Real-World Analytics: Cohort and Retention Analysis
Understanding customer retention is crucial for any organization that wants to grow and maintain a loyal user base. Cohort analysis is a powerful technique that groups users based on shared characteristics or events, such as their signup month, and then tracks their behavior over time. Using window functions in SQL, you can efficiently assign users to cohorts and calculate retention rates, giving you actionable insights into customer behavior patterns.
12345678-- Assigning cohort labels using FIRST_VALUE to find each employee's hire date (cohort) SELECT employee_id, name, department, hire_date, FIRST_VALUE(hire_date) OVER (PARTITION BY employee_id) AS cohort_date FROM employees;
To perform retention analysis, you typically want to know how many users from each cohort are still active or have performed certain actions in subsequent time periods. By using windowed counts and comparing dates, you can calculate retention rates for each cohort. For example, you might count how many employees hired in each month received a performance review in later months, and then express this as a percentage of the original cohort size.
123456789-- Calculating monthly retention: for each hire month (cohort), count employees who received reviews in later months SELECT DATE_TRUNC('month', e.hire_date) AS cohort_month, DATE_TRUNC('month', p.review_date) AS activity_month, COUNT(DISTINCT e.employee_id) AS retained_employees FROM employees e JOIN performance p ON e.employee_id = p.employee_id GROUP BY cohort_month, activity_month ORDER BY cohort_month, activity_month;
1. What is a cohort in analytics, and how can window functions help identify them?
2. Which window function is useful for assigning cohort labels?
3. Fill in the blank to assign each employee their cohort date:
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Understanding customer retention is crucial for any organization that wants to grow and maintain a loyal user base. Cohort analysis is a powerful technique that groups users based on shared characteristics or events, such as their signup month, and then tracks their behavior over time. Using window functions in SQL, you can efficiently assign users to cohorts and calculate retention rates, giving you actionable insights into customer behavior patterns.
12345678-- Assigning cohort labels using FIRST_VALUE to find each employee's hire date (cohort) SELECT employee_id, name, department, hire_date, FIRST_VALUE(hire_date) OVER (PARTITION BY employee_id) AS cohort_date FROM employees;
To perform retention analysis, you typically want to know how many users from each cohort are still active or have performed certain actions in subsequent time periods. By using windowed counts and comparing dates, you can calculate retention rates for each cohort. For example, you might count how many employees hired in each month received a performance review in later months, and then express this as a percentage of the original cohort size.
123456789-- Calculating monthly retention: for each hire month (cohort), count employees who received reviews in later months SELECT DATE_TRUNC('month', e.hire_date) AS cohort_month, DATE_TRUNC('month', p.review_date) AS activity_month, COUNT(DISTINCT e.employee_id) AS retained_employees FROM employees e JOIN performance p ON e.employee_id = p.employee_id GROUP BY cohort_month, activity_month ORDER BY cohort_month, activity_month;
1. What is a cohort in analytics, and how can window functions help identify them?
2. Which window function is useful for assigning cohort labels?
3. Fill in the blank to assign each employee their cohort date:
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