Cohort analysis in Digital Marketing - Step-by-Step Execution

Concept Flow - Cohort analysis

Identify Cohort Criteria

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Group Users by Cohort

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Track Behavior Over Time

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Compare Cohorts

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Analyze Trends and Insights

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Make Decisions

Cohort analysis groups users by shared traits, tracks their behavior over time, compares groups, and helps make informed decisions.

Execution Sample

Digital Marketing

Cohort = users who signed up in Jan
Track retention each month
Compare Jan vs Feb cohorts
Identify which cohort stays longer

This example tracks user retention monthly for cohorts defined by signup month.

Analysis Table

Step	Action	Cohort	Metric Tracked	Result/Insight
1	Identify cohort by signup month	Jan 2024	Signup date	Users grouped who joined in Jan
2	Track retention after 1 month	Jan 2024	Retention rate	60% users active in Feb
3	Identify next cohort	Feb 2024	Signup date	Users grouped who joined in Feb
4	Track retention after 1 month	Feb 2024	Retention rate	70% users active in Mar
5	Compare retention rates	Jan vs Feb	Retention rate	Feb cohort retains better
6	Analyze trend	All cohorts	Retention over months	Later cohorts show improvement
7	Make decision	Marketing team	Focus on improving Jan cohort	Plan re-engagement campaigns
8	End	-	-	Analysis complete

💡 All cohorts tracked and compared to find retention trends

State Tracker

Variable	Start	After Step 2	After Step 4	After Step 5	Final
Cohort Jan 2024	Users signed up in Jan	60% retained	60% retained	60% retained	60% retained
Cohort Feb 2024	Not defined	Not defined	70% retained	70% retained	70% retained
Retention Comparison	N/A	N/A	N/A	Feb cohort better	Feb cohort better

Key Insights - 3 Insights

Why do we group users by signup month instead of all users together?

Grouping by signup month creates cohorts that share the same start time, making it easier to compare behavior over time as shown in steps 1 and 3.

What does retention rate mean in this analysis?

Retention rate shows the percentage of users from a cohort still active after a period, like 60% retained in step 2, helping measure engagement.

Why compare different cohorts instead of just looking at one?

Comparing cohorts reveals trends and improvements over time, like Feb cohort retaining better in step 5, which helps guide decisions.

Visual Quiz - 3 Questions

Look at the execution table, what is the retention rate of the Jan 2024 cohort after 1 month?

A 50%

B 70%

C 60%

D 80%

💡 Hint

Check row 2 under 'Result/Insight' for Jan 2024 cohort retention

At which step do we first compare two cohorts?

A Step 3

B Step 5

C Step 2

D Step 6

💡 Hint

Look for 'Compare retention rates' action in the execution table

If the Feb cohort retention was 50% instead of 70%, what would change in the variable tracker?

A Cohort Feb 2024 retention would show 50% after Step 4

B Cohort Jan 2024 retention would change to 50%

C Retention comparison would say Jan cohort better

D No changes would occur

💡 Hint

Check 'Cohort Feb 2024' row values after Step 4 in variable tracker

Concept Snapshot

Cohort analysis groups users by shared traits (e.g., signup month).
Tracks their behavior over time (like retention rates).
Compares cohorts to find trends.
Helps make data-driven marketing decisions.
Useful for improving user engagement and retention.

Full Transcript

Cohort analysis is a method to group users who share a common characteristic, such as the month they signed up. We then track how these groups behave over time, for example, measuring how many users remain active after one month. By comparing different cohorts, like users who joined in January versus February, we can see which group retains better. This helps marketers understand trends and make decisions to improve engagement. The process involves identifying cohorts, tracking metrics, comparing results, analyzing trends, and acting on insights.