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3 Simple Steps To Unlock The Potential Of Your Digital Marketing Data with SQL – Even If You’ve Tried Before and Failed

Today, I’m sharing three simple steps to unlock the potential of your digital marketing data using SQL – even if you’ve tried before and failed.

SQL is a powerful tool that helps marketers organize and understand big sets of data, and makes their job easier and more effective.

  • SQL helps marketers organize and understand big sets of data
    For example, a digital marketer might use SQL to figure out what kinds of things customers like and don’t like, or to group customers into different categories.
  • SQL can be used to bring together data from different places and make it all work together
    This can help marketers get a complete picture of what their customers are doing and how they’re feeling.
  • SQL can be used to make marketing tasks easier and faster
    For example, a digital marketer might use SQL to create a program that sends personalized emails to customers based on their preferences and behavior.

Unfortunately, many marketers don’t take the time to learn SQL:

  • Lack of time and resources
    Learning a new programming language can be time-consuming and require a significant investment of time and resources. Digital marketers who are already stretched thin with other responsibilities may not have the time or resources to devote to learning SQL.
  • Their current process is “good enough”
    Some digital marketers may not see the value in learning SQL, particularly if they are already using other tools and technologies that allow them to effectively access and analyze their data. In this case, they may not see the need to invest the time and effort required to learn SQL.
  • They don’t have the experience or interest
    Some digital marketers may not have any previous experience with programming languages, and may not be interested in learning SQL. In this case, they may not see the benefits of learning SQL and may prefer to focus on other areas of their job.

SQL is worth learning for digital marketers because SQL makes it easier to work with marketing data.

This guide will walk you through the process of using SQL step-by-step.

Just remember:

  1. Take it one step at a time.
  2. Ask for help if you need it.
  3. Practice, practice, practice.

Step 1: Download and install a SQL database management system (DBMS)

Getting your DBMS set up is crucial for unlocking the potential of digital marketing data using SQL.

This will allow you to store and manage your digital marketing data, and use SQL to query and analyze it. Some popular options for DBMS include MySQL, PostgreSQL, and Microsoft SQL Server.

I recommend using MySQL. Here’s how to install it.

  1. Visit the MySQL website and download the MySQL installer for Windows or Mac.
  2. Run the installer and follow the on-screen instructions to complete the installation. This will typically involve selecting which components of MySQL you want to install, as well as setting a password for the root user.
  3. Once the installation is complete, launch the MySQL Workbench application. This will allow you to connect to the MySQL server and start using SQL to manage and manipulate your data.

By installing a DBMS, we’ll be able to import your digital marketing data and begin using SQL to gain valuable insights.

Step 2: Import your digital marketing data into the DBMS

This is an important step because it will allow you to use SQL to query and analyze your data.

There are a few different ways you can import your data, depending on where it’s stored and what format it’s in. For example, if your data is in a CSV or Excel file, we can use the MySQL Workbench to import it directly.

How to import data from a CSV file into MySQL:

  1. Launch the MySQL Workbench application and connect to your MySQL server.
  2. Select the schema (database) where you want to import the data, and then click on the “Table Data Import Wizard” option.
  3. In the wizard, select the CSV file that you want to import and specify the options for how the data should be imported, such as the delimiter and data type for each column.
  4. Preview the data to make sure it looks correct, and then click “Finish” to import the data into the selected schema.

Or, if your data is stored in a CRM or marketing automation platform, we can use a connector or integration tool to pull the data into your DBMS.

How to use an online connector tool like Zapier to import data from a CRM platform into MySQL:

  1. Sign up for a Zapier account and connect it to your CRM platform and MySQL server. This will typically involve providing your login credentials for both systems and allowing Zapier to access your data.
  2. Use the Zapier interface to create a “zap” that will automatically import data from your CRM platform into MySQL. This will typically involve selecting the data that you want to import and specifying the target schema (database) in MySQL where the data should be imported. For example, you can use Zapier to import new Facebook Lead Ads respondents to MySQL or Typeform entries to MySQL.
  3. Test the zap to make sure it’s working correctly and importing the data as expected. Once you’re satisfied with the results, you can turn on the zap to automatically import data from your CRM platform into MySQL on a regular basis.

Once your data is imported, we’re ready to start using SQL to gain insights and make data-driven decisions.

Step 3: Use SQL to query, manipulate, and analyze your digital marketing data.

This is an important step because it will allow us to gain valuable insights and make data-driven decisions.

For example, we can use SQL to filter your data based on certain criteria, such as the type of product or the location of the customer.

-- Filter data by product type
SELECT *
FROM purchases
WHERE product_type = 'tablet'

-- Filter data by customer location
SELECT *
FROM purchases
WHERE customer_location = 'San Francisco'

-- Filter data by multiple criteria
SELECT *
FROM purchases
WHERE product_type = 'tablet'
AND customer_location = 'San Francisco'

In these examples, the WHERE clause is used to specify the criteria for filtering the data. The SELECT statement is used to retrieve the data that matches the specified criteria.

  • In the first example, the query filters the data to only include purchases of tablets.
  • In the second example, the query filters the data to only include purchases by customers in San Francisco.
  • In the third example, the query filters the data to only include purchases of tablets by customers in San Francisco.

We can also use SQL to perform calculations on your data, such as calculating the average purchase price or the total number of purchases.

-- Calculate average purchase price
SELECT AVG(purchase_price)
FROM purchases

-- Calculate total number of purchases
SELECT COUNT(*)
FROM purchases

-- Calculate total sales revenue
SELECT SUM(purchase_price)
FROM purchases

In these examples, the SELECT statement is used to retrieve the data that will be used in the calculation. The AVGCOUNT, and SUM functions are used to perform the calculations on the data.

  • In the first example, the query calculates the average purchase price by using the AVG function.
  • In the second example, the query calculates the total number of purchases by using the COUNT function.
  • In the third example, the query calculates the total sales revenue by using the SUM function. These examples show how SQL can be used to easily and efficiently perform calculations on digital marketing data.

Finally, we can use SQL to group and summarize your data, such as by grouping customers into different segments based on their behavior and preferences.

-- Group customers by purchase frequency
SELECT COUNT(purchase_id) AS num_purchases, customer_id
FROM purchases
GROUP BY customer_id

-- Group customers by product type
SELECT COUNT(purchase_id) AS num_purchases, customer_id, product_type
FROM purchases
GROUP BY customer_id, product_type

-- Group customers by location and product type
SELECT COUNT(purchase_id) AS num_purchases, customer_id, customer_location, product_type
FROM purchases
GROUP BY customer_id, customer_location, product_type

In these examples, the GROUP BY clause is used to group the data by one or more columns. The SELECT statement is used to retrieve the data that will be used in the grouping and summarization.

  • In the first example, the query groups the data by customer ID, and calculates the number of purchases made by each customer.
  • In the second example, the query groups the data by customer ID and product type, and calculates the number of purchases made by each customer for each product type.
  • In the third example, the query groups the data by customer ID

Overall, using SQL to unlock the potential of your digital marketing data is an essential step in making data-driven decisions and creating effective marketing campaigns.

That’s it for today – I hope you enjoyed it.


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  3. If you want actionable data analytics advice on your specific situation, book a 1:1 coaching session with me today.

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