How to Calculate Percentage of New Users Per Day Using SQL Queries

Learn how to effectively query your database to extract the `percentage of new users per day`, even when users might not appear daily.
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Understanding the Problem

In the world of databases and user activity tracking, calculating the percentage of new users on a daily basis can be a necessity for web applications and services. This calculation is crucial for understanding user growth trends. Let’s explore this further through a practical example.

Imagine you have a table that tracks user activity. Users might not perform any actions every day; thus, simply counting distinct users can lead to misleading statistics. For example, if only returning users are active on a specific day, the monthly growth insights would be obscured.

The goal is to query this data to return the percentage of new users for each day, showcasing total active users and highlighting days where no new users were registered.

Sample Table Structure

Consider the following simplified table named tbl that logs user IDs and the date they last interacted with the system:

USERIDupdated_date1112023-01-012222023-01-011112023-01-033332023-01-073332023-01-093332023-01-10From this data, we want to generate a report that shows the date, total users for that date, and the percentage of new users compared to previous days.

Crafting the Solution

We can achieve the desired output using SQL analytic functions along with case expressions. We will break down the steps required to construct this SQL query effectively.

SQL Query Breakdown

The SQL to deliver the required report is structured as follows:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the SQL Query

Sample Data: We start by creating a table using common SQL pattern to hold sample records (in this case, user ID and their last updated date).

CTE (Common Table Expression): The WITH clause defines our source data, and the main query follows.

Counting Users: The SUM() function calculates the total of new users while ROW_NUMBER() helps identify new unique users based on their activity.

Calculating Percentages: We calculate two percentage columns:

New vs Previous (%): Percentage of new users compared to the number of users from the previous date.

New vs Total (%): Percentage of new users based on the overall total users till the current date.

Sample Output

From the above SQL, the output will look like this:

A_DATEPREVIOUS_USERSNEW_USERSNEW_VS_PREVIOUS_PCTTOTAL_USERSNEW_VS_TOTAL_PCT01.01.232100210003.01.232002007.01.232150333.3309.01.233003010.01.2330030Conclusion

The ability to track and analyze user growth is invaluable for decision-making and strategy development. The SQL query provided not only addresses the requirement to calculate daily new user percentages but also considers the core complexities involved with user activity tracking in databases.

By implementing this SQL solution, you can gain deeper insights into user engagement and growth trends over time. Happy querying!