Cohort analysis – meaning and significance 

Cohort analysis – meaning and significance 

Cohort analysis is the process of classifying data into different groups called cohorts. The groups have common traits and are defined by a fixed period. Let’s take a look at what the cohort types are, how cohort analysis works and why you may need cohort analysis. 

Types of cohort analysis

Basically, there are two most common cohorts – acquisitional and behavioral. 

Acquisitional Cohort Analysis segments the user base on the basis of the acquisition date and time of the service or when the user signed up for a product. This tracking can be performed with different frequencies relevant to the product like daily, weekly, monthly, etc. 

Example: A Food delivery application can analyze an Acquisition Cohort on a daily basis, on the other hand, a B2B service app can perform the Acquisition Cohort on a monthly basis. 

Behavioral Cohort Analysis segments the user base on the basis of the actions they undertake while using the application. Special event triggers can be tracked to understand the behavior of demographically different users. 

Example: Such triggers on a Food Delivery app could be the choice of restaurants and frequency with which customers order food, and for a social media platform, it could be the pages a user follows or the posts they like. 

Besides that, exists another point about types of cohorts. According to it, there are three types of cohorts:

  1. Time-based cohort analysis
  2. Size-based cohort analysis
  3. Segment-based cohort analysis

Steps to create cohort analysis

To clarify, performing cohort analysis is not that difficult to do. If you want to get a profound knowledge about creating cohort analysis in Google Sheets, check out my article!

Subsequently, let’s observe what steps we should undertake to perform cohort analysis:

  • 1. Define the Objective: Determine the specific objective or question you want to address through cohort analysis. It could be understanding customer retention, evaluating the impact of a marketing campaign, or identifying patterns in user behavior.
  • 2. Identify Cohort Characteristics: Select the relevant cohort characteristics based on your objective. Common characteristics include sign-up date, first purchase, subscription start date, or any other event or attribute that defines the cohort. This will help you group individuals with similar experiences.
  • 3. Data Collection and Preparation: Gather the necessary data for analysis. Ensure that your data set contains the required cohort characteristics and other relevant metrics such as customer activity, revenue, or engagement. Clean and organize the data to ensure accuracy and consistency.
  • 4. Define Cohort Periods: Determine the time periods over which you want to analyze the cohorts. It could be weekly, monthly, quarterly, or any other interval based on the nature of your data and objectives. Cohort periods should be consistent across the analysis to ensure meaningful comparisons.
  • 5. Create Cohort Segments: Segment your customers or users into distinct cohorts based on the selected cohort characteristics. Assign each individual to their respective cohort based on their entry or event date. Meanwhile, this step involves grouping individuals who share the same characteristic within a specific time period.
  • 6. Calculate Cohort Metrics: Calculate relevant metrics for each cohort, such as customer retention rate, average revenue per user, conversion rate, or any other metric that aligns with your objectives. These metrics will provide insights into cohort behavior and performance over time.
  • 7. Visualize the Data: Create visualizations, such as cohort charts or line graphs, to represent the performance of each cohort over time. Visual representations make it easier to identify trends, patterns, and variations among cohorts.

Importance of cohort analysis

Cohort Analysis is widely used in the following verticals:

  • Ecommerce
  • Mobile Apps
  • Cloud Software 
  • Digital Marketing
  • Online Gaming

Consequently, the information derived from a Cohort Analysis graph is incredibly beneficial to companies. It helps them to analyze data and find the answers they need to customer-targeted questions.

  • Cohort Analyses can compare different cohorts at the same time period during their lifecycle. They are a reflection of change in retention over a product lifetime.
  • They allow for an understanding of long-term relationships with a given user group.
  • The data sheds light on how different marketing strategies affect the customer churn for better or for worse.
  • Cohort analysis provides a better understanding of user trends and behaviors of cohorts  that affect business metrics like acquisitions and retention.
  • A better understanding of user trends and behaviors allows you to take steps to encourage other users to follow the same behaviors you identified in other cohorts. 
  • Cohort Analyses ensure more effective customer engagement that leads to optimized conversion rates. Cross-selling opportunities arise when you understand customer interaction towards marketing strategies and towards certain aspects of your product or service.

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Hopefully, this article was informational and useful for you! Don’t hesitate to leave your impressions and comments below!