Cohort Analysis

What is Cohort Analysis?

Cohort Analysis is one of the types of behavioral analytics that encompasses breaking down into related groups known as Cohorts. These cohorts are classified based on the common characteristics or experiences within a defined timeframe that allows businesses to observe patterns over the lifecycle of their customers or users.

Unlike traditional data analysis methods that treat all users as a homogeneous group, cohort analysis provides a more nuanced understanding of user behavior by focusing on specific segments.

It is a method where we segment data into groups based on shared experiences or characteristics within a specific timeframe. These groups or cohorts allow analysts to identify patterns, track behavior, and evaluate performance over time.

Cohort analysis is a critical analytical technique used across various industries to gain insights to understand behavioral aspects and improve business performance. Businesses can identify trends and patterns that inform strategic decisions by segmenting users into groups based on shared features or experiences over a specific time.

This article will delve into the complexities of cohort analysis, its applications, and how to implement it in your organization effectively.

Understanding Cohort Analysis

When understanding Cohort under Cohort Analysis, it refers to the individuals who share a particular characteristic during a specific time period. These features can include

• Users who have signed up for service in the same month
• Customers who made their first purchase during a promotional event, or
• Individuals who engage with a product after receiving targeted marketing.

The factor that differentiates a cohort from other segments is time, which may group users based solely on behavior without considering timing.

Cohort analysis is invaluable for several reasons:

1. Identifying Trends: Businesses can uncover trends in user engagement and retention by analyzing cohorts over time.
2. Understanding User Behavior: It facililates companies to see how various groups react to changes in products or marketing strategies.
3. Improving Customer Retention: Insights gained from cohort analysis can aid in identifying factors contributing to customers churn and inform strategies to enhance retention rates.

Types of Cohorts

Cohort can be classified into three types based on different criteria. They are

1. Acquisition Cohorts
2. Behavioral Cohorts
3. Time-based Cohorts

Let us understand them below.

Acquisition Cohorts

Acquisition Cohorts are created based on when users interact for the first time with the product or service. Or, we can say the group is formed on the basis of when the customers have signed up for the product.

This type of cohort enables an analysis of customer retention and churn rates in a specific time-frame.

For example, all users who signed up in January 2024 would form an acquisition cohort.

Behavioral Cohorts

The grouping of behavioral cohorts depends on specific actions they have taken within the application or service. Meaning, a cohort here is made depending upon the specific actions users have taken inside the product or application.

This will facilitate businesses in analyzing demographics with various behavioral patterns.

Time-Based Cohorts

These cohorts are grouped depending upon the behavior and engagement with a product or service in a specific time-frame. For example, analyzing users who made purchases during holiday sales versus those who purchased during regular months.

How to Conduct Cohort Analysis

Cohort analysis is a time-consuming process that depends on the size of the cohort and availability of data. That is the reason, it is recommended to begin a cohort analysis with a small number of observations to get a better sense of the analysis.

It should also be noted that there can be other approaches to conducting and executing a cohort analysis.

Below we present steps to conduct a cohort analysis.

Step 1: Define Your Objective

Before we dive into the data, it is critical to begin with a clear goal. Defining your objective helps in shaping the analysis.

• Are you examining customer retention?
• Product usage?
• Or, revenue growth?

Clearly outline what you are trying to achieve with the cohort analysis. Are you looking to improve retention rates? Understanding user engagement?

This objective will guide the business decision-makers in cohort selection and analysis methods.

Step 2: Identify Your Cohorts

Once we have defined our objective behind conducting an analysis of the cohorts, the next step in the process is to group users into cohorts. 

This can be based on the following:

• Acquisition Dates: Analysis based on when the user has joined.
• Behavior Patterns: Analysis based on different behavioral patterns.
• First Purchase: Analyzing cohorts from their first purchase.
• Product Interaction: Analysis based on product interactions.
• Or, any other relevant metrics.

It is important to ensure the parameters align with the business's objectives.

