<p>The pursuit of studies related to the parameterization tool, which offers advantageous gains in analyzing data in uncertain contexts, continues to hold great importance. This research revolves around the parameterization tool originally introduced within the framework of the soft set model. In the context of a soft set, parameterization involves the processing of objects corresponding to specific parameters. This study aims to not only identify the most optimal object, but also to explore techniques for determining the best parameter-object pair. To achieve this, the concept of (inverse) parametric objective relational membership function is introduced, providing a numerical representation of (inverse) parametric objective interactions. Thus, by identifying the best parameter-object pair, it becomes feasible to determine the parameter that exhibits the strongest interaction with a given object in data analysis. Furthermore, novel algorithms based on (inverse) parametric objective interactions are developed to address constraints encountered in decision-making problems. These algorithms introduce innovative gains that encourage a reevaluation of existing decision-making approaches.</p>