aim of the present paper is to look into whether, by using network representations and exploiting network properties, platforms could use data acquired from customers to design even more efficient price discrimination practices, or to better nudge and steer consumers towards their purchases. It appears indeed that, by letting information flow throughout the entire network, and thus taking into account the preferences of nodes which are closer to the targeted one (i.e. its [*alters*]{}), it would be possible for platforms to capture a wider amount of information compared to a situation where individuals are divided into clusters as it currently generally happens. As a matter of fact, considering the whole network and not just its subsets (thought clustering techniques, thereby items and/or subjects are divided into clusters, being considered as a portion of the entire universe) it is reasonable to think that the correspondingly greater amount of information, collected in the entire universe, could lead to better profiling results.

The theoretical proposal offered in the present paper is mainly based on the concept of [*network value*]{}[^1], which has been used in order to estimate the information available within the system, as well as the way how platforms exploit data stemming 