Cohort analysis in Digital Marketing - Mini Project: Build & Apply

Cohort Analysis Basics

📖 Scenario: You work as a marketing analyst for an online store. You want to understand how groups of customers who joined in the same month behave over time. This helps you see if your marketing efforts keep customers engaged.

🎯 Goal: Build a simple cohort analysis table that groups customers by their signup month and tracks their retention over the next three months.

📋 What You'll Learn

Create a data structure with customer signup months and their activity in following months

Add a variable to define how many months to track retention

Calculate retention rates for each cohort over the tracking period

Present the final cohort retention table clearly

💡 Why This Matters

🌍 Real World

Cohort analysis helps businesses understand customer behavior over time and improve marketing strategies.

💼 Career

Marketing analysts and product managers use cohort analysis to measure customer retention and engagement.

1

Create the customer signup data

Create a dictionary called customer_data with these exact entries: 'Jan2024': 100, 'Feb2024': 80, 'Mar2024': 60. Each value represents the number of customers who signed up in that month.

Digital Marketing

# Create the customer_data dictionary with signup counts
# Your code here

Hint

Use a dictionary with month names as keys and signup counts as values.

2

Set the retention tracking period

Create a variable called months_to_track and set it to 3 to represent tracking retention for three months after signup.

Digital Marketing

customer_data = {'Jan2024': 100, 'Feb2024': 80, 'Mar2024': 60}
# Set months_to_track to 3
# Your code here

Hint

This variable controls how many months you will analyze retention for.

3

Calculate retention rates for each cohort

Create a dictionary called retention_rates. Use a for loop with variables month and count to iterate over customer_data.items(). For each month, calculate retention rates as 100%, 80%, and 60% of the original count for months 0, 1, and 2 respectively. Store these rates as a list in retention_rates[month].

Digital Marketing

customer_data = {'Jan2024': 100, 'Feb2024': 80, 'Mar2024': 60}
months_to_track = 3
# Calculate retention_rates dictionary
# Your code here

Hint

Retention starts at 100% of original customers, then drops to 80% and 60% in following months.

4

Present the cohort retention table

Create a variable called cohort_table and assign it the value of retention_rates. This will represent the final cohort retention table.

Digital Marketing

customer_data = {'Jan2024': 100, 'Feb2024': 80, 'Mar2024': 60}
months_to_track = 3
retention_rates = {}
for month, count in customer_data.items():
    retention_rates[month] = [count, int(count * 0.8), int(count * 0.6)]
# Assign retention_rates to cohort_table
# Your code here

Hint

This final variable holds the cohort retention data for easy reference.

Practice

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

easy

A. To group users by shared traits and study their behavior over time

B. To create random user groups for testing ads

C. To track only the total number of website visits

D. To analyze competitor marketing strategies

Solution

1. Step 1: Understand cohort analysis definition

   Cohort analysis groups users who share common characteristics to observe their behavior over time.

2. Step 2: Identify the main goal in marketing context

   This helps marketers see patterns like retention and engagement, which is not about random groups or competitor analysis.

3. Final Answer:

   To group users by shared traits and study their behavior over time -> Option A

4. Quick Check:

   Cohort analysis = Group users by traits and track behavior [OK]

Hint: Remember: cohorts = groups with shared traits over time [OK]

Common Mistakes:

• Confusing cohort analysis with random user grouping
• Thinking it only counts total visits
• Mixing it up with competitor analysis

2. Which of the following is the correct way to define a cohort in cohort analysis?

easy

A. Users who made their first purchase in the same month

B. Users who visited the website once

C. Users who clicked any ad randomly

D. Users who have different signup dates

Solution

1. Step 1: Identify cohort grouping criteria

   Cohorts are defined by shared traits, such as the time of first purchase or signup.

2. Step 2: Evaluate options for correct cohort definition

   Only users who made their first purchase in the same month form a valid cohort; random clicks or single visits do not define cohorts.

3. Final Answer:

   Users who made their first purchase in the same month -> Option A

4. Quick Check:

   Cohort = users sharing a key event time [OK]

Hint: Cohorts share a key event time, like signup month [OK]

Common Mistakes:

• Choosing random or one-time actions as cohorts
• Ignoring the shared time or event aspect
• Mixing different signup dates in one cohort

3. Given this cohort retention table, what is the retention rate for users who joined in January after 2 months?

Month Joined | Month 0 | Month 1 | Month 2
January      | 100     | 80      | 60
February     | 120     | 90      | 70

medium

A. 70% retention after 2 months

B. 80% retention after 2 months

C. 60% retention after 2 months

D. 100% retention after 2 months

Solution

1. Step 1: Identify initial users and retained users after 2 months

   For January cohort, initial users (Month 0) = 100, users after 2 months (Month 2) = 60.

2. Step 2: Calculate retention rate

   Retention rate = (Users after 2 months / Initial users) * 100 = (60 / 100) * 100 = 60%.

3. Final Answer:

   60% retention after 2 months -> Option C

4. Quick Check:

   60/100 = 0.6 = 60% retention [OK]

Hint: Retention = (users at month N / initial users) x 100% [OK]

Common Mistakes:

• Using wrong month data for calculation
• Dividing initial users by retained users
• Confusing retention with total users

4. A marketer created cohorts based on signup month but accidentally mixed users from different months in one cohort. What is the main issue with this approach?

medium

A. It improves data accuracy by mixing more users

B. It helps compare different months easily in one group

C. It only affects the total user count, not behavior analysis

D. It breaks the cohort definition, causing inaccurate behavior tracking

Solution

1. Step 1: Understand cohort grouping importance

   Cohorts must have users sharing the same key trait, like signup month, to track behavior accurately over time.

2. Step 2: Analyze impact of mixing different months

   Mixing users from different months breaks this principle, leading to misleading retention and engagement data.

3. Final Answer:

   It breaks the cohort definition, causing inaccurate behavior tracking -> Option D

4. Quick Check:

   Mixing months breaks cohort accuracy [OK]

Hint: Keep cohorts pure by shared traits only [OK]

Common Mistakes:

• Assuming mixing cohorts improves accuracy
• Ignoring impact on behavior tracking
• Thinking it only changes user count

5. You want to analyze how different marketing campaigns affect user retention over 6 months. Which cohort analysis approach is best?

hard

A. Group users by signup date only, ignoring campaign data

B. Group users by the campaign they first interacted with and track retention monthly

C. Group all users together regardless of campaign and track total retention

D. Analyze only the first month retention for all users combined

Solution

1. Step 1: Define goal of analysis

   The goal is to see how different campaigns impact retention over time.

2. Step 2: Choose cohort grouping that matches goal

   Grouping users by the campaign they first interacted with allows comparing retention per campaign monthly.

3. Step 3: Evaluate other options

   Grouping all users together or ignoring campaign data loses campaign-specific insights; analyzing only first month misses long-term trends.

4. Final Answer:

   Group users by the campaign they first interacted with and track retention monthly -> Option B

5. Quick Check:

   Group by campaign for retention impact analysis [OK]

Hint: Group cohorts by campaign to compare retention effects [OK]

Common Mistakes:

• Ignoring campaign data in cohort grouping
• Mixing all users losing campaign insights
• Only checking short-term retention