The document discusses cohort analysis in Google Sheets, emphasizing its importance over vanity metrics like total sign-ups. It outlines a simple five-step process for conducting cohort analysis, including data structuring and using the =sumifs formula. The guide also provides tips on visualizing data and suggests involving a developer for more complex analyses.

1. REALLY
SIMPLE
COHORT
ANALYSIS*
in Google Sheets
Hugh Hopkins (@hughhopkins) + GoSquared

2. “Total Sign Ups” is
just a vanity metric.
It doesn’t help you
understand how your
business is
performing.
To really understand
how your business is
performing you need
to use cohorts.

3. So, what is Cohort Analysis?
Cohort analysis allows you to study a group of people/users who share the
same characteristic, i.e. the same sign up week, and see how they perform
over time.
This enables businesses to compare the effectiveness of a marketing
campaign, a new feature or the impact of increasing prices. Being able to do
this quickly for almost any metric can transform your business.
The cohort graph on the left
shows the number of users
who convert to a premium
plan. Within in seconds you
can see July saw a really low
number of conversions
whilst October saw the
highest.

4. But, calculating cohort
metrics can be really
complicated.

5. First, there are numerous ways of structuring the table.
https://blog.kissmetrics.com/wp-content/uploads/2012/10/cohort-report-1-sm.png
http://cdn.insideintercom.io/wp-content/uploads/2011/11/Cohort3-Full.jpg
http://www.saasoptics.com/SaaS-opedia/saas-opedia/MRR-Cohort/files/BIGmrr_cohort_subscription_metrics.png.png

6. Then there are even more ways of visualising it!
http://s3.amazonaws.com/custora-assets/assets/cohort_graph_B4.png
http://upload.wikimedia.org/wikipedia/commons/thumb/b/b4/Enwp-jan-cohorts-last-12-months--10-1-.png/600px-Enwp-jan-cohorts-last-12-months--10-1-.png
http://insideintercom.io/wp-content/uploads/2011/11/Cohort5-CyclePlot-Dull1.jpg
http://blog.recurly.com/wp-content/uploads/2013/07/Subscriber-Retention-by-Cohort.png
http://upload.wikimedia.org/wikipedia/commons/thumb/4/42/Megabytes_added_to_EN_NS4,5.png/650px-Megabytes_added_to_EN_NS4,5.png
http://blog.mixpanel.com/wp-content/uploads/2009/02/media_httpblogmixpanelcomwpcontentuploads200902picture2png_DDwiEorhikJvqgf.png.scaled1000.png
!

9. The latest version of Google
Sheets added the =SUMIFS
formula making it easy* for
anyone to quickly create
meaningful cohort analysis.

10. 5 Simple Steps
1. Enable the new Google Sheets at http://g.co/newsheets
2. Import your data.
3. Structure the data.
4. Create the formula and table.
5. Stand back in amazement with what you have just created.
Here’s one we made earlier…
You can find a working example of the Cohort Analysis here:
Google Sheets Working Example

11. Structuring The Data - Part 1
Regardless of your data format, you should have at least two columns
with dates in.
• The ‘Sign Up Date’ of that user (the bit which ties the user to a cohort)
• The ‘Event Date’ (i.e. upgraded to a premium subscription)
It will look something like this:
`User ID
Sign Up Date
Event Date
1 12/23/2013 17:53:30
12/23/2013 18:05:30
2 12/29/2013 0:30:05
1/12/2014 18:34:46
3 12/30/2013 19:22:23
1/3/2014 22:14:40

12. Structuring The Data - Part 2
Convert the date format to UNIX to avoid any potential formatting
errors. To do this, simply create another column and use the following
formula: =(A2-“1/1/1970”)*60*60*24. To convert UNIX to a normal
timestamp use: =A2/(60*60*24)+”1/1/1970 00:00:00.
Now all you need to do is set up the formula to compare the two dates
and return the number of users that match the search criteria.
`User ID
Sign Up Date
1
12/23/2013
17:53:30
Event Date
12/23/2013
18:05:30
2 12/29/2013 0:30:05 1/12/2014 18:34:46
3
12/30/2013
19:22:23
1/3/2014 22:14:40
Sign Up Date (UNIX)
Event Date (UNIX)
1387821210
1387821930
1388277005
1389551686
1388431343
1388787280
Working Example

13. Creating The Table - Part 1
The way in which you build your table will alter the final graph. A simple
set up in Google Sheets is to have the rows as the cohort (i.e. the user
segmented by the sign up date) and the column with the time period (i.e.
what happened to that cohort in that particular month).
Deciding to structure it in chronological order or pinning it to the same
starting point will alter the shape of your graph. Ultimately it depends on
what you are trying to visualise and toggling between views will help.
The Individual cohorts
Time Period (what
that cohort did in
that month)

14. Creating The Table - Part 2
For example:
1370044801
1370044801
July
1375304287
August
1375304287
June
1372674544
1372674544
14
26
In this cell, I want to find the number of users who “Signed Up” during the
period 1370044801 - 1372674544 and who also upgraded to a premium
plan in July.

15. How to use the formula
The SUMIFS formula sums the range depending on multiple
criteria. This is how it works:
=SUMIFS(sum_range, criteria_range1, criterion1, [criteria_range2,
criterion2, …])
And in plain english that means:
sum_range = The column to be added together
criteria_range1 = The search criteria area / range
criterion1 = The search term
criteria_range2 & criterion2 = Allow you to repeat with as many
conditions as you like

16. Your Formula will end up
looking something like this:
!
The Sum Range
The search criteria area / range
=SUMIFS('Data'!$F$2:$F$625,'Data'!$D$2:$D$625,">"&
$C$1,'Data'!$D$2:$D$625,"<"&$D$1,'Data'!$E$2:$E
$625,">"&A7,'Data'!$E$2:$E$625,"<"&A8)
The search criteria
to use the more
than, less than
operators you will
need to type: ">"&
Working Example

17. * OK, maybe I lied, it’s a little complicated

18. Your cohort table should end
up looking something like this:
All you have to do next is insert the graph!

19. Voila, simple cohort analysis!

21. !
One more time, just to be clear
=SUMIFS('Data'!$F$2:$F$625,'Data'!$D$2:$D$625,">"&$C
$1,'Data'!$D$2:$D$625,"<"&$D$1,'Data'!$E$2:$E
$625,">"&A7,'Data'!$E$2:$E$625,"<"&A8)
The above formula goes through the data on
the right to see if a row of data matches two
conditions, the “Sign Up” date and “Event
Date” and then returns the total. Simply fill out
the table below with the cohorts, hit the graph
button and your done!

23. Taking it up a notch
Half the battle is visualising your
cohort data. The graphs on the left show
how the same data can be altered by
changing the values from absolute to
percentages. You can make the graphs
easier to read by showing cumulative
values.
In addition, altering the cohort period
from monthly, to weekly, or even daily, will
provide greater insight (provided there
are enough numbers).

24. Little Tips
-
This is only really effective for quick cohort analysis, if
you’re wanting to do this regularly for multiple metrics
it’s best to get a developer involved and code it up.
-
Adding a config with a cell that sets the cohort length
will enable you to quickly give a different perspectives
on the same data by switching between weekly and
monthly cohorts.

25. Live Example Sheet
!
This has everything you need to get started and understand how
the cohorts are formed.
Enjoy!

26. You’ve been viewing
Really Simple Cohort Analysis
by
Hugh Hopkins (@hughhopkins) + GoSquared