Customer analysis is becoming more popular, and more complicated. As a result, many companies are turning to Mixpanel to move beyond simple visitor information, and start tracking the actions their users are taking. But what’s the best way to analyze this data? That’s the subject of our latest webinar, Analyzing Mixpanel Data with SQL. Andy Granowitz from Wagon and Shaun McAvinney from RJMetrics will discuss the importance of analyzing Mixpanel data alongside data from your other data sources, the challenges in consolidating that data, and how to query that data using SQL to find valuable insights into your users' actions.

1. Analyzing Mixpanel Data
With SQL

2. ● Shaun will lead Q&A at the end of the presentation
● Tag your tweets with #mixpanelsql
● We’re recording the show
● We’ll send an email follow up to everyone with the recording & slides
● Stick around for the cupcake giveaway at the end
Details

3. How analysts can build a lean data stack that will take them from
consolidation to visualization
The quickest way to start running SQL queries on raw Mixpanel
data
What you’re going to learn
1
2
3 Examples of the rich insights you can uncover when
analyze the data about your users' interactions alongside
data from your other sources

4. Who’s presenting?
Shaun McAvinney
Lead Sales Engineer
RJMetrics
@Smcavinney
Andy Granowitz
Engineer
Wagon
@agrano

5. Intro to Wagon
Wagon is a modern
SQL editor: write SQL,
analyze data, and
share charts.

6. Intro to Pipeline

7. A quick introduction and explanation
of Mixpanel

8. Mixpanel

9. 3 Methods for Analyzing
Mixpanel Data

10. Poll question
How does your company analyze Mixpanel data today?
1. Mixpanel’s in-app reporting
2. Writing JQL queries inside the MIxpanel interface
3. Loading the data into a data warehouse for analysis
4. Other

11. 1. Mixpanel reports

12. 2. JQL queries

13. 3. The best way to do this

14. 3. The best way to do this

15. All your data in one place

16. Mixpanel + SQL

17. Connecting Wagon

18. Examples
Using Mixpanel data, we can analyze:
1. Events over time
2. Events over time grouped by event type
3. Events over time, grouped by monthly cohort
4. 30-day active users
5. 30-day active users by monthly cohort

19. Events over time

20. Events over time

21. Events over time grouped by event type

22. Events over time grouped by event type

23. Events over time, grouped by monthly cohort

24. Events over time, grouped by monthly cohort

25. 30-day active users

26. 30-day active users

27. 30-day active users by monthly cohort

28. 30-day active users by monthly cohort

29. Data Source