The primary goal of personalized marketing is to engage customers by tailoring content to their particular needs and characteristics.

This has great benefits for both companies and customers, as when we segment the market properly, the customer feels valued by the company, which achieves more sales, visibility and loyalty.

However, it is not always easy to build a message that is personalized without going beyond invasive limits. Today we cover personalized marketing and data collected by companies using this marketing strategy.

From consumer behaviour to predictive analytics, companies regularly store and analyze large amounts of user data on a daily basis. These data, which are mostly demographic and behavioural data, are obtained in various ways, some more technical and some more deductive.

The top three ways to get this data are: by asking customers directly, analyzing their behaviour indirectly, or integrating other data sources into theirs.

A common example of using user data is location-based advertising, which uses the IP address of a device connected to the Internet to create a custom data profile. This information is then used to target users' devices with relevant and personalized advertising.

Brands can also deepen their own customer purchase history records to see how customers have interacted with the brand before. In this way, they are incorporating direct feedback on what worked and what didn't work with that particular customer.

Of course, capturing large amounts of data is not enough without extensive analysis. To do this, brands rely on certain software and artificial intelligence to help them analyze and automate the data collected and turn it into useful information for their personalized marketing strategies.