Running Count Distinct using Over Partition By

As a data analyst, I’ve encountered various challenges while working with large datasets. One such challenge is running a count of distinct users who have made purchases over time, partitioned by state and country. In this article, we’ll explore how to achieve this using the OVER clause in SQL.

Background

When working with large datasets, it’s essential to consider data aggregation techniques that can efficiently handle complex queries. The OVER clause is one such technique that allows us to perform calculations across rows based on a window of data.

In the context of running a count of distinct users who have made purchases over time, we need to consider the following requirements:

• We want to see the YTD (Year-To-Date) count of distinct users with purchase activity for each month of the year.
• The count should be partitioned by state and country.

The Problem

The given code snippet demonstrates how to run a count of distinct users who have made purchases over time, excluding the month from the view. However, this approach has a limitation when it comes to aggregating data at a monthly level and then summing up the counts.

SELECT Year, Country, State,
   COUNT(DISTINCT (CASE WHEN ActiveUserFlag > 0 THEN MBR_ID END)) AS YTD_Active_Member_Count
FROM MemberActivity
WHERE Month <= 5
GROUP BY 1,2,3;

In this code snippet:

• We select the year, country, and state.
• We count the distinct users who have made purchases using a CASE statement.
• We group by the selected columns.

However, this approach has a limitation when it comes to aggregating data at a monthly level and then summing up the counts. The issue arises when we try to aggregate the data by month, as the count of distinct users will be duplicated for each month.

Solution

To solve this problem, we can use the OVER clause in SQL to partition the data by year, country, and state. We’ll also use a cumulative sum to calculate the YTD (Year-To-Date) count of distinct users with purchase activity for each month of the year.

SELECT Year, Country, State, month,
   SUM(cnt)
   OVER (PARTITION BY Year, Country, State
         ORDER BY month
         ROWS UNBOUNDED PRECEDING) AS YTD_Active_Member_Count
FROM (
    SELECT Year, Country, State, month,
           COUNT(*) as cnt -- 1st purchases per month
    FROM 
        (SELECT *
            FROM MemberActivity
            WHERE ActiveUserFlag > 0 -- only active members
              AND Month <= 5
              AND year = 2019 -- seems to be for this year only
            qualify row_number() -- only first purchase per member/year
                    over (PARTITION BY MBR_ID, year
                          ORDER BY month --? probably there's a purchase_date) = 1
        ) as dt
    GROUP BY 1,2,3,4
) as dt;

In this code snippet:

• We first select the year, country, state, and month.
• We count the distinct users who have made purchases using a COUNT statement.
• We group by the selected columns.
• We use a subquery to filter out rows with no active members and only consider the first purchase per member/year.
• We use another subquery to calculate the cumulative sum of the counts.

Explanation

The key to solving this problem lies in using the OVER clause to partition the data by year, country, and state. This allows us to perform calculations across rows based on a window of data.

In this solution, we first select the year, country, state, and month. We then count the distinct users who have made purchases using a COUNT statement.

To calculate the cumulative sum, we use a subquery that groups by the selected columns. This allows us to aggregate the data at a monthly level and then sum up the counts.

The most critical part of this solution is the use of the ROWS UNBOUNDED PRECEDING clause in the OVER function. This clause specifies that we want to include all rows preceding each row in the partition.

Example Use Cases

This solution can be applied to various scenarios where you need to calculate the YTD (Year-To-Date) count of distinct users with purchase activity for each month of the year. Here are some example use cases:

• E-commerce analysis: This solution can be used to analyze customer purchasing behavior and identify trends over time.
• Marketing campaigns: By analyzing the number of unique customers who have made purchases, marketers can better understand the effectiveness of their campaigns.
• Customer retention: This solution can help businesses identify customers who are likely to churn and implement targeted retention strategies.

Conclusion

In this article, we explored how to run a count of distinct users who have made purchases over time using the OVER clause in SQL. We discussed the limitations of the given code snippet and presented a solution that uses a cumulative sum to calculate the YTD (Year-To-Date) count of distinct users with purchase activity for each month of the year.

By applying this solution, businesses can gain valuable insights into customer purchasing behavior and make data-driven decisions to drive growth.

Last modified on 2024-10-16