The question is asking to analyze the retention rate of users who signed up for a product on a weekly basis. This involves identifying the cohort of users who signed up in a particular week and tracking their engagement in the subsequent weeks. The expected outcome is a table or chart showing the retention rates of each cohort in each subsequent week.

In data analysis, a cohort is a group of people who share a common characteristic or experience within a defined period. Cohort analysis is a method of analyzing the behavior of groups of users over time, by identifying the cohort they belong to and comparing their behavior against other cohorts. It is a powerful tool for understanding trends, patterns, and opportunities for improvement in various business and social settings.

A user-signup cohort refers to a group of users who signed up for a product or service during a particular time period. The retention of a user-signup cohort can be tracked by monitoring the percentage of users who continue to use the product or service over a specific period of time after they initially sign up. This type of analysis can provide insight into the effectiveness of marketing campaigns, user onboarding processes, and product updates.

To solve this question in Excel, one can use pivot tables to aggregate the data by week and cohort. The COUNTIFS function can be used to count the number of users who signed up in a particular week and are still active in subsequent weeks. The retention rate can then be calculated as the ratio of active users in a given week to the total number of users in the cohort.

In SQL, one can use GROUP BY to aggregate the data by week and cohort. The COUNT function can be used to count the number of users in each cohort and the SUM function can be used to count the number of active users in each subsequent week. The retention rate can then be calculated as the ratio of active users in a given week to the total number of users in the cohort.

The expected outcome is a table or chart showing the retention rates of each cohort in each subsequent week

Case study 2