by Jamie Quint
The Agony and Ecstasy of Building with Data by Julie Zhuo
Growth hacking: leading indicators of engaged users by Richard Price Growth hacking is bullshit by Ben McRedmond
Tools for Collecting and Analysing Data
Mobile-first event tracking that enables powerful insights about your users.
Tool specialized in AB-testing on mobile platforms.
Free tool for tracking traffic and flows on websites.
AB-testing on web and mobile.
The Best Books about Product Analytics
Lean Analytics: Use Data to Build a Better Startup Faster by Alistair Croll
As a product manager, your biggest risk is building something nobody wants. Lean Analytics can help. By measuring and analyzing as you grow, you can validate whether a problem is real, find the right customers, and decide what to build, how to monetize it, and how to spread the word. Focusing on the One Metric That Matters to your business right now gives you the focus you need to move ahead and the discipline to know when to change course. Packed with over 30 case studies, and based on a year of interviews with over a hundred founders and investors, the book is an invaluable, practical guide for Lean Startup practitioners everywhere.
Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle hard questions.