Cohort Analysis: A Guide for Data Analysts

Today, only data-driven businesses that understand customer behavior can outperform their competitors. But how do companies analyze their data to better understand their customers?

Cohort analysis is a powerful tool that helps companies and data analysts understand trends and data patterns over time. It is not only about basic metrics and insights into how different groups of users interact with a product or service. Cohort analysis groups users into multiple segments based on shared characteristics. This helps data analysts to identify trends that may have been overlooked in a broader analysis.

According to the Corporate Finance Institute, businesses that use cohort analysis can improve customer retention rates by up to 20% by tailoring their strategies to specific user groups. However, successfully performing cohort analysis and making it scalable for your team needs careful planning and the right tools.

Let’s discuss the basics of cohort analysis and how senior data analysts can use it to achieve meaningful results.

Understanding Cohorts: The Foundation of Cohort Analysis

Cohorts are groups of people or things that share a common trait or experience during a certain time. We study these groups in cohort analysis to find patterns and behaviors over time.

Cohort analysis is useful for businesses across sectors–for e-commerce businesses, this analysis shows when customers are likely to buy again, whereas for SaaS companies, it looks at how many users signed up in the same month. By analyzing these patterns, your company can make better choices to improve your marketing and product strategies.

Data analysts can also monitor and analyze the behavior of different groups by segmenting their data into cohorts. Understand this with an example. You might find that customers who join a group in January have higher retention rates than those who join in March.

Understanding cohorts helps companies make more accurate predictions and targeted improvements. It helps answer questions like:

• Which customer segment has the highest engagement over time?
• How does user retention vary across different cohorts?
• Are marketing efforts producing long-term value for specific groups?

This level of analysis helps senior data analysts offer actionable insights that drive better decisions related to customer retention and product development.

Cohorts vs. Customer Segments

You might think cohorts and customer segments are similar in data analysis, as both involve analyzing different user groups. However, they serve distinct purposes and focus on different aspects. Let’s take a closer look at their differences through the table below.

Both techniques offer valuable insights, but they serve different purposes. Cohort analysis helps you understand how groups evolve. On the other hand, customer segmentation helps you understand the characteristics of your users at any given moment. You can better understand your customers and make informed decisions by combining the goods of both approaches.

Cohort Analysis Improves Customer Segmentation

Customer segmentation and cohort analysis are two powerful tools in data analysis. You can use them to understand user behavior and improve marketing strategies. Here is how cohort analysis can strengthen your customer segmentation efforts:

• Refine Targeting: Use cohort data to fine-tune customer segments based on behavior over time, not just static attributes. For instance, you can target users who signed up during a specific period and have high retention rates.

• Identify Valuable Segments: Cohort analysis can highlight which segments show long-term value. You may discover that a particular cohort has a higher lifetime value or better engagement than another, even if both groups are categorized as “loyal customers.”

• Track Segment Performance: Cohort analysis helps track how different customer segments perform over time. You can see how changes in marketing or product updates impact behavior within each segment.

• Data-Driven Decision-Making: Combining cohort analysis with customer segmentation can help you make informed decisions. If you have a deeper grasp of how various cohorts act and how they fit into your consumer segments, you can make strategic decisions that satisfy your user demands and commercial objectives.

An Example of Cohorts and Segments Working Together

Let’s say you analyze retention for a SaaS product with customer segments. You have divided your customers into three main groups:

• Freemium users: Users who never upgraded from the free version.

• Mid-tier subscribers: Users who pay for a mid level subscription.

• Enterprise clients: Larger businesses that pay for a premium service.

Cohort analysis will help you understand how retention changes over time in each group. For example, you might notice that users with mid level subscriptions who joined in the first quarter of 2023 leave at a higher rate than those who joined in the last quarter of 2022.

This finding could prompt you to examine more closely what happened during the onboarding process or any new features introduced in the first quarter. Understanding these patterns allows you to make improvements to keep more users engaged and reduce churn.

Metrics For Cohort Analysis

Tracking the right metrics is key to making cohort analysis effective. The retention rate measures the percentage of active users after a set period. On the other hand, the Churn rate shows how many users have dropped off. For revenue-focused insights, monitoring cohort revenue, a group’s total spending over time provides valuable data. The average order value measures the user spending habits in each cohort.

In SaaS, metrics like average revenue per user (ARPU) and customer lifetime value (CLTV) are critical for evaluating customer value and growth potential. These metrics help answer important questions, such as:

• Are new users sticking around longer after a pricing change?
• How do Black Friday buyers compare to regular customers in repeat purchases?
• Are SaaS users onboarded in Q1 upgrading faster than those onboarded in Q4?

Step-by-Step: Conducting Cohort Analysis with Python and ClicData

Cohort analysis can yield important information about user behavior. Here is a quick tutorial on using Python and ClicData to conduct cohort analysis.

