The more data we have about our customer base, the more relevant, personalized and user-friendly our communication can be. However, it will be of togo business email list no use to have all this data if we do not have a common place where it is accessible and a reliable method to consult it.
Modern marketing management platforms (Email Service Providers and Marketing Clouds) are designed to be the common place where data from different sources is hosted and consulted. Thus, they have become very powerful marketing tools.
On these platforms we can use our own segmentation system (create filters and filter combinations). The most common “user friendly” segmentation functions are logical operators of the type:
AND : Analyzes two conditions and returns only values where both are true.
OR : Analyzes two conditions and returns values where one of the two is true.
NOT : returns the opposite value of the expression that is posed.
IN : Specifies a relationship of certain values.
However, as we move forward in creating finer and more specific segments, the need to “cross” tables arises. On my marketing platform I can host and update, for example:
A table from the e-commerce with a list of all buyers.
A table from the e-commerce with all the transactions for each user (the date, the amount and the code of the purchased item).
The “deliverable/undeliverable” status of an email in the list of users subscribed to the email channel.
The date on which the user subscribed to the email channel.
Let's say I need to create an email communication aimed at building loyalty among users who have purchased in the last 30 days of the previous month, with an annual spending of between €80 and €200, who have subscribed to the email marketing program in the last 7 days and who are opt-in. In this case I will need to consult and cross-reference tables.
It is at this point that it will be very useful to resort to SQL. One of the main operations of this language will be the “JOIN” union. It will allow columns from several tables to be displayed as if they were one, combining related records together using foreign keys.
On the other hand, we can use SQL statements that will provide more flexibility to data management from marketing:
SQL aggregate functions , which will return a single value calculated from the values in a column.
GROUP BY – It is a statement that is closely linked to aggregate functions.
These types of functions will be very useful for segmenting a database based on user value.
SQL scalar functions that will return a single value based on the input value.
They will be very useful, for example, to ensure the correct format of content personalization variables when there are doubts about the format at the time of capture.
While writing SQL queries has traditionally been the domain of data engineers and analysts, learning to write basic SQL queries is one way for any marketer to gain better segmentation and insights. On the other hand, most technology vendors today offer the ability to fire off SQL queries against different tables.