Objective

Learn to analyze customer retention by cohort to identify trends and project lifetime value (LTV).

What is Cohort Analysis?

A cohort is a group of customers who share a common characteristic, typically their sign-up month. Cohort analysis tracks how these groups behave over time, especially their retention rates.

This is crucial for SaaS businesses because:

• It reveals if product improvements are increasing retention
• It helps forecast LTV
• It identifies when churn typically happens
• It shows if newer cohorts are healthier than older ones

The Data

Here's customer data for a SaaS company over 6 months:

Monthly Cohort Retention Table

Cohort	Month 0	Month 1	Month 2	Month 3	Month 4	Month 5
Jan	100	85	75	68	62	58
Feb	120	105	96	88	82	-
Mar	150	138	126	115	-	-
Apr	140	130	118	-	-	-
May	160	148	-	-	-	-
Jun	180	-	-	-	-	-

Reading the table:

• Month 0 = number of customers who signed up that month
• Month 1 = how many from that cohort are still active 1 month later
• And so on...

Your Analysis Tasks

1. Calculate Retention Rates

For each cohort, calculate the retention rate at each time period:

Retention Rate = (Customers Remaining / Starting Customers) × 100%

Create a new table showing retention rates (%) instead of raw numbers.

Example for January cohort:

• Month 0: 100% (100/100)
• Month 1: 85% (85/100)
• Month 2: 75% (75/100)
• Month 3: 68%
• Month 4: 62%
• Month 5: 58%

2. Identify Key Retention Milestones

Answer these questions:

Q1: What is the 1-month retention rate for each cohort? (Month 1 percentage)

• Jan: %
• Feb: %
• Mar: %
• Apr: %
• May: %

Q2: What is the 3-month retention rate for cohorts that have 3-month data?

• Jan: %
• Feb: %
• Mar: %
• Apr: %

3. Identify Trends

Based on your calculations, answer:

Is retention improving over time?

• Compare 1-month retention across cohorts. Are newer cohorts (May, June) retaining better than older ones (Jan, Feb)?
• Look at 3-month retention: Is March cohort better than January?

When does most churn happen?

• Look at the biggest drop-off points. Is it Month 0→1? Month 1→2? Month 2→3?

What's the long-term retention stabilization point?

• For the January cohort (most mature), where does the retention curve flatten? Month 3? Month 4?

Write 2-3 sentences summarizing your findings.

4. Project Lifetime Value Impact

Let's say:

• Monthly subscription price = $100
• Average monthly churn rate = 5%

Using the January cohort as a model:

Question: If we improve Month 1 retention by 10% (from 85% to 93.5% for new cohorts), what's the potential LTV impact?

Simplified LTV Calculation:

LTV = Monthly Price / Churn Rate

If churn drops from 15% (month 0→1) to 6.5%, how much more lifetime value does each customer generate?

Write a short answer explaining the business impact: "By improving Month 1 retention by 10 percentage points, we increase LTV from approximately $XXX to $XXX, meaning each customer is worth $XXX more."

5. Recommend an Action

Based on your cohort analysis, make one specific recommendation:

Examples:

• "Focus on Month 1 onboarding improvements since that's where we lose 15% of customers"
• "Newer cohorts are retaining better—double down on whatever changed in Q2"
• "3-month mark is critical—implement a retention campaign at 60 days"

Justify your recommendation with data from the cohort analysis.

Deliverable

Submit:

1. Retention rate table (% format)
2. Answers to trend questions (2-3 sentences)
3. LTV impact calculation (if retention improves)
4. One recommendation with justification

Outcome

You'll understand how to perform cohort analysis, interpret retention trends, and connect retention metrics to business value (LTV). This is a critical skill for growth marketers in subscription businesses.