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

Need a 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

Need a 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

Need a 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

Need a hint?

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