Cohort Analysis: What it is and Why it's Friggin' Awesome!

To learn how to create a cohort analysis using SQL and much more check out our SQL for Newbs on-demand online course here: www.udemy.com/sql-for-newbs/?...
A cohort analysis is one of the most powerful ways to look at the strength of your company.
As a data analyst you are able to break down your users by their signup time and then track their performance over time! This video walks you through the benefits and impact of the cohort chart.

[@KashKoncepts]

After watching this video, I'm very clear on what a cohort analysis is and how it benefits the business. Thank you!

[@danielpinzon706]

very helpful video, thank you ! wish you guys had continued making more videos

[@mcarthuradal8613]

Only one video? You guys are great

[@slejap1369]

Short and sweet and very useful intro. Thank you guys !!

[@davishama]

Thank you so much guys. Love from Brazil.

[@salty1234567890]

Best video on youtube. Will show this video to my class too. Thank you!!

[@youngzproduction7498]

Cool, crisp and casual explanation. This is what I found out after your vid

[@kavokolak]

Awesome video! Helped a lot to understand more about the subject

[@blairbeauchemin]

Omg, this is great stuff! I wish you guys would do a full course so I could learn more!

[@yogash69]

Enlightening Explanation about cohort. Thanks! :)

[@pavloseimskyi2413]

Well done. But where is the second video?

[@tomerhelzer]

Great explanation of what a cohort analysis is! Love your enthusiasm and humor, keep it up!

[@ThuHuongHaThi]

It's extremely easy to understand and it helped a lot, thank you

[@BM-fd6jv]

Great video guys, really helpful. Thanks👍🏾

[@dikshag7087]

This is a great video. Super helpful! Thank you guys for making this!

[@redsox00013]

You are welcome! Really glad you enjoyed it.

[@Blablabla-rq3to]

Awesome presentation!

[@freeaudiobooks2659]

Truly amazing explanation. Keep it up guys.

[@asthatripathi9211]

wow you two rocks..like your fun style do you have more videos??

[@CrispinValdezOfficial]

Great explanations!

[@kajalchhetri1781]

Loved the explanation

[@alithu93]

THANK YOU!

[@anandshubham100]

Good job!

[@moonspirittv6807]

good explanation

[@AkramAlodini]

great

[@CaribouDataScience]

Of course the word cohort refers to a part of a Roman Legion. A cohort generally consisted of around 480 soldiers.

[@seekluv]

What is the general umbrella topic or subject that this concept fits under?

[@CooperCarr]

I think your mic is backwards.

[@arthurmaldonado2534]

Amazing video,thank you. Im a follower now , feed a bit more and your CTR on Udemy will raise. Maybe a second video ? ;)

[@sirThomas95]

HI I don't get why this squares getting smaller. On 1st week we have filled data to the bottom and every next week is 1 square less. Could you explain this ? Btw Great video it is not boring, it seems dynamic and I understand much more than from other videos :D

[@cjkyle0123456]

+Michal Tom That's because we have more month over month data for the cohort that joined our service the earliest i.e. in January in this case. The February cohort was not active in January, March cohort was not active in February and so on. Hence you see that each subsequent month will have 1 less square (i.e. data) than the previous month.

[@sud-sud]

+Hipokryta Tom I'm assuming you're talking about how in September 2010, for example, there's data until 12 months in that particular row, but then every subsequent month, it decreases by one month. If that's your question, I guess that's because users who signed up in a later month (say, march 2010) haven't been around as long as those who joined earlier (september 2010). The "User signed in by months" field basically shows how much percentage of users who signed up in that particular month sign in after a certain amount of months. So the data in the 6th column for september 2010 (3.7%) means that 3.7% of people who signed up in september 2010 still signed in 6 months later. Let's suppose that this analysis was done in september 2011. When we're looking at data for september 2010, we have data for the past 12 months, because september 2010 was 12 months ago, and you can see how many people from that "cohort" have continued to sign in each month. But if you were to look at October 2010, that was only 11 months ago, so you can see how many people who signed up then for the past 11 months. For each subsequent month, the amount of months the users have been "retained" (been around) will decrease by 1. Hope this pointlessly long comment helped :)

[@spongebobby188]

You're making stats sexy. Btw u two have good chemistry!

[@raj-dsa]

You create a channel and post 2 videos, and one of them gets 46K views?! Where are the other videos?