Step 1: Data Preparation

Collect the required information, including transaction dates, customer IDs, and KPIs like revenue or subscription status. Use packages such as pandas to clean and format data. Make sure the format of all the dates is the same.

In Python, start by loading and cleaning your data:

import pandas as pd
# Example dataset
data = pd.DataFrame({
    'user_id': [1, 2, 3, 4, 5],
    'signup_date': ['2023-01-01', '2023-01-01', '2023-02-01', '2023-02-01', '2023-03-01'],
    'purchase_date': ['2023-01-05', '2023-01-10', '2023-02-15', '2023-02-20', '2023-03-25']
})
# Ensure dates are in the correct format
data['signup_date'] = pd.to_datetime(data['signup_date'])
data['purchase_date'] = pd.to_datetime(data['purchase_date'])
print(data.head())
# Output:
#    user_id signup_date purchase_date
# 0       1  2023-01-01   2023-01-05
# 1       2  2023-01-01   2023-01-10
# 2       3  2023-02-01   2023-02-15
# 3       4  2023-02-01   2023-02-20
# 4       5  2023-03-01   2023-03-25

ClicData simplifies the process by skipping the coding entirely. Connect your data source, such as a database or any cloud platform, and use ClicData’s built-in transformations to clean and structure your dataset. This eliminates manual errors and ensures data consistency.

Step 2: Defining Cohorts

Group customers based on shared actions or timeframes. For example, you can define cohorts based on your customers’ lifespan which needs to be calculated over time. 

Here’s a sample of an input table:

You have two options to calculate your customer lifespan with ClicData: using data templates or Python. Data templates are great tools for quicker and easier data modeling tasks, and don’t require any code.  

Here’s how it works.

Based on the input table above, you would use the calendar data template to add one row per month for each customer and calculate their lifespan based on their signup date:  

But if you want to do it all in Python, then the pd.to_period()function makes this straightforward:

# Define signup cohorts
data['cohort'] = data['signup_date'].dt.to_period('M')
print(data[['user_id', 'cohort']])
# Output:
#    user_id     cohort
# 0       1  2023-01
# 1       2  2023-01
# 2       3  2023-02
# 3       4  2023-02
# 4       5  2023-03

Step 3: Calculating Metrics

Calculate the key metrics you want to track for each cohort. These can be retention rate, churn rate, or lifetime value. Here is how to compute retention by comparing cohort activity over time with Python.

ClicData takes visualization further. You can build interactive dashboards with heatmaps, cohort curves, and real-time updates using the drag-and-drop feature. You can also share dashboards directly with stakeholders or schedule automated reports.

Step 5: Interpreting Insights

The last phase involves converting your results into practical suggestions. For instance, if your study reveals retention declines after three months, you may recommend focused re-engagement initiatives.

With Python, export summaries for your team:

# Calculate retention rates
retention_rate = cohort_counts.div(cohort_counts.sum(axis=1), axis=0)
print(retention_rate)
# Output:
# active_month  2023-01  2023-02  2023-03
# cohort                                
# 2023-01        1.00     0.00     0.00
# 2023-02        0.00     1.00     0.00
# 2023-03        0.00     0.00     1.00

ClicData helps you show important information using KPI widgets and notes. This makes it easier for teams to use insights effectively.

Why ClicData is Ideal for Cohort Analysis?

ClicData simplifies cohort analysis by offering all-in-one tools for data management, transformation, automation, visualization, and sharing.

• All-in-One Data Management: ClicData lets you connect and transform your data in one platform. You can import data from multiple sources. These sources can be CRMs, e-commerce platforms, or SaaS tools. Then, you can organize it for analysis without switching between tools.

• Intuitive Visualizations: With ClicData, you can create clear, interactive dashboards to visualize cohort data. Heatmaps, trend lines, and retention graphs can be built with a simple drag-and-drop interface.

• Automation and Real-Time Updates: ClicData supports automated workflows, so your cohort analysis stays updated with the latest data. You can also schedule reports and dashboards to automatically refresh and share them with stakeholders.

• Easy Integration: ClicData can connect with more than 200 cloud and on-premise applications and databases. This flexibility allows you to pull data from multiple sources into one platform.

• Advanced Analytics: ClicData supports calculations and modeling, including metrics like retention rate, churn, and customer lifetime value. These insights help refine marketing, sales, or product development strategies.

Take the First Step Toward Smarter Customer Insights with Cohort Analysis

With ClicData’s all-in-one platform, you can easily manage, analyze and visualize your data without any complex coding or multiple tools. ClicData integrates data from various sources so you can track key metrics, build meaningful cohorts, and share real-time reports with your team.

Take the first step today:

• Streamline your data management process
• Conduct cohort analysis with ease
• Visualize key trends to make informed decisions
• Collaborate and share insights with your team 

Ready to unlock valuable insights and drive better outcomes? Book your demo with ClicData now and see how easy cohort analysis can be!