Cohort Analysis

Hello Readers,
I’m passionate about reading and learning something new every day related to Tech, Analytics, and Data Science. Keeping that in mind, I thought, why not invite you all to join me in my learning journey?

Let us understand what Cohort Analysis is, where, and how we can use it, and what its purpose of it.

Suppose I have an e-commerce store where I have customer data for different months now to understand the purchasing pattern, I would have to make a cohort to understand the patterns.

Example- You group the customers who made their first purchase in January 2024 into one cohort. Customers from February form a different cohort.

Now you measure how often these customers made repeat purchases after their first purchase.

From this data, you can see that customers from February are more loyal. Perhaps you ran better offers or campaigns in February, which helped retain them better. This kind of insight helps you make smarter business decisions.

In short- Cohort Analysis is a powerful technique in analytics used to understand user behavior over time. Cohort analysis helps break down data into smaller, more manageable groups, providing insights into patterns and trends that might not be visible in aggregate data.

Cohort analysis tracks these groups (or cohorts) to observe their behavior and metrics over time, such as retention, churn, or revenue generation.

Steps to Perform Cohort Analysis:

1. Define the Cohort: Select a meaningful characteristic, such as signup or purchase date. For example, group users based on the month they joined your platform.
2. Identify the Metric to Track: Choose what to measure, such as retention rate (how many users remain active after X time) or conversion rate (how many users perform a specific action over time).
3. Organize Data: Create a table with rows representing cohorts, such as users who signed up in different months (e.g., January, February). Columns should represent periods after the cohort was formed (e.g., week 1, week 2).
4. Analyze Patterns:
   Compare performance across cohorts to identify trends. Look for patterns, like a decline in user engagement or higher retention for specific groups.

E-commerce and retail businesses like Amazon, Flipkart, and Shopify use cohort analysis to track customer purchasing behavior, repeat purchases, and product preferences over time. It helps them understand the impact of marketing campaigns and offers.

SaaS companies like Salesforce, HubSpot, Zoom, and Slack often use cohort analysis to understand which customers are staying active and which are dropping off, helping them improve product offerings and customer engagement strategies.

Cohort Analysis is critical for any business looking to retain customers, optimize strategies, and make data-driven decisions for growth!

I hope you learned something new today, just as I did. Continue exploring and evolving!