Step 3: Collect Data

The next step will be to collect relevant data from different sources like web analytics tools, CRM systems, or E-commerce platforms. When using data from these sources, it is important to ensure it is clean and organized for effective analysis.

Step 4: Analyze the Data

Organize users into cohorts based on the selected criteria. Use tools like Excel, Tableau, or SQL queries for data visualization and manipulation.

Compare cohorts over time to identify:

• Retention Rates: How many users from each cohort remain active?
• Engagement Patterns: What activities do cohorts engage in most?
• Revenue Trends: How does spending vary across cohorts?
• Conversion Rates: What is the rate at which potential customers are becoming part of the existing customer base?

Analytical tools are utilized to examine different behavior of each cohort over time. Look for trends and different metrics mentioned above.

Step 5: Interpret Results

After the data is analyzed, the next step will be to interpret the results in the light of original objectives. Identify actionable insights and consider how they can inform business strategies moving forward.

We can also compare the results from the standards set. And a detailed variance analysis can be conducted on why the results are steering away from the set standards, if the interpretation suggests the results are adverse. 

Also, evaluating the reasons why the results are positive, and what factors led us there.

Step 6: Implement Changes

Based on the results and interpretations, decision-makers can implement changes that are aimed at improving users' experience and engagement. Monitor the impact of these changes through ongoing cohort analysis.

Tools for Conducting Cohort Analysis

Several tools are available that facilitate cohort analysis:

Examples of Cohort Analysis in Action

To understand cohort analysis, we will go through three examples. a SaaS retention, an E-commerce purchase trends, and a Mobile App feature adaption analysis.

Let us go through them.

SaaS Retention Analysis

Consider a SaaS company that groups users by their subscription start date. They discovered that users that started using the platform in Q1 2024 have a greater churn rate when compared to Q4 2023. The company improves its onboarding experience, resulting in increased retention for subsequent cohorts.

E-commerce Purchase Trends

An E-comm platform tracks its users by their first purchase date. Analysis reveals the users making their first purchase during a holiday sale or more likely to return within six months. The company doubles down on seasonal promotions.

Mobile App Feature Adoption

A mobile app analyzes cohorts based on when users activated a new feature. The analysis shows that users who adopted the feature early have higher long-term engagement. The app team creates tutorials to encourage feature usage among newer cohorts.

Best Practices for Cohort Analysis

Cohort analysis is a sophisticated approach to understand the customer behavior and take this understanding to the business's advantage. To make the most of this technique, there are a few best practices. 

Let us look at them below.

1. Start Small: Start with small and simple analysis to grasp the process before diving into complex datasets.
2. Use Visualizations: Using visualization techniques and tools is always recommended to understand the data better. The range it allows to communicate most with less makes it really useful.
3. Iterate and Refine: It is important to regularly revisit the analysis as the new data becomes available.
4. Collaborate with Teams: Involve internal and external stakeholders in the process. Include members from marketing, product management, and analytics teams to derive meaningful insights.

Common Challenges in Cohort Analysis

Let us understand some of the most common challenges in cohort analysis.

1. Data Quality Issues: It is critical to ensure the data at hand is accurate and complete. Inconsistent or missing data can distort results.
2. Overlapping Cohorts: Businesses should be cautious of different overlapping criteria that may blur the differentiation between the cohorts.
3. Interpretation Errors: Users should not overanalyze any minor fluctuations. They should be focusing on significant and actionable trends.

Conclusion

Cohort analysis can prove to be a game-changer for understanding different user behaviors and improving decision-making. By understanding the different steps involved in cohort analysis, businesses can take advantage of the technique to drive growth, retention, and engagement.

Cohort analysis is a great data analytics topic even for finance professionals to understand customer behavioral patterns to derive actionable insights. This analysis provides you the necessary tools to unearth hidden opportunities.

Start small, iterate, and keep refining—your cohorts will tell a story worth listening to!

Free Resources

To continue learning and advancing your career, check out these additional helpful WSO resources:

• Data Presentation
• Data Visualization
• Empirical Rule
• Negatively Skewed Distribution
• Positively Skewed Distribution