This article is devoted to the development of a machine learning statistical framework to drive company's objectives. To this end, their sales data are used to target efficiently the issues or opportunities by a ranking. We implement a permutation based model using generalized Mallows models dealing with quantitative values, considering that a ranking is a permutation. The advantage of the generalized version is the possibility to differentiate the cost to move each element in the permutation. In our model, we differentiate the cost of an inversion in the permutation by using the gap value between the two elements. We propose model parameters estimators and we illustrate our estimation procedure on simulated data and on a real application.