Chapter 3: Running marketing

Welcome to the chapter “Running Marketing”. 👋

Let’s look at both SQL theory of this chapter and all practical applications of Data Analysis you’ll learn.

Before we start, I just want to clarify one thing. Yep, this chapter will cover a lot of marketing specifics – what data do we need to run marketing analysis, how to analyze marketing funnels, campaign performance, etc.

I know that even if you don’t work directly with marketing data, this chapter might seem like a waste of time. Believe me, it is not. I specifically chose the first heavy chapter to cover marketing because:

  • 90% of the time marketing analysis is about timelines, funnels and cohorts. These are the pillars of Data Analysis as a discipline. You can use them to analyze anything.
  • marketing analysis is simple (not easy): people see ads, click ads, land on the website, create accounts, use your product and pay. This is overly simplified, of course, but I believe that this funnel is a perfect starting point for learning Data Analysis.
  • we can do all that only by learning how to use GROUP BY (aggregate functions). It’s an easy incremental step for everything you’ve learned in the course so far.

Lastly, it’s just 44 bite-sized lessons and exercises. After that, it’s only a couple of chapters to finish the main part of the course and unlock the certificate. Let’s lock in! 🚀

SQL theory

I called this chapter “the first heavy chapter”, because there’re only a few hard concepts in SQL – aggregations, joins and window functions. It’s a very short checklist.

Apart of aggregate functions (that’s a precise term for “aggregations”), you’ll learn:

  • how to select unique values. That’s a must, because if you’re counting signups or purchases – you want to count every user or purchase once. Such mistakes are called “overreporting” and I’ve seen 7 digit marketing damage it may cause. 💰 💣
  • how to combine different filters. You’ve learned filters (WHERE clause) in the previous chapter, here we’ll level it up and learn how to combine filters.
  • how to build timelines – hourly, daily, monthly reports. 📊
  • how to combine datasets via UNION
  • a couple of useful functions for working with text (SPLIT_PART) and dates (DATE_TRUNC)

Practical Data Analysis knowledge

There’re 20 exercises to practice all that. ☝

You’ll learn:

  • how to analyze cohort sizes (users by country, books by category, etc)
  • how to extract useful insights from user emails 💡
  • how to build age group distribution
  • how marketing data is structured in a data warehouse
  • how to analyze referral program
  • how to analyze marketing campaigns (traffic, signups)
  • how to calculate purchase rate per campaign

As I mentioned before, this is a solid foundation every Data Analyst should have – fluently working with cohorts and funnels, analyzing aggregated metrics and dynamic of a metric over time. After this chapter, you’ll be able to put all these skills in your CV! 🚀 (maybe wait with this one until you get a certificate haha)

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 13 chapters (you’re looking at one atm) that contain 271 bite-sized lessons and exercises. All of them have a real-life setting and detailed explanations. You can immediately apply everything you’ve learned at work. 🚀

The 2nd part of the course is called Practice. It’s made of standalone exercises based on multiple datasets – E-commerce, Finance and Meditation app a-la Headspace or Calm. Practice exercises are harder than in the main course. They’ll get you ready for any challenge at work or an interview. 💪

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