Post by account_disabled on Mar 12, 2024 5:25:52 GMT
Have you ever wondered why you see some promotions on Facebook and not others? It is not the system that decides which ads to show you, but the advertiser who pays for the promotional campaign, who establishes who should see a certain content. The platform has the arduous task of profiling you and deciding who you are and what interests you most. How does Facebook understand what interests you? He does this first of all by assuming that the information you provide about your age, profession, studies you have done and everything you spontaneously fill in in your profile is valid. Unfortunately, this information alone is not enough to profile you correctly. So the platform draws on your behavior to understand what interests you: a like on a certain page, the installation of an application, the brand and model of the phone you purchased, a click on a certain content and even the visit a company website.
Facebook also monitors the purchases you make through the code (Facebook pixel) that e-commerce India Mobile Number Data stores install to track conversions. Nothing escapes the analysis of the Big Data that concern us. You can check and update all this data on this splendid page, which Facebook makes available to us Profiling on objective data From the advertiser's point of view, things might appear simple, but they are not. As you too could appreciate by looking at the link I posted, Facebook takes a lot of nonsense. Personally, I found myself among the interests “Belgium”, “horses” and “Buddhism”, things to which I am not particularly close. Let's say that if for some reason you simply like a certain piece of content, from that moment, for the system, you have expressed a preference that it will resell to advertisers. Since profiling is so imprecise, the risk of losing money in promotions is quite real.
There are ways to spend less by segmenting the audience more, the first of which is to rely on real data . For example, at the beginning I profiled based on the interests of poorly described users and the lot of "noise" allowed me to achieve lower performance compared to profiling calibrated on the data. Profiling on data means sticking only to real and objective information. If we are looking for a wealthy audience we set the brand and model of the latest 1,000 euro smartphone (data deduced from the app), the educational qualification (declared by the user) or the people who travel frequently (data coming from the "check ins" carried out on the application).
Facebook also monitors the purchases you make through the code (Facebook pixel) that e-commerce India Mobile Number Data stores install to track conversions. Nothing escapes the analysis of the Big Data that concern us. You can check and update all this data on this splendid page, which Facebook makes available to us Profiling on objective data From the advertiser's point of view, things might appear simple, but they are not. As you too could appreciate by looking at the link I posted, Facebook takes a lot of nonsense. Personally, I found myself among the interests “Belgium”, “horses” and “Buddhism”, things to which I am not particularly close. Let's say that if for some reason you simply like a certain piece of content, from that moment, for the system, you have expressed a preference that it will resell to advertisers. Since profiling is so imprecise, the risk of losing money in promotions is quite real.
There are ways to spend less by segmenting the audience more, the first of which is to rely on real data . For example, at the beginning I profiled based on the interests of poorly described users and the lot of "noise" allowed me to achieve lower performance compared to profiling calibrated on the data. Profiling on data means sticking only to real and objective information. If we are looking for a wealthy audience we set the brand and model of the latest 1,000 euro smartphone (data deduced from the app), the educational qualification (declared by the user) or the people who travel frequently (data coming from the "check ins" carried out on the application).