Querying Sales Data: Finding Top-Performing Salesmen by Year

Introduction

In this article, we’ll explore a real-world problem where we need to identify top-performing salesmen by year. We’ll dive into SQL queries and database design to achieve this goal.

Background

The problem statement is based on a common scenario in business intelligence and data analysis. Suppose we have a table containing sales data for different products and salesmen. Our task is to find the list of salesmen who had more sales than the average sales for each year.

Database Design

Before we jump into the SQL query, let’s discuss the database design. We’ll assume that we have a table named Sales_temp with the following columns:

• salesman: The ID of the salesman.
• sale: The total sales amount for the year.
• year: The calendar year.

We also need to create an auxiliary table to store the average sales for each year. This will be used in our SQL query.

CREATE TABLE Average_Sales (
    year INT,
    avg_sale DECIMAL(10, 2)
);

INSERT INTO Average_Sales (year, avg_sale)
SELECT YEAR, AVG(sale) AS avg_sale
FROM Sales_temp
GROUP BY YEAR;

SQL Query

Now that we have our database design and auxiliary table in place, let’s write the SQL query to find the top-performing salesmen by year.

We’ll use an INNER JOIN with the Average_Sales table to join on the year column. We’ll then filter out rows where the salesman’s total sales are less than or equal to the average sales for that year.

SELECT salesman, sale, year
FROM Sales_temp
INNER JOIN 
(
    SELECT year ayear, AVG(sale) asale
    FROM Sales_temp
    GROUP BY year
) atbl
ON year = ayear AND sale > asale
ORDER BY year, salesman;

Explanation

Let’s break down the query step by step:

1. We start by selecting all columns (salesman, sale, and year) from the Sales_temp table.
2. We then join this table with the Average_Sales table on the year column using an INNER JOIN. This ensures that we only consider rows where the salesman’s total sales are greater than the average sales for that year.
3. The subquery in the INNER JOIN clause calculates the average sales for each year by grouping the Sales_temp table by the year column and averaging the sale values.
4. We then filter out rows where the salesman’s total sales are less than or equal to the average sales for that year by adding the condition sale > asale.
5. Finally, we sort the results by the year column in ascending order and then by the salesman column in ascending order.

Code Explanation

The SQL query uses several key concepts:

• INNER JOIN: This type of join combines rows from two or more tables where there is a common column between them.
• Subquery: A subquery is a query nested inside another query. In this case, the subquery calculates the average sales for each year and returns it as a table alias (atbl).
• Alias: An alias gives a temporary name to a table or subquery in SQL. In this case, we use the alias atbl to refer to the subquery.
• Filter condition: The filter condition sale > asale ensures that only rows where the salesman’s total sales are greater than the average sales for that year are included in the results.

Conclusion

In this article, we explored a common problem in business intelligence and data analysis: finding top-performing salesmen by year. We discussed database design, auxiliary tables, and SQL queries to achieve this goal. By understanding these concepts and using them effectively, you can improve your skills in querying and analyzing large datasets.

Best Practices

Here are some best practices for querying and analyzing data:

• Use indexes: Indexes can significantly speed up query performance by allowing the database to quickly locate specific data.
• Optimize queries: Optimize your SQL queries by reducing the number of joins, using efficient join types, and minimizing the use of subqueries.
• Regularly maintain your database: Regularly back up your database, update statistics, and perform other maintenance tasks to ensure optimal performance.

Common Mistakes

Here are some common mistakes to avoid when querying and analyzing data:

• Inefficient joins: Using inefficient join types or joining too many tables can slow down query performance.
• Using unnecessary subqueries: Subqueries can be expensive in terms of performance. Try to rephrase your queries using joins or other techniques instead.
• Not indexing columns used in WHERE clauses: Failing to index columns used in WHERE clauses can lead to slow query performance.

Common SQL Terms

Here are some common SQL terms you should know:

• SELECT: Used to select data from a database table.
• FROM: Used to specify the table(s) to retrieve data from.
• WHERE: Used to filter data based on conditions.
• JOIN: Used to combine data from two or more tables.
• INNER JOIN: A type of join that returns only rows where there is a match between the tables.
• LEFT JOIN: A type of join that returns all rows from the left table and matching rows from the right table.
• RIGHT JOIN: A type of join that returns all rows from the right table and matching rows from the left table.

Last modified on 2023-10-06