Product Analytics. Part 1 Intro to pirate metrics AARRR: Acquisition, Activation, Retention, Revenue, Referral

122. Intro to pirate metrics AARRR: Acquisition, Activation, Retention, Revenue, Referral

It was not a usual late Friday evening in the life of Bindle founders. It seemed like during the last 3 months none of Friday evenings was usual. A small success with marketing campaigns lead to another, users were signing up and purchasing and the most important – they’ve learned so much in terms of data.

Just a bunch of tables – users, purchases and marketing_spends provided so much insights into marketing and revenue understanding. Most of the calculated metrics were actionable and one could say “There’s life before and after Unit Economics”. 😄

Bindle founders were especially excited about today because they discovered an old blog post by Dave McClure of 500 Startups about AARRR metrics for Internet Marketing & Product Management.

  • Acquisition – how users find your product?
  • Activation – what makes user an active user?
  • Retention – how often people use your product?
  • Revenue – how do we monetize?
  • Referral – do users bring their friends?

This excitement came from the fact that this month they decided to focus on product understanding. They felt pretty comfortable with launching and monitoring marketing campaigns but there was a problem – how to explain why a campaign was successful/unsuccessful? How different users use Bindle and what happens after users signs up – how do they find a book, how they decide to purchase?

In the end there are two forces driving our business forward – efficient marketing and great product. This month we’ll focus on the latter. This is so exciting! 😝

Welcome to the chapter on Product Analytics 🍾 We’ve been through a lot by now! 💪

  1. We’ve learned how to filter data by different types of columns, that was our entry point to the world of data and SQL.
  2. We’ve learned how to group data and it allowed us to move from filters to aggregations and look at metric dynamics in time per segment.
  3. We’ve learned that data is spread among different tables and we are able to join them. It unlocked tremendous potential in working with revenues.

In this chapter we’ll continue working with all grouping techniques and joins (I mean, they’re our best friends now and there’s no way we can live without them). We’ll apply them to Product Analytics for calculating various product metrics:

  • CTR (Click Through Rate) of links or buttons
  • Funnel Analysis. Imagine you have a 4 step process to purchase a product. Funnel analysis gives you conversions and churn rates of each step. Funnel Analysis helps to identify a bottleneck (the step with the highest churn), so we can fix it aka “widen the funnel”
  • Marketing Attribution. We’ll learn how these UTM columns in the users table were calculated

In this chapter we’ll cover one more fundamental instrument of SQL – Window Functions. That’s a very advanced level and I’m glad we’re on this journey together. Let’s get it 🚀

Anatoli Makarevich, author of SQL Habit About SQL Habit

Hi, it’s Anatoli, the author of SQL Habit. 👋

SQL Habit is a course (or, as some of the students say, “business simulator”). It’s based on a story of a fictional startup called Bindle. You’ll play a role of their Data Analyst 📊 and solve real-life challenges from Business, Marketing, and Product Management.

SQL Habit course is made of bite-sized lessons (you’re looking at one atm) and exercises. They always have a real-life setting and detailed explanations. You can immediately apply everything you’ve learned at work. 🚀

“well worth the money”

Fluent in SQL in a month

Master Data Analysis with SQL with real life examples from Product Management, Marketing, Finance and more.
-- Type your query here, for example this one -- lists all records from users table: SELECT * FROM users
Loading chart... ⏳