Cohort analysis reporting refers to the process of generating and presenting insights derived from analyzing groups of users (cohorts) who share common characteristics or experiences within a defined period. This analysis helps product managers understand user behavior, engagement, and retention to inform product development and marketing strategies.

Cohort analysis is an indispensable tool for product managers to understand user behavior, engagement, and retention. By segmenting users into cohorts, product managers can identify trends, measure the impact of product changes, and make data-driven decisions. In this comprehensive guide, we'll explore the concept of cohort analysis, discuss different calculation methods, provide examples and case studies, and delve into SQL queries for cohort analysis.

What is Cohort Analysis?

Cohort analysis is a technique used to analyze the behavior and performance of groups of users who share a common characteristic or experience within a defined period. These groups, or cohorts, allow product managers to compare how different segments of users interact with the product and identify patterns that can inform product development and marketing strategies.

The most common type of cohort analysis is time-based, where users are grouped based on when they started using the product or completed a specific action. However, cohorts can also be defined based on other attributes, such as user demographics, acquisition channels, or user behaviors.

Why Cohort Analysis Matters for Product Managers

Cohort analysis provides several key benefits to product managers, including:

1. Improved retention insights - by examining cohorts, product managers can identify trends and factors that contribute to user churn, enabling them to implement strategies to improve user retention.
2. Impact of product changes - cohort analysis helps product managers measure the effect of product updates, new features, or marketing campaigns on user behavior and engagement.
3. Segmentation for personalization - cohorts can inform personalization strategies by identifying unique patterns or preferences within specific user groups, leading to a better user experience.
4. Benchmarking performance - comparing cohorts allows product managers to establish performance benchmarks and identify best practices that can be replicated across other user segments.

How to do cohort analysis

There are various methods to calculate cohort metrics depending on the data available and the insights required. Here are some common cohort analysis calculation methods:

1. Retention rate cohort analysis - this method measures the percentage of users within a cohort who continue using the product over time. The retention rate can be calculated by dividing the number of active users in a cohort by the cohort's initial user count.
2. Revenue cohort analysis - this method tracks the revenue generated by each cohort over time, providing insights into the cohort's lifetime value (LTV) and allowing product managers to identify high-value user segments.
3. Event-based cohort analysis - this method groups users based on specific events or actions they have taken within the product, such as completing an onboarding process or making an in-app purchase. This analysis can help product managers understand user behavior and optimize the product for specific user actions.
4. Conversion rate cohort analysis - this method measures the percentage of users within a cohort who complete a desired action or reach a specific goal, such as upgrading to a premium plan or inviting a friend to join. Conversion rate analysis can inform product and marketing strategies to drive user growth and engagement.

Examples and case studies

To illustrate the power of cohort analysis, let's consider a few examples and real world case studies:

Example 1 - measuring the impact of an onboarding redesign

A product manager redesigns the onboarding process for their app, aiming to improve user engagement and retention. By comparing retention rates for cohorts before and after the redesign, the product manager can measure the impact of the changes and identify areas for further optimization.

Case Study - how Slack improved user retention

Slack, a popular collaboration platform, used cohort analysis to identify a key user behavior that correlated with long-term retention. They found that users who connected with at least 10 other team members within the first week were more likely to become long-term users.

Armed with this insight, Slack focused on improving the onboarding experience to encourage users to make more connections early on, ultimately leading to better user retention.

Example 2 - analyzing the effectiveness of a marketing campaign

A product manager launches a marketing campaign to attract new users to their platform. By creating cohorts based on the acquisition channel and tracking the retention and conversion rates of each cohort, the product manager can determine the effectiveness of different marketing channels and optimize their marketing budget allocation.

Case study - how Airbnb optimized host acquisition

Airbnb used cohort analysis to optimize their host acquisition strategy. By analyzing the performance of different host cohorts, they discovered that hosts who received bookings within the first month of listing their property were more likely to remain active on the platform. With this insight, Airbnb focused their marketing efforts on acquiring hosts in high-demand areas, ultimately improving host retention and platform growth.

Cohort analysis SQL

SQL (Structured query language) is a powerful tool for product managers who want to perform cohort analysis using raw data stored in databases. Using SQL for cohort analysis reporting offers several advantages for product managers and analysts:

1. Flexibility - SQL allows you to perform complex queries and custom calculations, giving you the flexibility to create tailored cohort analysis reports based on your unique requirements. You can easily modify your queries to segment users by different attributes, measure various metrics, and compare cohorts over different time periods.
2. Scalability - SQL is designed to work with large datasets and can handle millions of rows of data without compromising performance. This makes SQL an ideal choice for cohort analysis reporting, as it can efficiently process large amounts of user data to generate meaningful insights.
3. Data accuracy - by working directly with raw data stored in databases, SQL ensures that your cohort analysis reports are based on accurate, up-to-date information. This is crucial for making informed product decisions and tracking the impact of your strategies over time.
4. Integration - SQL is widely supported by many database management systems and business intelligence tools, making it easy to integrate cohort analysis reports into your existing data infrastructure. You can export SQL query results to various formats, such as CSV or Excel, for further analysis or visualization.
5. Reusability - SQL queries can be saved and reused, enabling you to automate cohort analysis reporting and save time on manual data processing. You can also create templates for different cohort analysis scenarios, making it easy to generate reports for various use cases or stakeholders.
6. Cost-effectiveness - SQL is a standard language supported by many open-source and commercial database systems. By using SQL for cohort analysis, you can leverage your existing data infrastructure and avoid the need for additional specialized tools or software, reducing costs and complexity.

In this section, we'll provide a step-by-step guide to conducting time-based cohort analysis using SQL queries. Assuming you have a database table called 'users' with the following columns:

• user_id - Unique identifier for each user
• signup_date - Date when the user signed up
• last_activity_date - Date of the user's last activity

Step 1 - create cohorts based on signup date

To group users into cohorts based on their signup date, use the following SQL query:

SELECT
    user_id,
    DATE_TRUNC('month', signup_date) AS cohort_month
FROM
    users;

This query truncates the signup_date to the month and assigns each user to a cohort_month.

Step 2 - calculate retention period

Next, calculate the retention period for each user by finding the difference between their last_activity_date and signup_date:

SELECT
     user_id,
     cohort_month,
     EXTRACT(month FROM AGE(last_activity_date, signup_date)) AS retention_period
FROM
     ( -- Step 1 query here
     ) cohort_data;

Step 3 - calculate retention rates

Finally, calculate the retention rates for each cohort by dividing the number of active users in each retention period by the total number of users in the cohort:

SELECT
     cohort_month,
     retention_period,  
     COUNT(user_id) * 100.0 / COUNT(DISTINCT user_id) OVER (PARTITION BY cohort_month) AS retention_rate
FROM
     ( -- Step 2 query here
     ) retention_data
GROUP BY
     cohort_month,
     retention_period
ORDER BY
     cohort_month,
     retention_period;

This query calculates the retention rate for each cohort and retention period, allowing you to analyze user retention trends over time.

In conclusion, cohort analysis is a powerful technique for product managers to gain insights into user behavior, retention, and engagement. By segmenting users into cohorts, product managers can identify trends, measure the impact of product changes, and make data-driven decisions. By leveraging examples and case studies, and mastering SQL queries for cohort analysis, product managers can unlock the full potential of cohort analysis to drive product success and create better user experiences.