Cohort Analysis – Meaning | Types | Working Explained

Cohort analysis helps businesses understand customer behavior, retention, and engagement by grouping data into smaller, similar groups based on time, actions, or demographics. Unlike general analysis, it looks at specific groups to find hidden trends and patterns. This helps businesses make better decisions, keep customers, and improve marketing. For example, an e-commerce company can study loyal customers, or a SaaS business can track subscription trends. By using tools companies can create clear visuals, and understand patterns. As well as they can take steps to improve customer satisfaction and stay ahead in the market.

Cohort Analysis Meaning

It is a way to study and group data based on people who share something in common. Like when they made their first purchase, sign up for a service, or where they are located. It helps businesses follow these groups over time to understand their behavior. Such as how often they return, how valuable they are, and how engaged they stay. Unlike general data analysis, cohort analysis focuses on specific groups instead of combining data from everyone. This makes it easier to see patterns, find problems, and spot opportunities that might not be obvious in overall data.

Why is Cohort Analysis Important?

It offers several critical benefits for businesses:

• Understand Customer Retention: It shows how well different groups of customers stick with your business over time, helping you find ways to keep them longer.
• Create Better Marketing: It helps you understand what different customer groups need. So you can make marketing more personal and effective.
• Spot Trends Easily: By looking at specific groups, you can find useful patterns to make smarter business decisions.

Types of Cohort Analysis

It can be categorized into several types based on the criteria used to define cohorts. Below are the most common types:

1. Time-Based Group Analysis

This method looks at customers based on when they joined a service or made a purchase. For instance, you might want to compare all users who signed up in January to those who signed up in February. By doing this, a business can see how its actions differ and find ways to improve. As well as how it welcomes new customers or encourages them to use the product.

2. Action-Based Group Analysis

In this approach of cohort analysis, customers are grouped by what they do, rather than when they signed up. For example, a business might track people who made their first purchase recently to understand if they are likely to buy again. This helps the company see patterns in customer behavior.

3. Demographic Group Analysis

This type of analysis looks at customers based on shared characteristics, like their age, where they live, or their gender. By understanding these traits, businesses can learn how different groups respond to promotions or new features. For example, they might observe how young customers react differently from older ones.

4. Subscription-Based Business Analysis

For companies that offer services through subscriptions, analyzing how different groups of customers perform over time is very important. For instance, a subscription service might track customers who joined during a specific time frame to see how many of them continue their subscriptions after several months or a year. This helps the business gauge customer loyalty and satisfaction.

How to Perform Analysis - Step-by-Step Guide?

Now that we understand the importance and types of cohort analysis, let’s dive into how you can perform this analysis. This section will walk you through the steps involved in conducting an effective analysis:

Step 1: Identify Your Groups

The first part of analyzing how groups of customers behave is to decide which groups you want to look at. Think about the traits or actions that are most important for your study. For instance, if you want to see how well you keep customers, you might group them by when they made their first purchase.

• Example: A retail store could create groups based on the month customers made their first purchase, like all the customers who bought something for the first time in January, February, March, and so on.

Step 2: Gather the Right Information

Next, you’ll need to collect all the details that will help you keep track of your groups. This information can include things like when customers first interacted with your business. What they've bought in the past when their subscriptions renew, or how they engage with your offerings.

Make sure this information is clean and organized so you can easily analyze it over time.

• Example: If you’re looking at customer retention cohort analysis, you’ll want to know how often customers come back after their first purchase and how long it takes them before they return.

Step 3: Monitor Group Performance Over Time

Once you have your groups and the necessary information, the next step is to see how they perform over time. This often means calculating things like how many customers stay loyal or stop buying from you after a certain period.

You might measure, for example, the percentage of customers from a group who make a second purchase within 30 days and compare that to other groups.

• Example: You might find that customers who bought something for the first time in January tend to come back more often than those who made their first purchase in June, indicating seasonal trends or differences in marketing efforts.

Step 4: Present Your Findings in a Visual Way

To make your analysis easier to understand and act upon, you can create visual representations of the data, like graphs or charts. A typical cohort analysis graph shows how many customers keep returning over time for each group.

Using tools like Tableau or similar software, you can create visuals that highlight trends, differences among groups, and important insights.

• Example: A graph might show that customers who joined in January have a 50% return rate after 60 days. While those who joined in June have only a 30% return rate.

Step 5: Draw Conclusions and Take Action

The final step is to use the insights gained from your analysis to make informed decisions. Whether it's fine-tuning your marketing approach, boosting customer loyalty, or enhancing your product offerings, what you learn from this analysis can help shape your business strategies.

• Example: If you notice a particular group has a low return rate, it’s worth investigating why. Maybe there’s a problem with how new customers are welcomed or a feature they’re not finding useful. By identifying these issues early, you can address them before they affect other groups.

Cohort Analysis Examples

To better understand the application of cohort analysis, here are a few real-world examples:

1. Keeping Customers: An E-commerce Example

An online shopping company can group customers based on when they made their first purchase. By looking at how many of these customers come back to buy again, the company can discover which months bring in the most loyal shoppers.

2. Understanding Subscription Loyalty in Software Services

A software service company can focus on customers who signed up during specific times of the year. By checking how many of these customers continue their subscriptions after six months. The company can find patterns in how well it keeps its customers and adjust its marketing and product offerings accordingly.

3. Using Tableau to See Trends

A business can use a tool called Tableau to create easy-to-understand visual charts and graphs that show analysis. This way, they can see how many customers stick around or how other important metrics change over time. With Tableau cohort analysis, businesses can build interactive dashboards that help track the performance of different customer groups.

Cohort analysis is a powerful tool for understanding customer behavior over time. If you want to dive deeper into this technique and more, our Data Analytics Course will help you master data analysis skills. Learn to apply cohort analysis alongside other data techniques to uncover valuable insights and make data-driven decisions.

Cohort Analysis in SQL

Cohort analysis in Structured Query Language is used to group and analyze users or data based on a common characteristic or event over a specific time frame. For example, you might group users by their sign-up month (cohort) and analyze their behavior, like retention rates or purchase patterns, over time.

Steps for Analysis in SQL-

1. Define the Cohort: Decide on the characteristic or event that defines the cohort, such as the user's sign-up date or the first purchase date.
2. Group Data by Cohort: Use the SQL GROUP BY clause to group users by the chosen cohort, typically truncating dates to a specific granularity (e.g., month).
3. Measure the Metric of Interest: For each cohort, track the metrics, such as retention, engagement, or revenue over time.
4. Visualize the Data: Export the results to a visualization tool like Excel or Tableau to create a cohort analysis table or chart.

Example: Retention Analysis Using SQL

Scenario: Analyze user retention based on the sign-up month.