the collection of habits and preferences, in the light the unprecedented capacity of online platforms to collect digital footprints left by individuals [@fuller2018privacy; @acquisti2008identity], even considering constraints such as clustering limitation. In such sense, due to the presence of some constraints, it is improper to assert – contrary to what claimed by a wide part of the literature (Council of Economic Advisers - CEA, 2015) – that we are assisting to a shift from third-degree price discrimination to totally personalised prices just because of the increased availability of behavioural data. There are indeed at least three obstacles – in addition to the fact that platforms do not implement similar strategies due to reputation motivation as reported in @CouncilofEconomicAdvisers-CEA2015 – standing between perfect price discrimination and the forms of discrimination that platforms currently implement [@Ezrachi2016 pp. 96-99]: Lack of data[^13]; Irrationality[^14]; Sufficient sample size[^15]. In the light of such obstacles, platforms must put in place practical solutions so that preferences can be detected and reserve prices – which are not directly deducible – approximated [@Shiller2015] [@Ezrachi2016 pp. 96][^16]. Starting from the purchase behaviour patterns charactering each group to which individuals have been allocated to, platforms predict consumers’ desires and “their next 