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 (
WHEREclause) 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)
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1. Intro to performance marketingStart
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2. Filtering with OR operatorStart
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3. Counting signups from English speaking countriesStart
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4. Counting signups from DACH regionStart
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5. Counting fiction books in the libraryStart
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6. Listing distinct book categories with DISTINCTStart
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7. Counting unique valuesStart
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8. Count user signup countriesStart
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9. Filtering string values by matchingStart
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10. Matching string values regardless of caseStart
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11. First marketing campaign: counting signups from a custom domainStart
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12. Identifying email domain with SPLIT_PARTStart
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13. Count unique email domainsStart
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14. Сounting signups per country: intro to GROUP BYStart
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15. Order of SQL commands execution in a queryStart
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16. Practicing queries on virtual datasets with UNIONStart
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17. Set operators: UNION and UNION ALLStart
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18. Country with the highest number of signupsStart
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19. Calculate signups per day, per countryStart
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20. Referencing columns by numbersStart
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21. Most popular email domainStart
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22. The smallest book categoryStart
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23. Daily and hourly timelinesStart
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24. Monthly and yearly timelinesStart
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25. The month with the highest number of signupsStart
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26. Counting number users per age groupStart
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27. Biggest age group in USAStart
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28. Introduction to UTM parameters and marketing attributionStart
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29. Calculating number of signups per campaignStart
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30. Calculating number of signups from Twitter adsStart
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31. The biggest marketing campaignStart
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32. Organic vs paid signupsStart
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33. Count organic signups in a monthStart
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34. Introduction to referral programsStart
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35. Identifying power referral usersStart
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36. Best performing Twitter ad messageStart
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37. Marketing metrics: ROIStart
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38. Import of marketing dataStart
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39. Calculating marketing spend per channelStart
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40. Calculating marketing spend per campaignStart
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41. Finding the biggest marketing channelStart
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42. Calculate purchase rate per campaignStart
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43. Calculate purchase rate for organic trafficStart
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44. Marketing data and SQL recapStart
Lessons and exercises
![Anatoli Makarevich, author of SQL Habit]()
About 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 273 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. 