With the widespread adoption of renewable energy systems in residential buildings, particularly in the context of collective self-consumption groups (CSC) and Renewable Energy Communities (REC), understanding user behavior becomes pivotal for enhancing energy efficiency and increasing the energy share among participants for an optimal use of renewable resources. Regardless of which configuration is adopted (CSC or REC), a key aspect is how to share the generated economic benefits from the self-produced energy and identify the fairest way to distribute the incentive derived from the shared energy among users. In this context, the aim of this work is to introduce a data-driven energy benchmarking process that leverages the analysis of long-term monitoring data of residential buildings to i) characterize energy consumption patterns of users over time, ii) support the development of an optimal incentive sharing mechanism among users involved in such legal entities. The proposed approach is tested on a monitored residential building, located in Northern Italy, which includes 13 flats and is equipped with a centralized photovoltaic system.