SQL for PMs - Turning Data into Actionable Product Insights

Data Analysis using SQL is a powerful tool for Product Managers - theproductuniversty.com
Data Analysis using SQL is a powerful tool for Product Managers - theproductuniversty.com

In today's data-centric world, product managers rely on insights to guide their decisions. SQL is a crucial tool for accessing and analyzing data, allowing them to extract valuable information that informs product strategy and development. Let's explore basic SQL syntax and how SQL skills can benefit product managers and provide practical examples of its application in real-world scenarios.

What is SQL

SQL, or Structured Query Language, is a standardized programming language primarily used for managing and querying relational databases. It helps organize and find information stored in databases, like finding specific data or adding new information. SQL helps teams keep their data organized and easy to find, which helps them make better decisions. Lots of different databases use SQL, so it's a handy tool that works in many places, making it useful for anyone who deals with data.

Key SQL Concepts

Tables and Databases

In SQL, data is organized into tables, which are structured collections of related information. Each table consists of rows and columns, where each row represents a unique record, and each column represents a different attribute or field of the data. On the other hand, databases are containers that hold one or more tables along with other database objects.


A SQL query is a request for specific information from a database. It typically consists of one or more SQL commands that specify what data to retrieve and how to retrieve it. Queries can range from simple requests for individual records to complex operations involving multiple tables and conditions.

SELECT Statement

The SELECT statement is one of the fundamental SQL commands used to retrieve data from a database. It allows you to specify which columns to include in the query result and which table(s) to retrieve the data from. The basic syntax of a SELECT statement is SELECT and usually followed by the column names you want

SELECT first_name, last_name
FROM employees;

SELECT Query example for SQL - theproductuniversity.com

This query retrieves the first name and last name of all employees from the "employees" table.

WHERE Clause

The WHERE clause is used to filter rows returned by a SELECT statement based on specified criteria. It allows you to narrow down the results to only those rows that meet certain conditions.

FROM customers
WHERE country = 'USA';

WHERE syntax example for SQL - theproductuniversity.com

This query retrieves all columns from the "customers" table where the country is 'USA'.


JOINs are used to combine rows from two or more tables based on related columns between them. There are different types of JOINs, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN, each serving a different purpose in retrieving data from multiple tables.

SELECT customers.customer_id, orders.order_id, orders.order_date
FROM customers
INNER JOIN orders ON customers.customer_id = orders.customer_id;

JOIN Query example for SQL - theproductuniversity.com

This query retrieves the customer ID, order ID, and order date from the "customers" and "orders" tables, joining them based on the common "customer_id" column.

Few More Capabilities of SQL

Filtering and Sorting Data

SQL allows us to filter and sort data to focus on specific subsets or to organize data in a meaningful way. For example, filtering orders by region or sorting customer feedback by date can provide valuable insights into regional preferences or temporal trends.

Aggregating Data

SQL provides functions for aggregating data, such as SUM, AVG, COUNT, and MAX, which allow product managers to calculate metrics and perform statistical analysis. Aggregating data can help in summarizing information, identifying trends, and measuring performance indicators.

Grouping Data

Grouping data in SQL allows product managers to analyze information at various levels of specificity. For instance, grouping sales data by product category or customer segment can reveal patterns and insights that might not be visible at the individual transaction level.

Real-World Examples Illustrating SQL Queries

These examples demonstrate how SQL queries can be applied in real-world scenarios across different industries, including entertainment (Netflix), technology (Google), and e-commerce (Amazon), to extract valuable insights from data.


Example 1: This query calculates the average rating for each movie genre in the Netflix database, providing insights into viewer preferences across different genres.

```SELECT genre, AVG(rating) AS average_rating
FROM movies
GROUP BY genre;

Example 2: This query counts the total number of views per month for a specific user (user ID = '123'), allowing Netflix to track user engagement over time and tailor content recommendations accordingly.

SELECT month(watch_date) AS watch_month, COUNT(*) AS total_views
FROM user_activity
WHERE user_id = '123'
GROUP BY month(watch_date)
ORDER BY watch_month;


Example 1: This query identifies the top 10 most frequently searched keywords in Google's search engine, helping Google understand popular search trends and improve search result relevance.

SELECT keyword, COUNT(*) AS search_volume
FROM search_queries
GROUP BY keyword
ORDER BY search_volume DESC

Example 2: This query calculates the average number of ad clicks per country in Google's advertising campaigns, enabling Google to assess the effectiveness of its advertising efforts across different regions.

SELECT country, AVG(ad_clicks) AS average_clicks
FROM ad_campaigns
GROUP BY country;


Example 1: This query identifies the top 5 best-selling product categories on Amazon by calculating the total quantity of units sold for each category, allowing Amazon to prioritize product restocking and marketing efforts accordingly.

SELECT category, SUM(quantity_ordered) AS total_units_sold
FROM orders
JOIN products ON orders.product_id = products.product_id
GROUP BY category
ORDER BY total_units_sold DESC

Example 2: This query identifies customers on Amazon who have placed more than 5 orders, indicating loyal customers who may be eligible for loyalty rewards or targeted marketing campaigns.

SELECT customer_id, COUNT(DISTINCT order_id) AS total_orders
FROM orders
GROUP BY customer_id

Pitfalls to Avoid While Writing SQL Queries as Beginners

Forgetting to Specify Conditions with WHERE: Forgetting to include a WHERE clause when retrieving data can result in returning the entire table, which may overwhelm your system and slow down query execution. Always specify relevant conditions in the WHERE clause to filter the data you need.

Overusing SELECT * : While it's tempting to use "SELECT *" to retrieve all columns, it's better to explicitly list the columns you need. This not only makes your queries more efficient by reducing unnecessary data retrieval but also improves readability for yourself and others.

Not Using Aliases for Column Names: When working with multiple tables or performing calculations, it's important to use aliases to rename columns in your query results. This makes the output more understandable and easier to work with, especially for complex queries.

Forgetting to Add JOIN Conditions: When joining tables together, forgetting to specify the conditions on which the tables should be joined can lead to incorrect or unintended results. Always ensure you have appropriate JOIN conditions to link the related data from different tables.

Ignoring Data Type Conversion: SQL is strict about data types, and mixing incompatible data types in expressions or comparisons can lead to unexpected results or errors. Be mindful of data types and use appropriate conversion functions when necessary to ensure compatibility.

Not Testing Queries Before Running Them: Before executing complex or critical queries, it's essential to test them using sample data or in a test environment. This helps catch syntax errors, logic issues, or unintended consequences before affecting the production environment.

Using Ambiguous Column Names: When working with multiple tables, be careful to use unambiguous column names in your queries. If column names are not unique across tables, specify the table alias or use table. column notation to clarify which column you're referring to.

Overlooking Case Sensitivity: SQL is case insensitive by default, but this may vary depending on the database system. Avoid relying on case sensitivity for identifiers or comparisons unless necessary, and be consistent in your casing to avoid confusion.

If Nothing Else, Remember This😉

  • SQL empowers product managers to extract valuable insights from data to drive informed decisions.
  • Not considering the context in data analysis can lead to flawed conclusions
  • Use aliases for table names and columns to improve query readability.
  • Always use explicit column names instead of "SELECT *", for clarity and efficiency.
  • Remember to include WHERE clauses to filter data and avoid returning unnecessary rows.

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