Qualitative preferences specified in CP-theory define a partial order over outcomes based on induced dominance relations. We investigate the problem of measuring divergence between such induced orders. We introduce two formally equivalent distance metrics, each leveraging a distinct computational strategy based on different types of normalization. Preliminary empirical results support the soundness of the proposed methodologies and offer comparative insights into their computational properties. To the best of our knowledge, this is the first study to compute distance measures between qualitative preferences that jointly accounts for conditional dependencies and relative importance statements.