Cohort analysis patterns in Tableau - Cell-by-Cell Formula Trace

Concept Flow

Order Date Column (B) --> Extract Month --> Compare to Earliest Month --> Assign Cohort Label

The cohort analysis groups orders by the month of their order date compared to the earliest order month in the data.

Formula

IF DATEPART('month', [Order Date]) = DATEPART('month', {MIN([Order Date])}) THEN 'Cohort 1' ELSE 'Cohort 2+' END

This Tableau formula checks if the order month matches the earliest order month and assigns cohort labels accordingly.

Step-by-Step Trace

Customer ID	Order Date	Revenue	Order Month	Earliest Month	Cohort
C001	2023-01-15	100	1	1	Cohort 1
C002	2023-01-20	150	1	1	Cohort 1
C001	2023-02-10	200	2	1	Cohort 2+
C003	2023-02-15	300	2	1	Cohort 2+
C002	2023-03-05	250	3	1	Cohort 2+
C004	2023-03-10	400	3	1	Cohort 2+

Orders in January are assigned 'Cohort 1', others 'Cohort 2+'.

Variable Tracker

Variable	Value	Explanation
MIN([Order Date])	2023-01-15	Earliest order date in dataset
DATEPART('month', [Order Date])	Varies per row (1,2,3)	Extracts month number from each order date
DATEPART('month', MIN([Order Date]))	1	Month number of earliest order date
Cohort Label	'Cohort 1' or 'Cohort 2+'	Assigned based on month comparison

Key Moments

What is the earliest order date found in the data?

How does the formula determine which cohort an order belongs to?

What cohort label is assigned to orders not in the earliest month?

Sheet Trace Quiz - 3 Questions

Which month is considered the earliest order month in the sample data?

A January

B February

C March

D April

Key Result

Cohort analysis groups data by the time period of first activity. Here, orders are grouped by the month of their order date compared to the earliest order month, enabling tracking of customer behavior over time.

Transcript

We start by finding the earliest order date in the data, which is January 15, 2023. Then, for each order, we extract the month number from its order date. We compare this month to the earliest order month. If they match, we assign the label 'Cohort 1'. Otherwise, we assign 'Cohort 2+'. This simple pattern helps us group customers or orders by their first active month, a common approach in cohort analysis.

Practice

1. What is the main purpose of cohort analysis in Tableau?

easy

A. To create pie charts for sales distribution

B. To filter data by geographic location only

C. To group users by their start time and track their behavior over time

D. To calculate total revenue without time context

Solution

1. Step 1: Understand cohort analysis concept

   Cohort analysis groups users based on when they started using a product or service.

2. Step 2: Identify its purpose in Tableau

   It tracks user behavior or retention over time, not just static metrics like revenue or location.

3. Final Answer:

   To group users by their start time and track their behavior over time -> Option C

4. Quick Check:

   Cohort analysis = group by start time and track behavior [OK]

Hint: Remember: Cohorts track groups by start time over periods [OK]

Common Mistakes:

• Confusing cohort analysis with simple filtering
• Thinking cohort analysis is only about total sales
• Ignoring the time dimension in cohort grouping

2. Which of the following calculated fields correctly defines a cohort start month in Tableau?

easy

A. DATEDIFF('day', [User Signup Date], TODAY())

B. DATEPART('year', [User Signup Date]) + 1

C. SUM([User Signup Date])

D. DATETRUNC('month', [User Signup Date])

Solution

1. Step 1: Understand cohort start date calculation

   Cohort start is usually the first day of the period, here month, so DATETRUNC('month', date) is correct.

2. Step 2: Evaluate each option

   Calculating days to today gives elapsed time, not the start month; adding 1 to the year part shifts cohorts incorrectly; summing dates is invalid.

3. Final Answer:

   DATETRUNC('month', [User Signup Date]) -> Option D

4. Quick Check:

   Start month = DATETRUNC('month', date) [OK]

Hint: Use DATETRUNC to get cohort period start date [OK]

Common Mistakes:

• Using DATEPART instead of DATETRUNC for cohort start
• Calculating date differences instead of truncating
• Applying aggregation functions on dates incorrectly

3. Given the cohort start month calculated as DATETRUNC('month', [Signup Date]) and the current month as DATETRUNC('month', TODAY()), what does this calculation return?
DATEDIFF('month', DATETRUNC('month', [Signup Date]), DATETRUNC('month', TODAY()))

medium

A. The number of days since the user signed up

B. The number of months since the user signed up

C. The user's signup date truncated to the day

D. The total count of users signed up this month

Solution

1. Step 1: Analyze the DATEDIFF function

   DATEDIFF('month', start, end) returns the number of whole months between two dates.

2. Step 2: Apply to given dates

   It calculates months between the user's signup month and the current month, showing how many months have passed.

3. Final Answer:

   The number of months since the user signed up -> Option B

4. Quick Check:

   DATEDIFF('month', signup, today) = months since signup [OK]

Hint: DATEDIFF with 'month' counts months between dates [OK]

Common Mistakes:

• Confusing months with days in DATEDIFF
• Thinking it returns a date instead of a number
• Assuming it counts users instead of time difference

4. You created a calculated field for cohort period as:
DATEDIFF('month', [Cohort Start], [Order Date])
but the results show negative values. What is the most likely cause?

medium

A. [Order Date] is earlier than [Cohort Start], causing negative differences

B. The DATEDIFF function does not support 'month' as an interval

C. The calculation should use DATEADD instead of DATEDIFF

D. [Cohort Start] is not a date field but a string

Solution

1. Step 1: Understand DATEDIFF behavior

   DATEDIFF returns negative values if the first date is after the second date.

2. Step 2: Check date order in calculation

   If [Order Date] is before [Cohort Start], the difference is negative, which explains the issue.

3. Final Answer:

   [Order Date] is earlier than [Cohort Start], causing negative differences -> Option A

4. Quick Check:

   Negative DATEDIFF means first date > second date [OK]

Hint: Check date order: earlier date first to avoid negatives [OK]

Common Mistakes:

• Assuming DATEDIFF can't use 'month' interval
• Confusing DATEDIFF with DATEADD function
• Not verifying data types of date fields

5. You want to create a heatmap in Tableau showing user retention by cohort month and months since signup. Which combination of fields and visualization best achieves this?

hard

A. Rows: Cohort Month (DATETRUNC), Columns: Months Since Signup (DATEDIFF), Color: Count of Users

B. Rows: User ID, Columns: Signup Date, Color: Total Sales

C. Rows: Order Date, Columns: Product Category, Color: Average Price

D. Rows: Months Since Signup, Columns: Total Revenue, Color: Cohort Month

Solution

1. Step 1: Identify correct cohort and period fields

   Cohort Month groups users by signup month; Months Since Signup tracks time elapsed.

2. Step 2: Choose visualization layout

   Heatmap uses rows and columns for cohort and period, color shows user counts for retention.

3. Final Answer:

   Rows: Cohort Month (DATETRUNC), Columns: Months Since Signup (DATEDIFF), Color: Count of Users -> Option A

4. Quick Check:

   Heatmap = cohort by period with user count color [OK]

Hint: Heatmap axes: cohort start and months since signup, color by users [OK]

Common Mistakes:

• Using unrelated fields like product category or revenue
• Placing total revenue on columns instead of cohort period
• Not using count of users for color intensity